Search Results (1,843)

Background: Thyrotoxicosis is a systemic condition with well-documented cardiovascular effects, but its role as a precipitant of acute coronary syndromes (ACS) is often overlooked. This review summarizes clinical cases and original studies from the last 20 years, describing ACS triggered by thyrotoxicosis. Methods: Following PRISMA 2020 guidelines, we searched PubMed, Scopus, and Embase for reports published between 2004–2025. Only case reports and original articles were included. Data extracted included demographics, ECG findings, angiography results, thyroid function, etiology of hyperthyroidism, and outcomes. Results: A total of 35 cases were identified. The mean age was in the fourth decade of life, with a female predominance (57%, 20 out of 35). More than half of the patients presented with ST-segment elevation myocardial infarction (STEMI) or STEMI equivalents (21 out of 35; 60%). Electrocardiographic abnormalities most often involved anterior or inferior leads. Coronary angiography revealed normal vessels or diffuse vasospasm in 18 cases (51%), while thrombotic occlusion was observed in 4 cases (11%), spontaneous dissection in 2 cases (6%), and myocardial bridging in 3 cases (9%). The leading cause of thyrotoxicosis was Graves’ disease (≈65%), followed by painless thyroiditis, iatrogenic causes, and gestational hyperthyroidism. Thyroid storm was reported in approximately 20% of cases and was associated with malignant ventricular arrhythmias or sudden cardiac death. Conclusions: Thyrotoxicosis should be recognized as a rare but important trigger of ACS, especially in young patients without traditional risk factors. Pathophysiological mechanisms include coronary vasospasm, increased myocardial oxygen demand, and hypercoagulability. Early recognition may prevent unnecessary revascularization and optimize outcomes through integrated endocrine and cardiac management. Full article

Under the influence of global climate change, extreme weather events have become more frequent, and earth and rockfill dams often encounter unconventional working conditions such as sudden changes in reservoir water levels during operation. These abrupt changes are characterized by their strong suddenness and rapid rate of change, which can be challenging for traditional numerical analysis methods due to slow modeling and time-consuming calculations, presenting certain limitations. Therefore, an approach has been developed that integrates seepage monitoring data into the failure probability analysis and early warning methods for earth and rockfill dams. Based on the model’s prediction results, dynamic safety warning indicators for the effect of single measurement points on earth and rockfill dams under sudden reservoir water level changes have been quantitatively designed. A risk probability function reflecting the relationship between the residuals of seepage monitoring effects and the risk rate has been constructed to calculate the risk rate of single measurement points for dam seepage effects. By employing the Copula function, which considers the differences and correlations in monitoring effect amounts across different parts of the dam, the single-point seepage risk rates are elevated to a multi-point seepage risk rate analysis. This enables the quantification of the overall seepage risk rate of dams under sudden reservoir water level changes. Case study results show that the safety model has high prediction accuracy. The joint risk rate of the dam based on the Copula function can simultaneously consider spatial correlations and individual differences among multiple measurement points, effectively reducing the interference of randomness in the calculation of single-point risk rates. This method successfully achieves the dynamic transformation of actual seepage effect measurements into risk rates, providing a theoretical basis and technical support for the operational management and safety monitoring of earth and rockfill dams during emergency events. Full article

Cameras have become indispensable sensors in intelligent vehicles, with their deployment steadily increasing across modern automobiles. It is critical for camera modules to have reliable and accurate environmental perception, but a major challenge is condensation inside the modules that severely compromises imaging quality. To address this issue, we performed comprehensive thermodynamics-based simulations to clarify condensation mechanisms and evaluate their impact on optical imaging performance. Based on these insights, we proposed an integrated optical–structural optimization strategy that reduces the internal cavity volume adjacent to the first lens, simultaneously increasing the first lens thickness and the curvature of its internal surface. This strategy both reduces water vapor volume and elevates the temperature of potential condensation zones. The optimized module exhibits markedly improved resistance to condensation compared with the baseline design in the experiment, raising the critical condensation threshold from a sudden temperature drop of 42 °C to over 60 °C. This approach effectively mitigates condensation under harsh environmental conditions without additional cost. Our simple yet effective design is broadly applicable to diverse automotive camera module architectures, thereby enhancing system reliability and improving the overall safety of autonomous driving. Full article

Background: Mitral valve prolapse (MVP) is the most common valvular abnormality in the general population and has been linked to mitral regurgitation, arrhythmias, and sudden cardiac death. Its prevalence and prognostic significance in athletes remain uncertain, raising important questions for pre-participation screening, eligibility for competition, and long-term follow-up. Methods: We systematically searched PubMed, Scopus, and EMBASE databases from inception through August 2025 for original studies reporting MVP prevalence in athletes, diagnosed by echocardiography or pathological assessment. Data on study characteristics, diagnostic definitions, prevalence, arrhythmias, and outcomes were independently extracted by three reviewers. Methodological quality was appraised using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: Twelve studies published between 1987 and 2024 met inclusion criteria, enrolling 19,463 athletes from diverse sports and competitive levels. A total of 407 MVP cases were identified, corresponding to a crude pooled prevalence of 2.4%. Prevalence estimates varied substantially (0.2–20%), reflecting heterogeneity in study populations and diagnostic definitions. When all studies were pooled using a random-effects model, the overall prevalence was 2.0% (95% CI 1.2–2.8%). A sensitivity analysis restricted to contemporary, unselected athletic cohorts yielded a prevalence of 1.1% (95% CI 0.4–1.9%), closely aligning with population-based estimates. Ventricular arrhythmias were more frequent than supraventricular arrhythmias, particularly in association with bileaflet prolapse, leaflet thickening, or significant mitral regurgitation. Most athletes were asymptomatic, and only one prospective study provided long-term follow-up, confirming a generally benign prognosis, though rare adverse events (atrial fibrillation, valve surgery) were documented. Conclusions: MVP is relatively uncommon in athletes and occurs at rates similar to the general population. In most cases, prognosis is favorable and should not preclude sports participation. Nonetheless, recognition of high-risk phenotypes with arrhythmogenic potential highlights the need for individualized evaluation and tailored surveillance strategies in sports cardiology practice. Full article

Forecasting real-world time series in domains with strong event sensitivity and regional variability poses unique challenges, as predictive models must account for sudden disruptions, heterogeneous contextual factors, and structural differences across locations. In tackling these challenges, we draw on the concept of symmetry that refers to the balance and invariance patterns across temporal, multimodal, and structural dimensions, which help reveal consistent relationships and recurring patterns within complex systems. This study is based on two multimodal datasets covering 12 tourist regions and more than 3 years of records, ensuring robustness and practical relevance of the results. In many applications, such as monitoring economic indicators, assessing operational performance, or predicting demand patterns, short-term fluctuations are often triggered by discrete events, policy changes, or external incidents, which conventional statistical and deep learning approaches struggle to model effectively. To address these limitations, we propose an event-aware multimodal time-series forecasting framework with graph-based regional transfer built upon an enhanced PatchTST backbone. The framework unifies multimodal feature extraction, event-sensitive temporal reasoning, and graph-based structural adaptation. Unlike Informer, Autoformer, FEDformer, or PatchTST, our model explicitly addresses naive multimodal fusion, event-agnostic modeling, and weak cross-regional transfer by introducing an event-aware Multimodal Encoder, a Temporal Event Reasoner, and a Multiscale Graph Module. Experiments on diverse multi-region multimodal datasets demonstrate that our method achieves substantial improvements over eight state-of-the-art baselines in forecasting accuracy, event response modeling, and transfer efficiency. Specifically, our model achieves a 15.06% improvement in the event recovery index, a 15.1% reduction in MAE, and a 19.7% decrease in event response error compared to PatchTST, highlighting its empirical impact on tourism event economics forecasting. Full article

Postmortem diagnosis of sudden cardiac death (SCD) may escape detection due to the absence of thrombi and slow development of structural and immunohistochemical changes. Therefore, this study explores the possibility of analyzing relevant clinical chemistry biomarkers in myocardial homogenates and serum. Following an initial pilot study, myocardial samples from 113 autopsy cases were homogenized with distilled water, T-PER or 2 M urea. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatine kinase (CK-MB), lactate dehydrogenase (LDH), orosomucoid and total protein were analyzed with an IndikoPlus and a subset was also analyzed with a Roche Cobas 8000 c701 analyzer, which also provided results for cardiac Troponin T, myoglobin and NT-proBNP. Although the yields varied with different extraction buffers depending on the analyte, distilled water was often as effective as T-PER and 2 M urea extraction for most analytes. Biomarker levels were consistently higher in the myocardial homogenates than in serum. Proteomic profiling on a subset confirmed higher concentrations of the cardiac markers in the tissue samples than in serum. Finally, we investigated whether selected markers could support the diagnosis of acute cardiac disease by classifying cases as sudden cardiac death (SCD) or controls. There was no significant difference in serum concentrations of the selected biomarkers between SCD cases and controls, whereas a significant loss of several markers was observed in SCD myocardial samples as compared to controls. Hence, our results suggest that analysis of tissue homogenates is likely better for detecting early ischemia, and we show that an in-house benchtop multi-analyzer can provide rapid results to assist the pathologist’s decision-making during autopsy. Full article

Cryogenic industries handling liquid hydrogen and helium require rigorous safety verification. However, current standards (ASTM, ASME, ISO) are optimized for LNG at −163 °C and remain inadequate for extreme cryogenic conditions such as −253 °C. As the temperature decreases, materials experience ductile-to-brittle transition, raising the risk of sudden fracture in testing equipment. This study presents a fuzzy-integrated reliability framework that combines fault tree analysis (FTA) and Failure Modes, Effects, and Criticality Analysis (FMECA). The method converts qualitative expert judgments into quantitative risk indices for use in data-scarce conditions. When applied to a cryogenic impact testing apparatus, the framework produced a total failure probability of 1.52 × 10⁻³, about 7.5% lower than the deterministic FTA result (1.64 × 10⁻³). These results confirm the framework’s robustness and its potential use in cryogenic testing and hydrogen systems. Full article

Background/Objectives: Sudden unexpected postnatal collapse (SUPC) is a rare but catastrophic event affecting apparently healthy neonates during the first days of life. Therapeutic hypothermia has been increasingly applied in this setting due to pathophysiological overlap with hypoxic–ischemic encephalopathy, but its effectiveness remains uncertain. The aim of this review is to systematically identify, appraise, and synthesize the evidence on therapeutic hypothermia for SUPC. Methods: We searched MEDLINE, Scopus, Embase, Web of Science, and Cochrane up to February 2025. Eligible studies included term or near-term infants with SUPC within seven days of life who underwent therapeutic hypothermia. Data were extracted on demographics, collapse circumstances, therapeutic hypothermia protocol, mortality, seizures, neuroimaging, and neurodevelopment. Results: Thirteen studies were included, encompassing 70 infants. Most events occurred within two hours of life, during skin-to-skin or breastfeeding, and were strongly associated with primiparity. Therapeutic hypothermia was typically initiated within six hours of collapse, using whole-body cooling at 33–34 °C for 72 h. Mortality was approximately 10% (widely ranging from 0 to 50%). Seizures were frequent (70–90%), and MRI abnormalities were reported in about half of cases. Approximately half of survivors demonstrated normal neurodevelopment at one year. Study quality was low to moderate, and risk of bias substantial. Conclusions: Therapeutic hypothermia is feasible in SUPC and survival with favorable outcomes has been documented, but the certainty of evidence is very low. Given recurrent risk factors such as primiparity and early skin-to-skin/breastfeeding, enhanced vigilance and preventive strategies are essential. Therapeutic hypothermia should be considered case by case, ideally within specialized centers and supported by registries. Full article

Background/Objectives: Current guidelines recommend 24–48 h Holter for risk stratification and atrial fibrillation (AF) screening in hypertrophic cardiomyopathy (HCM). However, the limited duration of this approach may not provide optimal sensitivity. In addition, extended ECG monitoring has been demonstrated to be more effective in detecting arrhythmias in other clinical entities. We aimed to assess the utility of extended ECG monitoring for 30 days in a non-high-risk cohort of HCM patients. Methods: We conducted a prospective multicentre study with 113 non-high-risk HCM patients who underwent 30-day ECG monitoring with a dedicated device. We compared the detection of relevant arrhythmias (AF, atrial flutter, and non-sustained ventricular tachycardia) during 30-day monitoring with the findings observed during the first 24 h. Results: Extended ECG monitoring detected relevant arrhythmias in 63.7% of patients, compared with 12.4% during the first 24 h (p < 0.001). This difference was mainly driven by non-sustained ventricular tachycardia (NSVT) (61.1% vs. 8.9%, p < 0.001). Atrial fibrillation episodes were detected in 10.6% of patients after completing prolonged monitoring vs. 6.2% during the first 24 h (p = 0.066). Extended monitoring resulted in a reclassification of 21.2% of patients to a higher sudden cardiac death (SCD) risk category using the HCM-SCD calculator. Conclusions: Extended ECG monitoring significantly enhances the detection of arrhythmias in HCM. Using this technique, NSVT were detected in most patients of a non-high-risk HCM cohort. Further investigation is warranted to determine the role of extended monitoring in SCD risk stratification and AF screening. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

Accurate forecasting of residential heating loads is crucial for guiding heating system control strategies and improving energy efficiency. In recent years, research on heating load forecasting has primarily focused on continuous district heating systems, and it often struggles to cope with the abrupt load fluctuations and irregular on/off schedules encountered in intermittent heating scenarios. To address these challenges, this study proposes a hybrid convolutional long short-term memory (ConvLSTM) model that replaces the conventional batch normalization layer with a Dynamic Tanh (DyT) activation function, enabling dynamic feature scaling and enhancing responsiveness to sudden load spikes. An improved channel–temporal attention mechanism, CBAM(T), is further incorporated to deeply capture the spatiotemporal relationships in multidimensional data and effectively handle the uncertainty of heating start–stop events. Using data from two heating seasons for households in a residential community in Dalian, China, we validate the performance of ConvLSTM-DyT-CBAM(T). The results show that the proposed model achieves the best predictive accuracy and strong generalization, confirming its effectiveness for intermittent heating load forecasting and highlighting its significance for guiding demand-responsive heating control strategies and for energy saving and emissions reduction. Full article

Objective: We aimed to explore the efficacy of repetitive transcranial magnetic stimulation (rTMS) for idiopathic sudden sensorineural hearing loss (ISSNHL) and evaluate the correlation between treatment courses of rTMS and hearing outcomes. Methods: A prospective observational study was conducted at West China Fourth Hospital, Sichuan University, from January 2018 to January 2025. The study enrolled 339 patients (342 affected ears) diagnosed with ISSNHL. Among them, 67 patients (group A) received standard therapy combined with rTMS, while the control group (group B) received conventional therapy only. To verify the correlation between different treatment courses of rTMS and hearing outcomes, patients in Group A were divided into Group A1 (treatment courses ≤ 10) and Group A2 (treatment courses > 10). Hearing thresholds and clinical characteristics were evaluated at admission, discharge day and 6 months post-treatment. The SDRG’s criteria were used for the grading of hearing recovery. Results: Tinnitus (79.89% vs. 75.32%, p = 0.361) and sleep disorders (33.70% vs. 41.14%, p = 0.178) were highly prevalent among patients in group A and group B. 1 Hz rTMS significantly improved these symptoms (PSQI: 52.32% vs. 44.44%, p = 0.032; THI: 16.67 ± 19.41 vs. 8.22 ± 12.77, p = 0.002). Compared to high-tone hearing loss patients, those with low-tone loss in groupA2 showed a more rapid improvement (250 Hz: 17.66 ± 16.59 vs. 14.09 ± 15.58, p = 0.041; 500 Hz: 21.20 ± 18.03 vs. 17.31 ± 16.24, p = 0.036) than grouA1, with benefits sustained at 6-month follow-up (250 Hz: 27.79 ± 18.74 vs. 22.71 ± 18.31, p = 0.012; 500 Hz: 31.89 ± 19.73 vs. 26.49 ± 20.08, p = 0.013). Conclusions: rTMS at 1 Hz, administered in courses > 10 sessions, demonstrated both short-term and long-term beneficial effects in the ISSNHL. Those with low-tone hearing loss exhibit better recovery but a higher chance of relapse than high-tone loss patients. As a non-invasive approach with minimal side effects, rTMS is suitable for routine ISSNHL treatment. Full article

Accurate prediction of structural displacements in hydropower stations is essential for the safety and long-term stability of large-scale water-related infrastructure. To address this challenge, this study proposes an AI-assisted monitoring framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Gated Recurrent Units (GRUs) for temporal sequence modeling. The framework leverages long-sequence prototype monitoring data, including reservoir level, temperature, and displacement, to capture complex spatiotemporal interactions between environmental conditions and dam behavior. A parameter optimization strategy is further incorporated to refine the model’s architecture and hyperparameters. Experimental evaluations on real-world hydropower station datasets demonstrate that the proposed CNN–GRU model outperforms conventional statistical and machine learning methods, achieving an average determination coefficient of R² = 0.9582 with substantially reduced prediction errors (RMSE = 4.1121, MAE = 3.1786, MAPE = 3.1061). Both qualitative and quantitative analyses confirm that CNN–GRU not only provides stable predictions across multiple monitoring points but also effectively captures sudden deformation fluctuations. These results underscore the potential of the proposed AI-assisted framework as a robust and reliable tool for intelligent monitoring, safety assessment, and early warning in large-scale hydropower facilities. Full article

Big L days are days when the lunar semidiurnal variation M₂ in the ionosphere is strongly enhanced by a factor of 2 or more. The worldwide network of ground-based receivers for the Global Navigation Satellite System (GNSS) has monitored the ionospheric total electron content (TEC) since 1998. The derived world maps of TEC are provided by the International GNSS Service (IGS) and allow the study of the characteristics of big L days in TEC. In the data analysis, the signal of the lunar semidiurnal variation M₂ in TEC is separated from the solar semidiurnal variation S₂ by means of windowing in the spectral domain. The time series of the M₂ amplitude often shows enhancements of M₂ (big L days) a few days after sudden stratospheric warmings (SSWs). The M₂ amplitude can reach values of 8 TECU. The M₂ composite of all SSWs from 1998 to 2024 shows that the M₂ amplitude is enhanced after the central date of the SSW. Regions in Southern China and South America show stronger effects of big L days. Generally, the effects of big L days on TEC show latitudinal and longitudinal dependencies. Full article

Sudden cardiac death (SCD) is a leading cause of mortality worldwide, with arrhythmia serving as a major precursor. Early and accurate prediction of SCD using non-invasive electrocardiogram (ECG) signals remains a critical clinical challenge, particularly due to the inherent asymmetric and non-stationary characteristics of ECG signals, which complicate feature extraction and model generalization. In this study, we propose a novel SCD prediction framework based on hierarchical feature fusion, designed to capture both non-stationary and asymmetrical patterns in ECG data across six distinct time intervals preceding the onset of ventricular fibrillation (VF). First, linear features are extracted from ECG signals using waveform detection methods; nonlinear features are derived from RR interval sequences via second-order detrended fluctuation analysis (DFA2); and multi-scale deep learning features are captured using a Temporal Convolutional Network-based sequence-to-vector (TCN-Seq2vec) model. These multi-scale deep learning features, along with linear and nonlinear features, are then hierarchically fused. Finally, two fully connected layers are employed as a classifier to estimate the probability of SCD occurrence. The proposed method is evaluated under an inter-patient paradigm using the Sudden Cardiac Death Holter (SCDH) Database and the Normal Sinus Rhythm (NSR) Database. This method achieves average prediction accuracies of 97.48% and 98.8% for the 60 and 30 min periods preceding SCD, respectively. The findings suggest that integrating traditional and deep learning features effectively enhances the discriminability of abnormal samples, thereby improving SCD prediction accuracy. Ablation studies confirm that multi-feature fusion significantly improves performance compared to single-modality models, and validation on the Creighton University Ventricular Tachyarrhythmia Database (CUDB) demonstrates strong generalization capability. This approach offers a reliable, long-horizon early warning tool for clinical SCD risk assessment. Full article