Search Results (666)

This paper examines the predictive power of credit default swap (CDS) spread correlations in the context of the 2023 Credit Suisse failure. Using a two-window design, we separate a 50-week pre-crisis period from the final two-week “jump” window and evaluate whether historical market-implied dependence anticipated cross-sectional crisis repricing across Global Systemically Important Banks (G-SIBs). We find that the magnitude of each bank’s crisis-period CDS jump is significantly related to its prior co-movement with Credit Suisse across the full sample ($r=0.80$, $p<0.001$, $n=15$), indicating that contagion followed a structured dependence pattern rather than an undifferentiated panic dynamic. The relationship holds across both regional cohorts, with the European G-SIB group displaying a considerably tighter fit ($r=0.96$, $p<0.001$, $n=8$) than the non-European group ($r=0.84$, $p=0.019$, $n=7$), consistent with geographic and institutional proximity to Credit Suisse amplifying the contagion channel. Additional empirical outputs, including stepwise-regression diagnostics and placebo/event-time checks, support the interpretation that the estimated relationship contains an economically meaningful signal while remaining partly event-driven in short horizons. Overall, the evidence suggests that rolling CDS dependence regimes may serve as a useful leading indicator for identifying institutions most likely to face disproportionate repricing pressure during a localized systemic shock. These findings are drawn from a single crisis episode and 15 peer institutions; they should be read as preliminary evidence of a potentially useful mechanism rather than as the basis for an operational early-warning system, and replication across additional crises and institutional settings is required before broader generalization. Full article

This paper proposes a resilience assessment model for irregular intersections that combines conflict analysis, channelization conditions, and traffic operational data to identify turning movements with high cascading-failure potential. Turning movements are the basic analytical unit. A channelization-weighted conflict matrix captures coupling among traffic flows by incorporating signal phase separation and lane function allocation. A CLI (Conflict Load Index) integrates the weighted conflict degree, a saturation correction factor, and expert risk scores to rank turning movements by cascading-failure risk. Three failure scenarios are compared: random failure, descending conflict-degree failure, and descending CLI failure. A failure propagation probability function based on weighted conflict-degree load distribution is paired with a resilience loss index that quantifies cumulative intersection capacity loss during failure propagation. The model is applied to the irregular intersection near Donggang Passenger Station. Among 16 turning movements, R2-T2, R4-T2, R5-T2, and R1-T1 have the four highest CLI values and form the high-risk set. The high-saturation movement R5-T2 (saturation 0.738) rises to rank 3 in the revised model, three positions higher than in the topology-only model, reflecting its risk level under actual traffic conditions. Sensitivity analysis shows that the relative ranking of the three failure modes is consistent across all parameter combinations, confirming the robustness of the conclusions. The model provides a quantitative basis for resilience diagnosis, risk early warning, and improvement planning at irregular intersections. Full article

The bigeye cigarfish (Cubiceps pauciradiatus) is a common bycatch species in Indian Ocean fisheries, yet its stock status remains largely unevaluated despite its potential as a future “backup” fishery resource. Here, we develop an integrated assessment framework that couples a Bootstrap based growth estimation tool (fishboot) with the Length based Bayesian biomass (LBB) model. Using length frequency data collected from 2023 to 2025, fishboot quantified uncertainty in asymptotic length (L_∞) and growth rate (K), yielding estimates of L_∞ = 175 mm and K = 0.92 year⁻¹ that reflect a fast-growing, medium-bodied life history. This L_∞ was then supplied as an informed prior to the LBB model. Results indicate that the stock maintained a relatively stable resource status throughout the study period, with its baseline biomass remaining healthy (B/B_msy > 1) and no alarming early warning signals detected (F/M < 1). The modest biomass fluctuation observed is likely attributable to the species’ status as a common bycatch rather than to systematic overfishing pressure. The fishboot–LBB framework provides a robust approach for proactively assessing data limited and bycatch stocks, thereby extending fisheries research beyond traditional target species to support ecosystem-based management in the region. Full article

Power IoT acts as the final frontier of the modern grid, where the reliability of energy supply hinges on precise monitoring. However, current systems often suffer from delayed responses, poor feature separation, and a computational wall when dealing with high-frequency data on the edge. We move past the traditional reactive detection mindset and propose STF-MKD, a framework built on spatio-temporal-frequency graph decoupling and Mamba-WKAN knowledge distillation. Our goal is to shift the operational focus from responding to failures to forecasting them. The first part of the system is the STF-Extractor. It uses dynamic graph attention to map the connections between nodes and a masking game to pull structural features out of the background noise. Following this, we address the wild nonlinear nature of equipment failure with the Mamba-WKAN backbone. By embedding Mexican Hat wavelets and B-splines into the Mamba architecture, the model maintains efficiency while splitting the work: splines track the daily cycles and wavelets lock onto sudden transients. To prevent the model from smoothing away rare anomaly signals, we introduce the TGAR (Teacher-Guided Anomaly-focused Reconstruction) distillation scheme. This one-teacher-two-students setup uses a teacher model with a global view to guide the student predictor. In doing so, the system triggers early warnings based on faint structural shifts before a fault fully develops. Tests on six major datasets, including ETTh/m and WADI, show that STF-MKD outperforms mainstream methods. Full article

Background: Tisagenlecleucel (tis-cel) is the sole chimeric antigen receptor T-cell (CAR-T) therapy approved for pediatric/adolescent relapsed/refractory B-cell acute lymphoblastic leukemia (r/r B-ALL). Infection-related adverse events (IRAEs) represent a leading cause of non-relapse mortality, yet dedicated analysis in patients <18 years remains limited. Objective: This study aimed to analyze the characteristics of IRAEs in pediatric and adolescent patients with r/r B-ALL treated with tis-cel, based on the FDA Adverse Event Reporting System (FAERS) database. The analysis included the reporting proportion, temporal distribution, pathogen spectrum, and overlapping features with other adverse events, generate hypotheses for infection prevention and control in patients aged <18 years. Research Design and Methods: We analyzed FAERS data (August 2017–March 2025) and included 492 cases of <18-year-old r/r B-ALL patients treated with tis-cel. Disproportionality analyses (reporting odds ratio, ROR; information component, IC), time-to-onset analysis, and co-occurrence assessments were performed on 149 infection-related reports. Results: Infection-related AEs occurred in 30.28% of cases, with significantly higher mortality in infected versus non-infected patients (40.27% vs. 12.83%, p < 0.001). Most infections (93.86%) occurred within one month (median time-to-onset = 4 days), peaking within 15 days (77.50%); 2.65% occurred after one year. Significant disproportionate reporting signals were observed for Clostridioides difficile (ROR₀₂₅ = 5.45), influenza virus (ROR₀₂₅ = 7.16), and adenovirus (ROR₀₂₅ = 3.51). Hypogammaglobulinemia (52.94%) and hypoxia (54.90%) exhibited higher co-occurrence with infections than CAR-T-specific toxicities (23.08–35.41%). Conclusions: Infection-related AEs following tis-cel treatment are frequent and associated with significantly increased mortality in pediatric and adolescent r/r B-ALL patients. While most occur early, late infections warrant long-term vigilance. Disproportionality analyses identified signals suggestive of potential high-risk pathogens such as Clostridioides difficile,, influenza, and adenovirus, and hypogammaglobulinemia/hypoxia may serve as early warning indicators. These hypothesis-generating findings require validation in prospective studies. Full article

Can the distress of a cryptocurrency be predicted from its market behavior, and is that predictability reliable when it matters most? Using daily data for 609 USD-quoted coins traded on Kraken between 2013 and 2025, we built a survivorship-inclusive coin-quarter panel and model the onset of severe, sustained price distress—a deep, non-recovering drawdown relative to a trailing peak. A panel logit confirmed that realized volatility, illiquidity, weak momentum, and asset youth predict distress, with a coin-stratified cross-validated out-of-sample AUC of about 0.68. Our central contribution was to show that this predictability is regime-dependent. Interactions between coin-level signals and contemporaneous market-wide volatility are jointly significant (likelihood-ratio p < 0.001), and a rolling-origin evaluation reveals prospective accuracy swinging from no better than chance (AUC 0.43) to strong (0.79) across years. This regime-dependence is robust across alternative distress thresholds, regime proxies, data frequencies, cluster-bootstrap inference, and replication on a second exchange (Binance), though the individual signal channels are not. Testing the most natural mechanism—rising cross-asset co-movement in turbulent markets—we find no support. Microstructure-based early-warning signals for crypto distress are thus conditionally reliable: informative in calm markets but unreliable in the turbulent conditions where warning is most valuable. Full article

Furnace tube rupture is a critical safety and reliability issue in high-temperature industrial systems, often leading to unplanned shutdowns, severe economic losses, and safety incidents. Conventional monitoring approaches based on threshold alarms and single-variable diagnostics are frequently inadequate for detecting early degradation under complex multivariable operating conditions. This study proposes a multiclass predictive maintenance framework using a decision forest algorithm to predict furnace tube rupture severity from industrial operational data. The dataset comprised 10,957 observations and 56 furnace operating parameters collected from industrial historian systems. Following preprocessing and Pearson correlation-based feature selection, 19 significant parameters were retained for model development. The proposed framework integrates data preprocessing, feature selection, hyperparameter optimization, and validation using unseen operational data from 2021. The results demonstrate that the model effectively captures rupture-risk trends and provides early warning signals more than 14 days before rupture events, with high-risk classifications exceeding 70% predictive probability. Unlike conventional binary classification methods, the proposed multiclass framework enables risk-based maintenance decision-making, including targeted inspections, load reduction, and scheduled shutdown planning. The findings, based on validation against a single industrial furnace system, highlight the effectiveness of ensemble machine learning techniques in improving predictive maintenance, operational safety, and reliability engineering for this class of industrial furnace; broader generalization across furnace configurations and sites remains to be confirmed through multi-installation validation. Full article

Iced conductor galloping poses a critical threat to high-voltage transmission line safety and stability, yet existing monitoring systems are constrained by single-sensor dependency, inadequate signal denoising, and limited early warning accuracy. This paper presents a field-validated framework integrating multi-source sensor fusion, adaptive signal denoising, and deep learning-based early warning for iced conductor galloping. A five-layer Internet of Things (IoT) monitoring architecture is designed, fusing fiber Bragg grating (FBG) arrays, MEMS inertial sensors, and micro-meteorological stations, with dual-spectrum cameras providing auxiliary visual verification. Self-powered MEMS nodes utilizing electromagnetic induction energy harvesting are shown to have achieved year-round zero-external-power maintenance. An improved hummingbird local optimization algorithm (IHLOA) adaptively optimizes variational mode decomposition (VMD) parameters, combined with wavelet threshold denoising (WTD) for joint signal preprocessing, achieving a 17.78 dB signal-to-noise ratio improvement [95% CI: 17.2–18.3 dB]. A 26-dimensional multi-domain feature vector is constructed and reduced to 12 discriminative features via ReliefF selection. A Temporal Adaptation Gated Recurrent Unit with Attention (TA-GRU-Attention) model incorporating an adaptive irregular time series perception module is developed for galloping early warning, with all models evaluated exclusively on real-event test samples. Experimental validation through 1:20 scale wind tunnel aeroelastic tests and a 15-month field demonstration on an operating 220 kV transmission line at 2800–3200 m elevation demonstrates a galloping prediction accuracy of 91.3% [95% CI: 81.5–97.2%] under stratified time series split, a missed alarm rate of 12.2%, and a median advance warning time of 38.5 min (range: 18–65 min, IQR: 26–52 min), with 97.3% system availability over the deployment period. Full article

To delineate the serum proteomic profile of persistent subclinical hypocalcemia (pSCH) in periparturient dairy cows, elucidate its molecular pathogenesis, and provide a theoretical basis for early warning and precision prevention and control, we selected 12 Holstein dairy cows of similar age (2.92 ± 0.12 years), parity (1.56 ± 0.21), body condition score (BCS) (2.84 ± 0.04), milk yield (26.81 ± 0.22 kg/d), and day in milk (DIM) (6.60 ± 0.24 d) and no significant between-group differences as experimental animals. Based on serum calcium concentrations on postpartum days 1 to 4 and clinical presentation, the cows were divided into a healthy control group (serum calcium > 1.77 mmol/L on day 1 and >2.20 mmol/L on day 4 postpartum, n = 6) and a persistent subclinical hypocalcemia group (serum calcium ≤ 1.77 mmol/L on day 1 and ≤2.20 mmol/L on day 4 postpartum, n = 6). Serum samples were collected on postpartum days 1, 2, and 4 and analyzed using 4D-DIA quantitative proteomics. A total of 178 significantly differentially expressed proteins were identified (fold change > 1.2, p < 0.05), including 59 up-regulated and 119 down-regulated proteins. These differentially expressed proteins were mainly enriched in pathways involving endocrine and other factor-regulated calcium reabsorption, regulation of actin cytoskeleton, the tricarboxylic acid cycle (TCA cycle), and lipoic acid metabolism. The results indicate that the pathological state of pSCH is closely associated with abnormalities in calcium reabsorption regulation, actin cytoskeleton maintenance, and mitochondrial energy metabolism-related signaling pathways. The key proteins and pathways identified in this study provide a theoretical foundation for future in-depth research on the pathogenesis, prevention, and treatment of pSCH. Full article

Mudslides are sudden and highly destructive; their source areas typically undergo slow, millimeter-scale creep over a period of months or even years before destabilization. If these precursor signals can be detected, valuable time can be gained for disaster prevention and mitigation. However, in the weathered crust and residual deposits of potential debris flow source areas, the long-term coupled action of freeze–thaw cycles and rainfall causes continuous reorganization of internal particle contact force chains, generating weak, metastable creep signals. The high-order nonlinearity and spatial heterogeneity of the interference phase gradient in low-coherence regions lead to pixel-spanning jumps in the unwrapped phase that are blurred by integer multiples of π. The high rate of phase jumps between adjacent pixels severely hampers the early detection of weak deformation. To address this, we propose a method for the early detection of weak deformation in potential debris flow source areas based on phase-unwrapping convolutional neural networks and long-time-series InSAR technology. First, we use long-time-series InSAR technology to construct a spatiotemporal map of interferogram sequences and establish feature propagation paths between high- and low-coherence interferogram pairs using the coherence coefficient as an edge weight. Second, we design a phase-unwrapping graph convolutional network that aggregates phase gradient information from neighboring nodes through two graph convolutional layers to correct the unwrapping results of low-coherence interferogram pairs and suppress cross-pixel jumps caused by π-integer-multiple blurring. Finally, by combining a dual-criterion classification approach based on temporal attention scores and deformation acceleration, the method captures the complete evolutionary process from stable creep to accelerated deformation. Experimental results show that the maximum phase jump rate of this method is approximately 0.02, effectively resolving the phase jump issue caused by high-order nonlinear gradients; in some areas of the study region, where deformation ranges from −1 mm to −9 mm, the inversion error is consistently controlled within ±1 mm. A total of five potential debris flow source areas were identified, classified by creep stage as follows: one in the accelerated deformation stage, two in the stable creep stage, and two in the early creep stage. No significant surface failure occurred in any of these source areas. This method provides reliable technical support and a decision-making basis for refined early warning, disaster prevention, and mitigation of debris flow hazards and holds significant engineering application value. Full article

Cirrhosis, accounting for 2.4% of global mortality in 2019, represents a leading cause of death in chronic liver disease. Hepatic encephalopathy (HE), a decompensated complication of cirrhosis, is associated with a median survival of only 0.92 years post-diagnosis. Current screening methods relying on neuropsychological tests (e.g., Psychometric Hepatic Encephalopathy Score, PHES) have limitations such as time-consuming procedures and subjective interpretation, potentially delaying diagnosis. To address this, we integrated four cirrhotic transcriptomic cohorts (GSE41919, GSE57193, GSE139602, and GSE15654) and employed an integrated algorithm (LASSO [Least Absolute Shrinkage and Selection Operator]–RFE [Recursive Feature Elimination]–random forest) to identify HE-specific biomarker genes. Ultimately, we developed an HE risk-prediction system centered on eight HE-specific marker genes, namely, PRB2, TUBA1C, NPC2, LRRC32, TLN1, SOX9, SERPINA3 and RNASE4. Based on these genes, an XGBoost (eXtreme Gradient Boosting)-based HE risk stratification model was constructed, and SHAP (SHapley Additive exPlanations) analysis was further introduced to address the “black-box” limitation of conventional machine learning models and to improve the interpretability. The finalized eight-gene system enables accurate, efficient, and interpretable HE risk assessment in patients with cirrhosis. Functional characterization through gene set enrichment analysis and structural equation modeling further revealed that these marker genes converge on four interconnected biological processes, namely, metabolic homeostasis, synaptic and neural transmission, immune inflammatory signaling, and hepatic detoxification, which collectively reflect the gut–liver–brain axis disruption central to HE pathogenesis. This dual-model system, incorporating both cirrhosis progression and survival prognosis, provides a reliable and clinically applicable tool for early HE risk warning and stratification, reducing the limitations of traditional neuropsychological screening and offering a translational foundation for timely intervention and prognostic optimization in high-risk cirrhotic patients. Full article

High-turbidity exceedance events are important indicators of abnormal coastal water quality conditions, but their relatively low frequency compared with normal observations makes reliable classification challenging. This study developed a scenario-based machine learning framework to classify high-turbidity exceedance using water quality and meteorological predictors from an automated seawater monitoring station along the Arabian Gulf. After quality control screening and temporal matching with NASA POWER meteorological variables, turbidity was converted into a binary target using a 2 NTU threshold, resulting in 4408 low-turbidity observations (85.3%) and 758 high-turbidity observations (14.7%). Four predictor scenarios were evaluated: meteorological-only selected variables, combined selected variables, water-quality-only all variables, and combined all variables. Random Forest, XGBoost, a Support Vector Machine, and K-Nearest Neighbors were tested under three class-imbalance treatments: undersampling, oversampling, and SMOTE. The meteorological-only scenario showed moderate classification ability, with ROC-AUC values of 0.81–0.86, but relatively low precision, indicating limited control of false alarms. Adding water quality predictors substantially improved classification performance, with the combined selected-variable scenario achieving ROC-AUC values of 0.96–0.98. Under oversampling, Random Forest achieved an accuracy = 0.95, precision = 0.83, specificity = 0.97, weighted F1-score = 0.95, and ROC-AUC = 0.98. The water-quality-only scenario also performed strongly, with accuracy values of 0.90–0.95 and ROC-AUC values of 0.95–0.96, confirming that direct water quality measurements carried most of the predictive signal. The combined all-variable scenario produced similarly high performance, with ROC-AUC values of 0.96–0.98, while variable selection reduced redundancy without loss of predictive skill. Computational analysis showed that the top-ranked XGBoost model under the combined all-variable oversampling scenario required only 0.08 s for training and 0.003 ms per sample for prediction. Overall, the results demonstrate that integrating selected water quality and meteorological predictors provides an accurate, efficient, and operationally practical framework for high-turbidity early-warning classification. Full article

Sudden rupture of freezing pipes frequently occurs in artificial ground freezing engineering. Owing to complex field environments, it is difficult to clarify the single-factor evolutionary laws of vibration signals induced by pipe fracture through field monitoring. Four groups of model freezing pipes with diameter-to-thickness ratios ranging from 20 to 26 were fabricated through geometric scaling based on engineering prototype pipes of Φ140 × (5–7) and Φ159 × (6–8). A low-temperature brine medium at −30 °C was circulated inside the pipes to simulate actual in situ refrigeration conditions. A series of tensile rupture tests were performed to explore the vibration response characteristics of freezing pipes with different specifications. The test results indicate that the ultimate rupture load is positively correlated with signal energy. Within the diameter-to-thickness ratio range of 20–26, the signal amplitude decreases approximately linearly (R² = 0.99), while the progress count and signal energy increase gradually, and the dominant frequency of rupture vibration signals decreases continuously. In other words, the dominant frequency gradually declines as the diameter-to-thickness ratio rises. Unlike conventional acoustic emission characteristics of bare steel fracture, which are typically characterized by high-amplitude, high-energy bursts with prominent central frequencies, the vibration signals of freezing pipes surrounded by frozen soil exhibit significantly lower dominant frequencies (3–30 kHz), rapid attenuation due to pipe–soil energy radiation, and a frequency shift that is structurally governed by the diameter-to-thickness ratio rather than by material properties alone. The quantitatively established frequency bands and parameter evolution patterns can serve as reference criteria for field monitoring and early warning of freezing pipe fracture in artificial ground freezing engineering. Full article

Financial markets are increasingly shaped by heterogeneous information sources, including trading behaviors, order-book dynamics, textual events, investor sentiment, and macroeconomic conditions. These signals are often asynchronous, noisy, partially missing, and associated with different degrees of reliability, which makes trustworthy financial risk early warning challenging. To address these issues, this study proposes an uncertainty-aware multimodal financial sensing network, termed UAMF-Net. The model treats price series, trading volume, order books, news texts, investor sentiment, and macroeconomic variables as financial sensing signals and integrates them through three task-oriented modules. First, the asynchronous multimodal temporal alignment module uses absolute time encoding, relative interval modeling, event-lag representation, temporal gating, and target-time-guided cross-scale attention to align cross-frequency financial signals according to their relevance to the prediction time. Second, the risk-aware multimodal soft fusion module estimates modality-level risk contribution and signal reliability by combining fuzzy risk membership, modality confidence weights, and cross-modal consistency constraints. Third, the uncertainty-aware risk early warning module adopts evidential learning to generate nonnegative class evidence, derive risk-category probabilities from Dirichlet parameters, estimate predictive uncertainty from total evidence strength, and jointly predict continuous risk intensity. Experimental results show that UAMF-Net achieves the best overall performance, with Accuracy, Precision, Recall, F1-score, Macro-F1, ROC-AUC, and PR-AUC reaching $0.882$, $0.864$, $0.849$, $0.856$, $0.839$, $0.941$, and $0.824$, respectively, while ECE and Brier score are reduced to $0.037$ and $0.096$. Under severe temporal asynchrony, UAMF-Net maintains an Accuracy of $0.849$, a Macro-F1 of $0.797$, and a PR-AUC of $0.774$. Under the missing multiple modalities setting, it achieves an Accuracy of $0.842$ and a Macro-F1 of $0.788$. The uncertainty analysis further shows that Risk Precision@90% reaches $0.889$. Validation on FNSPID, Daily News, and StockEmotions also confirms its generalization ability across public financial benchmarks. These results indicate that UAMF-Net improves financial risk early warning by jointly modeling temporal asynchrony, modality reliability, and predictive uncertainty. Full article

Tang dynasty beacon towers in Xinjiang’s circum-Tarim region constitute surviving architectural evidence of a frontier military communication system that supported regional governance and safeguarded the Silk Road. However, these beacon tower sites are generally small, severely weathered earthen structures whose military and heritage significance is difficult to interpret from individual remains alone. This study aims to reconstruct the military communication functions and spatial organization of Tang dynasty beacon tower architectural heritage in the circum-Tarim region. Specifically, this study seeks to quantify the recognizable distance of beacon-fire signals, identify visual connections among archaeological sites, and explain how visual signaling and road-based document transmission jointly maintained regional communication. First, key parameters were extracted from historical documents to calculate the maximum recognizable distance of beacon-fire signals. GIS intervisibility analysis was then conducted for 134 beacon towers and 54 associated city sites, and the results were integrated with military administrative jurisdictions, ancient transportation routes, city-site hierarchy, topography, and the “mapu” document-relay system. The analysis identified 262 effective intervisibility links among 139 sites. Among 101 adjacent site pairs without direct intervisibility, 62 routes may have maintained communication through road-based document transmission. The results indicate that the reconstructed system combined beacon-fire signaling with military document transmission and exhibited three principal spatial patterns: networked early warning in core oasis areas, linear transmission along the Silk Road, and point-based early warning near mountain passes. These arrangements linked the major military jurisdictions and directed information toward the Anxi Protectorate, the highest military and administrative center in the Western Regions. This study provides a viable GIS–historical approach for interpreting the functional and spatial organization of beacon tower architectural heritage, while offering a scientific basis for its integrated conservation and the interpretation of its military heritage values. Full article