Search Results (16,050)

The quantification of structural resilience and sub-percentile tail risk represents a major challenge across both corporate financial engineering and modern industrial logistics. Traditional aggregation architectures, such as linear risk matrices and parametric copulas, can exhibit computational and sensitivity challenges when modeling extreme tail-risk dependencies under sparse data regimes. While parametric copulas are highly effective under standard conditions, they can be sensitive to parameter specifications and sample size limitations in deep-tail regions. This paper highlights a numerical limitation of the Gumbel extreme-value copula in deep-tail regions (F ≥ 0.999). Analytical results indicate that the logarithmic structure of the tail generator produces progressively higher sensitivity near the distribution boundary, yielding an empirical condition number greater than 1220 at the regulatory 99.9% Value-at-Risk (VaR) threshold. This numerical conditioning issue increases sensitivity to sample noise and data scarcity, resulting in a 36.5% underestimation of systemic tail damage. The proposed model formalizes risk scenarios by mapping multi-node threats as normalized directional unit vectors within a compact 3D vector space. Interactions are then calculated algebraically using geometric invariants—the Dot Product for root-cause convergence and the Cross Product Norm for dynamic, second-order risk resonance—effectively contracting high-dimensional combinations into a stable framework. Rather than treating risks as frame-dependent scalar probabilities, this generalized High-Dimensional Geometric Invariant Operational Risk Framework extends legacy structures with domain-agnostic invariants capturing dynamic risk resonance and multi-trigger cascades. Simulation results across rugged operational environments, acute data scarcity (Ntrain = 100), and high-dimensional scaling (50 risk factors) demonstrate that the proposed model outperforms standard alternatives by a factor of approximately 13 in out-of-sample predictive accuracy (MSE = 0.08193) while maintaining absolute parametric stability. Furthermore, a Taylor-series tensor contraction successfully collapses 1275 second-order interactions into just 2 free parameters. This framework bypasses iterative Maximum Likelihood Estimation (MLE) bottlenecks, unlocking real-time, low-latency Monte Carlo stress testing for systemic banking compliance and global supply chain risk governance. Full article

Against the backdrop of the coordinated advancement of the dual carbon goals and new-type urbanization, the traditional high-carbon development model has become a major constraint on urban green transformation. As a critical vehicle for digital technologies to empower low-carbon governance, smart cities have yet to receive a systematic evaluation of their long-term policy effects based on quasi-natural experiments. Using panel data from 280 prefecture-level cities from 2003 to 2023, this study takes the smart city pilot policy as a quasi-natural experiment. It adopts Interpretive Structural Modeling (ISM) to identify the key influencing factors and transmission paths of carbon emissions, and employs the progressive difference-in-differences (DID) model to assess the carbon emission reduction effects, dynamic evolutionary characteristics and urban heterogeneity of smart city construction. Furthermore, the mediation effect model is applied to clarify its underlying mechanisms. The empirical results show that smart city construction significantly curbs urban carbon emissions, and this finding remains valid after a series of robustness tests, including the parallel trend test, placebo test and PSM-DID. The emission reduction effect of the policy exhibits an obvious time lag: the effect is insignificant in the first and second years after policy implementation but turns significantly negative and continues to strengthen starting from the third year. Noticeable urban heterogeneity is also observed, with a more prominent emission reduction effect in eastern regions, central cities with high administrative ranks and large-sized cities. Mechanism analysis reveals that the conventional industrial pollution reduction pathway does not serve as the primary transmission channel. Instead, a suppression effect is identified, suggesting that smart cities achieve carbon abatement primarily through the digital empowerment of energy allocation efficiency—a pathway distinct from traditional end-of-pipe governance approaches. Unlike previous studies, this study combines ISM with a staggered DID framework to reveal the dynamic effects and transmission mechanisms of smart city policies. Full article

Background: Inappropriate antibiotic use contributes to antimicrobial resistance. Evidence regarding the impact of antimicrobial stewardship programs (ASPs) in pediatric intensive care units (PICUs) remains limited. The study aims to evaluate the impact of a multidisciplinary prospective post-prescription review and feedback (PPRF) ASP in a European PICU on antimicrobial use and prescription quality (PQ). Methods: A prospective quasi-experimental study was conducted using interrupted time-series analysis to evaluate antimicrobial use trends before (July 2018–December 2020) and after (January 2021–March 2025) the implementation of a multifaceted ASP, integrated within a hospital-wide ASP, in a tertiary 24-bed medical–surgical PICU in Barcelona, Spain. Antimicrobial use was measured as days of therapy per 100 patient-days (DOT/100 PD) and per 100 discharges (DOT/100 D) and was analyzed by WHO AWaRe group and by individual drug. PQ was evaluated by means of cross-sectional point-prevalence surveys (PPSs). Results: During the study-period, median monthly activity in the PICU remained stable: 475 patient-days (IQR: 396–534) and 113 patients discharged (IQR: 102–124). Following ASP implementation, total antibiotic consumption significantly decreased, with an immediate reduction of 18.2 DOT/100 patient-days (p = 0.006) and a sustained monthly decline of 0.73 DOT/100 patient-days (p = 0.028). The COVID-19 pandemic did not significantly affect overall antibiotic consumption. Access and Reserve antibiotic use decreased significantly after ASP implementation, whereas Watch antibiotic use remained unchanged. Overall antifungal consumption was not significantly modified, although a transient increase was observed during the pandemic. At the individual drug level, several antibiotics demonstrated significant immediate decreases after ASP implementation. Prescription quality remained high (>88% optimal prescriptions), and PICU length of stay and mortality were unchanged. Conclusions: Implementation of a multidisciplinary PPRF-based ASP in a tertiary PICU was associated with a sustained reduction in antibiotic exposure without compromising prescription quality or clinical safety. These data support the effectiveness of collaborative stewardship in one of the most complex pediatric healthcare settings. Full article

Advance prediction and management of ground subsidence are crucial, as its occurrence can lead to human casualties and property damage, particularly in densely populated metropolitan areas. This study developed artificial intelligence (AI)-based models to predict the daily occurrence of urban ground subsidence using meteorological factors. Focusing on selected areas within South Korea, a daily time-series dataset spanning 2010–2015, the primary analysis period selected for record consistency, was constructed using daily precipitation, temperature, and ground subsidence occurrence records. The predictive performance of seasonality-based baselines, conventional machine-learning models (random forest, extreme gradient boosting (XGBoost)) and deep-learning models (long short-term memory (LSTM), LSTM-Transformer (LT)) was evaluated under a strictly chronological, leakage-free protocol with multi-seed repetition and bootstrap confidence intervals. Antecedent meteorological conditions provided predictive skill significantly beyond seasonal climatology; notably, this skill was captured most effectively by a logistic regression on a compact summary of the preceding day’s conditions (macro F1 = 0.608, ROC-AUC = 0.655), which the deep sequence models matched but did not exceed. Analyses of input sequence length showed that short windows outperformed longer ones, and temperature variables emerged as the dominant predictors, indicating that recent antecedent conditions—rather than extended meteorological sequences or model complexity—carry most of the predictive information. This study confirms the feasibility of meteorologically informed daily screening of ground subsidence risk at a prototype level. These findings are expected to facilitate the development of a more robust ground subsidence prediction system through future integration with station-level meteorological inputs and data on subsurface infrastructure and geological conditions. Full article

Objective: To evaluate the association between daily meteorological variability and pediatric afebrile seizure presentations using ecological time-series analysis and Distributed Lag Non-linear Models (DLNMs). Methods: This retrospective ecological time-series study was conducted in a tertiary pediatric emergency department in northwestern Türkiye. Pediatric seizure-related presentations recorded between January 2019 and December 2023 were retrospectively screened for case identification and clinical validation. The primary environmental exposure analysis was restricted to eligible afebrile seizure presentations occurring during the 2023 calendar year and linked to synchronized daily meteorological data. Associations between meteorological variables and daily afebrile seizure presentation counts were assessed using multivariable Quasi-Poisson regression and Distributed Lag Non-linear Models (DLNMs). Results: Among 777 screened seizure-related presentations, 67 children contributing 157 afebrile seizure presentations during 2023 were included. Relative humidity showed the strongest independent association with pediatric afebrile seizure presentations. Each 10-percentage-point increase in relative humidity was associated with an 18.6% higher rate of pediatric afebrile seizure presentations (IRR = 1.186; 95% CI: 1.103–1.275; p < 0.001). DLNM analysis demonstrated a significant cumulative delayed association, with a 10-percentage-point increase in relative humidity associated with a 15% higher cumulative rate of afebrile seizure presentations across the 0–3-day lag period (cumulative IRR = 1.15; 95% CI: 1.08–1.22; p < 0.001). Ambient temperature and precipitation demonstrated less consistent associations across analytical approaches, whereas humidity-related effects remained robust throughout all analyses. Conclusions: Higher relative humidity was associated with increased pediatric afebrile seizure presentations and represented the only meteorological exposure demonstrating both significant contemporaneous and cumulative delayed associations. These findings suggest that meteorological variability, particularly relative humidity, may contribute to short-term and delayed fluctuations in seizure-related healthcare utilization among children. Given the single-center ecological design and the one-year environmental exposure period, these findings should be interpreted as population-level and hypothesis-generating and should not be considered a basis for individual risk prediction or humidity-based clinical counseling. Further multicenter, multi-year prospective studies incorporating individual-level and higher-resolution environmental exposure assessment are needed to confirm these findings and clarify the biological mechanisms underlying humidity-associated seizure susceptibility in pediatric populations. Full article

Accurate and efficient early pregnancy diagnosis (EPD) in livestock is crucial for optimizing breeding management and enhancing productivity in modern animal husbandry. Over the past century, EPD technology has evolved from empirical methods to sophisticated techniques, encompassing biochemical marker detection, ultrasonic imaging, and further extending to emerging non-invasive approaches such as infrared thermography (IRT) and spectroscopic analysis. These advancements have not only improved diagnostic accuracy but also broadened the research scope to include small livestock and multiple species. This review critically examines the historical evolution, current methodologies, and applications of EPD technology, with a focus on analyzing the advantages and limitations of both traditional and emerging techniques. Additionally, it explores the potential of multimodal fusion strategies and artificial intelligence (AI) in EPD. At present, machine vision, wearable monitoring, and several AI applications remain prospective approaches rather than validated tools for routine EPD. The conclusion highlights that, despite significant progress, current technologies still face limitations in achieving in situ, non-contact, and high-throughput detection. Looking ahead, the integration of cutting-edge technologies, such as AI, small wearable sensors, and physiological time-series data analysis, holds promise for overcoming these bottlenecks, enabling more intelligent and efficient pregnancy diagnosis, and providing scientific support for modern animal husbandry. Full article

Background/Objectives: In older patients undergoing abdominal surgery, perioperative risk is influenced not only by chronological age but also by frailty, comorbidity burden, and diminished physiological reserve. These factors may limit tolerance of conventional abdominal surgery and increase interest in less invasive surgical approaches in high-risk patients. The trephine approach provides limited surgical access without routine laparotomy, but evidence regarding its use in this population remains limited. This study evaluated the technical feasibility and short-term outcomes of the trephine approach in high-risk older patients. Methods: Patients aged ≥65 years who underwent stoma surgery using the trephine approach at a tertiary referral center between January 2016 and January 2026 were retrospectively reviewed. During the study period, 488 patients underwent stoma surgery; of these, 10 (2.0%) were managed using the trephine approach and formed the study cohort. We used preoperative CT for surgical planning in all cases, with intraoperative endoluminal guidance when required. We analyzed demographic, perioperative, and short-term outcome data descriptively. Results: The mean age was 77.9 ± 9.6 years. Median ASA physical status was 4, median Clinical Frailty Scale score was 5.5, and median Charlson Comorbidity Index was 5.5. Eight patients (80%) underwent emergency surgery. The planned procedure was completed without conversion to laparotomy in 9/10 patients (90%), and bowel resection was performed through the same incision in 4/10 (40%). Median operative time was 32.5 min, and median hospital stay was 5 days. Major complications occurred in 3/10 patients (30%), and 30-day mortality was 2/10 (20%). Conclusions: In this small retrospective series of high-risk older patients, the trephine approach was technically achievable in most cases when used with preoperative CT-based planning and, when needed, intraoperative endoluminal guidance. These findings provide preliminary descriptive evidence regarding the feasibility of this approach in a highly selected clinical setting. Given the small sample size, lack of a comparator, and observed postoperative morbidity and mortality, no conclusions can be drawn about safety, comparative effectiveness, or patient-selection criteria. Full article

Climate policy in Italy has come to treat energy and resource productivity as central levers, reflecting a broader recognition that how efficiently an economy uses energy and materials shapes its environmental outcomes. This study aims to investigate the dynamic nexus between energy productivity, resource productivity, and carbon dioxide (CO₂) emissions in Italy. To do this, this study uses quarterly time series data from 2000 to 2022 and employs wavelet quantile methods, specifically wavelet quantile regression and wavelet quantile correlation. The wavelet quantile correlation reveals that a euro per kilogram unit change in resource productivity negatively correlates with CO₂ emissions by approximately −0.9 metric tonnes, and a euro per kilogram of oil equivalent unit change in energy productivity negatively correlates with CO₂ emissions by approximately −0.9 metric tonnes across all periods and quantiles, particularly in the long term. Moreover, the findings of wavelet quantile regression reveal that, in the long run, a euro per kilogram unit change in resource productivity and a euro per kilogram of oil equivalent unit change in energy productivity reduce CO₂ emissions across all periods and quantiles by approximately −0.40 and −1.0 metric tonne, respectively. The study recommends that stakeholders in Italy should invest in both short- and long-term energy productivity programs, such as investing in smart appliances that adjust energy consumption and a Combined Heat and Power (CHP) System, as all will be effective. Beyond its methodological novelty, Italy’s dualistic industrial base and EU-driven decarbonisation commitments make it a substantively useful case for a distributional, multi-horizon analysis of this kind. Full article

With the increasing penetration of renewable energy and inverter-based resources, power systems exhibit stronger uncertainty and nonlinear dynamic characteristics, which increases the need for accurate short-term prediction of dynamic states. This study proposes a hybrid prediction method combining Long Short-Term Memory (LSTM) networks and XGBoost to improve the forecasting accuracy of key dynamic variables. The LSTM module is used to extract temporal dependencies from historical time-series data, and the extracted deep features are fused with the raw input features to construct an augmented feature vector. An XGBoost regressor is then employed to capture nonlinear feature interactions and generate the final prediction results. The proposed method is evaluated using rotor speed, active power, and power angle as representative dynamic variables. Test-set results in physical units show that the proposed model achieves RMSE values of 1.0 × 10⁻⁶ p.u., 2.5808 MW, and 0.0001 deg, and MAE values of 1.0 × 10⁻⁶, 1.0269 MW, and 0.0001 deg, respectively. Compared with the reference model, the proposed method reduces both RMSE and MAE for all three variables, indicating that the LSTM-XGBoost framework can improve dynamic-state prediction accuracy in power systems. Full article

The Yangtze River Basin is a critical water supply region in China. To quantify the contributions of climate change and human activities to water resources across multiple temporal scales, this study focuses on actual evapotranspiration (AET), which directly influences water availability. Monthly runoff data from the Zhimenda Hydrological Station in the source region of the Yangtze River for the period 1982–2019 were analyzed using the Mann–Kendall (M-K) abrupt change test and the Bernaola–Galván (B-G) segmentation algorithm to identify the change point in runoff depth. The study period was subsequently divided into a baseline period and a post-change period. The ABCD hydrological model was employed to simulate monthly runoff variations during both periods, and the simulated results were used to calculate intra-annual (seasonal and monthly) AET. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was then applied to the AET series derived from the ABCD model to analyze temporal trends and intra-annual distribution characteristics. Finally, a multi-time scale Budyko framework was constructed to conduct attribution analysis based on AET data, quantifying the respective contributions of climate change and human activities to AET variation. The results indicate that: (1) The change point in the runoff series was detected in 2008. The Nash–Sutcliffe efficiency coefficients for both the baseline and post-change periods exceeded 0.88. (2) At the monthly scale, AET showed an increasing trend in January, February, July, September, November, and December. At the seasonal scale, AET exhibited a decreasing trend in spring and summer and an increasing trend in autumn and winter, though these trends were not statistically significant. Despite the varying directional trends observed across individual months and seasons, AET showed no statistically significant changes at either the seasonal or monthly intra-annual scale. (3) The intra-annual distribution of AET in the source region of the Yangtze River was highly consistent with precipitation patterns, showing significant synchronous variation. (4) Attribution analysis revealed that human activities played a dominant role in driving the observed trends in AET. Full article

Railway settlement requires long-term monitoring with millimeter-level accuracy; however, conventional leveling provides limited spatial coverage and requires considerable field effort. This study presents a practical assessment of selected StaMPS parameter settings for ComSAR-based railway settlement monitoring using Sentinel-1 InSAR, with conventional StaMPS PS-InSAR used as a comparative workflow. A ComSAR-based compressed interferogram stack and a conventional PS-InSAR workflow were applied to a section of the Honam High-Speed Railway using 61 Sentinel-1A IW SLC scenes acquired between 28 December 2018 and 29 December 2020. Track Concrete Layer (TCL) leveling data acquired at three annual epochs in December 2018, 2019, and 2020 were projected onto the radar line of sight (LOS) using local incidence angles. The InSAR LOS displacement time series were then compared with the leveling-based linear reference trend using trend-referenced RMSE and a 5 m spatial matching criterion. Four StaMPS parameters were evaluated: spatial resampling grid size before unwrapping (unwrap_grid_size), Goldstein filter window size (unwrap_gold_n_win), temporal window for phase unwrapping (unwrap_time_win), and temporal low-pass filtering window (scn_time_win). A univariate parameter-effect assessment was conducted, in which each parameter was varied individually while the remaining parameters were held at their reference settings. Among the four parameters examined, unwrap_grid_size produced the clearest first-order RMSE response in the ComSAR-based workflow under the reference settings used in this study. Grid sizes of 10–20 m produced clear degradation in trend-referenced validation performance, with the RMSE increasing to approximately 6.7–7.4 mm, whereas 40 m was the smallest tested grid size that recovered practically stable RMSE-based validation performance, at approximately 5.58 mm. In contrast, unwrap_gold_n_win, unwrap_time_win, and scn_time_win produced only small or localized numerical RMSE variations, with no consistent deterioration trend across the tested settings. The conventional PS-InSAR workflow also showed relatively stable RMSE-based validation performance across the tested parameter ranges, with RMSE values of approximately 4.87–4.91 mm. These findings provide case-study-based practical guidance for selecting StaMPS parameter settings in ComSAR-based railway settlement monitoring. Full article

Somatic in situ regeneration (SISR) is a unique reproductive mode of the green-tide-forming alga Ulva prolifera. Based on reproductive and developmental characteristics, a previous study has speculated that SISR represents an early evolutionary form of somatic embryogenesis (SE) in land plants, but molecular evidence was lacking. In this study, time-series RNA-Seq was conducted during SISR, focusing on key regulatory modules in SE, including phytohormones, stress responses, epigenetic modifications, and transcription factors (TFs). Additionally, transcriptome data were subjected to homology analysis with 13 phylogenetically representative plant genomes. Results indicated that overall transcriptional dynamics during SISR were highly similar to those in SE, suggesting that the initiation of SISR likewise requires rapid auxin accumulation and transport, as well as the involvement of numerous stress-responsive genes. Epigenetic modifications displayed a sequential trend of dedifferentiation followed by redifferentiation. Notably, most key SE-related TFs were detectable among the differentially expressed genes (DEGs), and exhibited conserved regulatory relationships as in SE. Combined with the revealed homology of module-related genes between SISR and SE, these findings suggested that SISR and SE might share deep homology in terms of conserved molecular modules and utilize a similar core hierarchical regulatory network. This raised the possibility that the underlying molecular modules of SE could have evolved prior to plant terrestrialization. Full article

For Internet of Underwater Things (IoUT)−oriented condition monitoring of underwater propulsion systems, continuous visual observations can provide rich information on the spatial distribution and temporal evolution of cavitation, while the direct transmission of complete image sequences may impose a substantial data burden on underwater communication networks. To provide compact and physically interpretable visual monitoring information, this study develops an image-derived multi-domain feature−extraction method for hydrofoil cavitation monitoring. Cavitation image sequences of an original NACA0009 hydrofoil and a hydrofoil with hole−pit structures were analyzed as two experimental configurations exhibiting different cavitation behaviors. Static−background subtraction, RGB−channel−response processing, Canny edge detection, morphological processing, and threshold-based focusing were applied to extract cavitation regions. The normalized cavitation−area time series of the tip leakage vortex (TLV), tip separation vortex (TSV), and six local regions were then constructed from consecutive images and treated as image-derived monitoring signals. Time−domain and spatial features were used to characterize the overall cavitation level and regional distribution, while Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) were further employed to describe spectral fluctuations and multi-scale transient variations. The extracted monitoring features showed clear responses to changes in hydrofoil configuration and operating conditions. Under the baseline condition, the mean TLV and TSV cavitation−area features of the hydrofoil with hole−pit structures were approximately 40.0% and 85.0% lower than those of the original hydrofoil, respectively. Differences were also observed in the regional, frequency−domain, and time−frequency characteristics and remained observable under the two changed operating conditions. These results indicate that continuous cavitation images can be transformed into complementary multi-domain descriptors for characterizing variations in cavitation behavior. The proposed approach therefore provides a potential visual sensing and feature−extraction component for future IoUT-based condition monitoring of underwater propulsion systems, while reducing reliance on the transmission of complete image sequences. Full article

Against the backdrop of China’s dual-carbon goals—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060—capacity optimization in the steel industry can no longer rely solely on aggregate output reduction. Instead, governance must shift toward a multidimensional approach that combines scale control, structural adjustment, and coordinated regional allocation. This study develops a quantifiable and interpretable assessment model for capacity-adjustment pressure. Monthly provincial crude steel output is used as a high-frequency proxy for capacity utilization and production adjustment. Additive time-series decomposition is applied to extract three components from monthly output—trend, residual, and volatility—which respectively represent structural evolution, short-term deviations, and exposure to shocks. The model further incorporates multidimensional variables, including downstream steel demand, resource and transport constraints, scrap steel ratio, and policy constraints. On this basis, a two-stage hybrid assessment framework is developed. In the first stage, extreme gradient boosting (XGBoost) is used to learn nonlinear relationships and derive data-driven feature importance. In the second stage, a composite pressure index is constructed and transformed into a standardized 0–100 score through a robust rank-based mapping mechanism. Dual thresholds are then used to generate three policy recommendations: maintaining current capacity, structural optimization, and capacity reduction. The results show that production trends and volatility intensity are the primary drivers of capacity-adjustment pressure, while pronounced spatial heterogeneity requires highly localized strategies. The classification assigns 25 provinces to maintaining current capacity, 3 to structural optimization, and 3 to targeted capacity reduction. Finally, integrating SHapley Additive exPlanations (SHAP) enhances model interpretability and provides a quantitative basis for shifting from indiscriminate capacity suppression toward differentiated, region-specific capacity governance, thereby supporting the sustainable low-carbon development of the global steel industry. Full article

Remaining useful life (RUL) prediction of power transformers is essential for ensuring reliable operation and supporting condition-based maintenance. However, the complex nonlinear degradation characteristics and long-term temporal dependencies of transformer monitoring data pose significant challenges for accurate RUL prediction. Moreover, most existing deep learning-based prediction methods provide deterministic predictions without explicitly quantifying predictive uncertainty, while the consequences of RUL overestimation and underestimation in practical maintenance are inherently asymmetric. Therefore, this paper proposes an uncertainty-aware RUL prediction method for power transformers by integrating the Patch Time Series Transformer (PatchTST) with deep evidential regression (ER-PatchTST). PatchTST is used to transform long time-series inputs into patch-level representations, enabling the model to capture local temporal patterns and long-range dependencies through patch-wise tokenization and channel-independent modeling. In addition, the deep evidential regression module is designed by placing a Normal–Inverse-Gamma (NIG) prior over the parameters of the Gaussian likelihood, which can simultaneously predict RUL and quantify aleatoric and epistemic uncertainties in a single forward pass. Furthermore, a safety-oriented RUL indicator and hierarchical warning strategy are developed, and different maintenance actions are initiated when alarms at different levels are triggered. Experiments on the ETT dataset demonstrate that ER-PatchTST achieves competitive forecasting performance compared with state-of-the-art time-series forecasting methods while simultaneously providing predictive uncertainty estimates. On the DGA dataset, ER-PatchTST achieves the best RUL prediction performance among the compared methods and provides informative uncertainty quantification for condition-based maintenance decision support. Full article