Search Results (4,075)

Background/Objectives: Spondylarthritis (SA) is characterized by high clinical heterogeneity, often resulting in a discrepancy between systemic inflammation and patient-reported symptoms. While hematologic indices are emerging as cost-effective biomarkers, their role in phenotypic differentiation remains unclear. We investigated the utility of traditional inflammatory markers, including the novel fibrinogen-to-platelet ratio (FPR), in differentiating SA subtypes and predicting patient-reported disease activity. Methods: This cross-sectional study included 64 patients with spondylarthritis: axial SA (n = 32), peripheral SA (n = 8), and psoriatic SA (n = 24). Clinical assessments included the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Functional Index (BASFI). Systemic inflammation was evaluated via C-reactive protein (CRP), fibrinogen, and calculated ratios (NLR, PLR, MLR, and FPR). Principal Component Analysis (PCA) was employed to map the inflammatory architecture, while Receiver Operating Characteristic (ROC) curves evaluated the predictive power for high disease activity (BASDAI ≥ 4). Results: Significant phenotypic differences were observed with the FPR demonstrating superior discriminative capacity (p = 0.003). Patients with peripheral SA exhibited significantly higher FPR (median 1.88) compared to axial (1.33) and psoriatic (1.32) subtypes, and the dedicated ROC analysis for phenotypic discrimination yielded an AUC of 0.866 (95% CI: 0.745–0.987) (1.36, p = 0.039). HLA-B27 prevalence was low overall (31.3%) and in psoriatic SA (4.2%, p = 0.012). Correlation analysis revealed strong associations between BASDAI and BASFI (ρ = 0.79), NLR and MLR (ρ = 0.78), and CRP and fibrinogen (ρ = 0.66). PCA identified two independent inflammatory dimensions explaining 74.8% of variance: neutrophil-hypercoagulable axis (41.4%, driven by NLR, PLR, and MLR), and an acute-phase/fibrinogen axis (33.4%, driven by CRP, fibrinogen, and FPR). Notably, FPR clustered with acute-phase reactants rather than leukocyte-derived ratios, supporting its role as a marker of systemic inflammatory burden. Although fibrinogen is involved in the coagulation cascade, the absence of direct coagulation markers precludes definitive characterization of this component as hypercoagulable. ROC analysis revealed that fibrinogen showed the highest discriminative ability for disease activity (BASDAI ≥ 4), with an AUC of 0.690 (95% CI: 0.519–0.861), followed by NLR (0.621), MLR (0.592), and FPR (0.583). However, overall discriminative performance remained modest, with most 95% confidence intervals crossing 0.5. Conclusions: FPR emerges as a robust phenotypic biomarker capable of discriminating against SA subtypes, particularly identifying peripheral SA with high accuracy and excellent negative predictive value. In contrast, its ability to predict patient-reported disease activity remains limited, reinforcing the distinction between trait and state biomarkers. Exploratory PCA revealed that FPR clusters with acute-phase reactants rather than leukocyte-derived ratios, supporting its biological link to systemic inflammatory burden. These findings advocate for a dual-purpose biomarker approach in SA: FPR for phenotypic stratification and fibrinogen for activity assessment, while clinical indices remain indispensable for symptom monitoring. Validation in larger, prospective cohorts is warranted. Full article

Background and Objectives: Acute exacerbation of COPD (AECOPD) poses a major burden on healthcare systems, with critically ill AECOPD patients having increased morbidity and mortality. Since adverse outcomes are due both to respiratory failure and the systemic inflammatory response, prognostic markers accounting for these patterns are needed. Our aim was to investigate the predictive power of derived hematological indices for intensive care unit (ICU) mortality in patients with non-infectious versus infectious AECOPD. Materials and Methods: This is a retrospective, observational, monocentric cohort study on 88 AECOPD patients admitted to the ICU between 2018 and 2023. Descriptive statistics were performed for the entire cohort, and for predefined subgroups (non-infectious, infectious and bacterial AECOPD). Receiver Operating Characteristics (ROC) analysis was performed to test the predictive power of the studied indices. Cut-off values were identified using the Youden index. Kaplan–Meier analysis was conducted to test the association with ICU mortality. Results: Overall ICU mortality was 44%. For the whole cohort, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelets ratio (NPR) and systemic inflammation response index (SIRI) showed moderate predictive power for ICU mortality (areas under the curve (AUCs) of 0.71–0.73). Non-infectious and infectious subgroups were comparable in terms of size, demographics, comorbidities and baseline COPD characteristics (p > 0.05). Mortality was significantly higher in infectious AECOPD (64.6% versus 20%, p < 0.001). For non-infectious AECOPD, monocyte-to-lymphocyte ratio (MLR) and SIRI had very good predictive power (AUCs between 0.82 and 0.855), while NPR and systemic inflammation index (SII) showed moderate AUC values (between 0.7 and 0.79). In infectious AECOPD, only NPR retained fair predictive power (AUC 0.691), which improved in bacterial AECOPD (AUC 0.781). Conclusions: Derived hematological indices have different predictive values for ICU mortality. MLR and SIRI exhibited very good predictive power in non-infectious AECOPD, while NPR was the best discriminator in bacterial AECOPD. These stress the importance of individualized prognostication in AECOPD. Full article

High-power-density LEDs can achieve many functions that are difficult for traditional light sources and conventional LEDs to realize, pushing the semiconductor lighting technology chain to a new level. Increasing the number of chips is an effective approach to improving the light output capability of LED devices. In this study, five high-power-density LED devices with different chip numbers (4, 9, 16, 25, and 64 chips) were fabricated using the same blue LED chips, and the effects of chip number on the light output capability, spatial light distribution characteristics, and spatially correlated color temperature distribution characteristics of high-power-density LED devices were systematically investigated. The temperature distribution characteristics of the internal chips were further analyzed in combination with infrared thermal imaging results. The results show that increasing the chip number significantly enhances the total light output capability of the device; however, due to the influence of thermal coupling among chips, the saturation current and saturated luminous intensity of devices with different chip numbers are not proportional to the chip number. Increasing the number of chips in the device does not alter the intrinsic spatial emission pattern. Under optical saturation conditions, the luminous intensity distribution curves of all five devices exhibit Lambertian spatial light distribution characteristics. In terms of correlated color temperature, the CCT of all devices increases with increasing current per chip, and devices with more chips exhibit higher CCT values and a faster increasing trend. The spatial CCT distribution results show that the correlated color temperature of the device reaches its maximum in the normal direction, decreases with increasing testing angle, and exhibits good spatial symmetry. The thermal imaging results show that devices with more chips exhibit higher average chip temperatures and a relatively more uniform temperature distribution, which improves the spatial CCT uniformity of the device to some extent. Full article

Accurate demand forecasting and adaptive load balancing are critical for maintaining stability and efficiency in modern smart power grids. This study proposes a hybrid deep learning (DL) framework, termed Transformer-Generative Adversarial Network-Gated Recurrent Unit (Transformer-GAN-GRU), which integrates global attention-based temporal modeling, generative data augmentation, and sequential refinement into a unified architecture. The proposed framework captures both long- and short-term dependencies while improving representation of imbalanced demand patterns. The model is evaluated on three heterogeneous benchmark datasets, namely Pecan Street, the reliability test system-grid modernization laboratory consortium (RTS-GMLC), and the reference energy disaggregation dataset (REDD). Experimental results demonstrate that the proposed model consistently outperforms state-of-the-art baselines, achieving a maximum accuracy (Acc) of 99.49%, a recall of 99.67%, and an area under the curve (AUC) of 99.83%. In addition to high predictive performance, the framework exhibits strong stability, fast convergence, and low inference latency, confirming its suitability for real-time deployment in smart grid environments. Full article

Background: This study evaluated the independent predictive value of preoperative Systemic Immune–Inflammation Index (SII) for postoperative wound infection (WI) in diabetic patients undergoing isolated Coronary Artery Bypass Grafting (CABG). Methods: A retrospective cohort of 300 diabetic patients (2024–2025) was analyzed. The primary outcome was 30-day postoperative WI. Preoperative SII was calculated from blood counts within 24 h before surgery. Multivariable logistic regression was performed using both a primary model (adjusting for age, BMI, and comorbidities) and an extended model including glycemic control (HbA1c), smoking status, operative duration, and transfusion requirements. Model discrimination was evaluated via Area Under the ROC Curve (AUC). Statistical power and sensitivity analyses were conducted to ensure the robustness of the findings. Results: WI occurred in 7% (n = 21). Preoperative SII was significantly lower in the WI group (958.48 ± 493.49 vs. 1293.56 ± 758.15, p = 0.047). SII remained an independent predictor in the adjusted model (Adjusted OR per 100-unit increase: 0.93; 95% CI: 0.86–1.00; p = 0.048). ROC analysis confirmed an inverse predictive pattern (AUC: 0.374, 95% CI: 0.312–0.436). Comparative analysis showed that SII provided superior additional insight compared to NLR and PLR in this population. Conclusions: Preoperative SII is an independent predictor for WI in diabetic CABG patients. However, given the modest discriminative performance (AUC: 0.374), it should be integrated into a broader clinical risk assessment. Contrary to conventional expectations, lower SII values indicated increased susceptibility, suggesting that immune exhaustion rather than hyperinflammation may drive infectious risk in diabetic patients. Full article

Background: The recent U.S. Food and Drug Administration authorization of AirPods Pro as over-the-counter hearing aids (HAs) has increased interest in consumer devices as potential alternatives to traditional amplification; however, their electroacoustic performance relative to clinically fitted HAs remains unclear. The purpose of this study was to compare the electroacoustic characteristics and real-ear measures of AirPods Pro 2nd generation (APP2), AirPods Pro 3rd generation (APP3), and a traditional receiver-in-the-canal HA across mild flat, mild-to-moderate sloping, and moderate flat hearing loss configurations. Methods: Outcome measures included 2cc coupler output curves, saturation sound pressure level for a 90 dB input (SSPL90), real-ear speech mapping, maximum power output (MPO), and real-ear-to-coupler differences. Results: Coupler-based electroacoustic measures showed that APP2 and APP3 produced output comparable to the traditional HA (within 7 dB). SSPL90 outputs were similar for APP2 and APP3, whereas the HA demonstrated profile-dependent increases. In contrast, real-ear measurements demonstrated that both APP2 and APP3 consistently produced less output relative to the HA that was fitted to NAL-NL2 targets, with the largest deviations observed for moderate hearing loss and at higher frequencies (up to 14 dB). Across all configurations, MPO was consistently highest for the HA, with both AirPods devices exhibiting reduced maximum output, especially in speech-critical frequency regions. Real-ear-to-coupler difference findings indicated reduced acoustic coupling for APP3 relative to APP2 and the HA, contributing to reduced in-ear amplification despite comparable coupler outputs. Conclusions: While AirPods Pro may offer benefit for mild hearing loss or moderate high-frequency hearing loss, they do not provide output comparable to prescriptively fitted HAs. These findings underscore the continued importance of clinical verification and prescription-based fitting of hearing assistive technology for achieving appropriate audibility across hearing loss configurations. Full article

Within the framework of consistent couple stress theory (CCST) and employing Hamilton’s principle, we derive a Galerkin weak formulation to analyze the three-dimensional (3D) size-dependent free vibration characteristics of a simply supported, functionally graded (FG) magneto-electro-elastic (MEE) doubly curved (DC) microscale shell subjected to a uniform temperature change. Incorporating the differential reproducing kernel (DRK) interpolants into the weak formulation, we further develop an element-free Galerkin (EFG) method. The microscale shell of interest is composed of two-phase MEE materials, and its material properties are assumed to vary through its thickness according to a power-law distribution of the volume fractions of the constituents. The results show that the natural frequency solutions obtained using the EFG method are in excellent agreement with the reported 3D solutions for laminated composite and FG-MEE macroscale plates, with the material length-scale parameter and the inverse of the curvature radii set to zero. The effects of the material length-scale parameter, temperature change, inhomogeneity index, and mid-surface radius and length-to-thickness ratios on the FG-MEE microscale shell’s free vibration characteristics in a thermal environment are examined and appear to be significant. Full article

Many countries are actively promoting the large-scale deployment of wind power generation, both onshore and offshore. However, damage to wind turbines caused by winter lightning has become a growing concern in Japan. Japan has made efforts since an early stage to establish legal frameworks for reducing lightning damage; nevertheless, lightning damage to wind turbines remains a problem that has not been completely eradicated. After a wind turbine has been struck by lightning, it is restarted only after its structural integrity has been verified; however, the current method relies on visual inspection by workers, making accurate and rapid inspections difficult. One approach to solving this problem is to use anomaly detection techniques based on SCADA data. Research is currently underway to implement this approach. However, anomaly detection methods based on SCADA data have been criticized for their limited ability to accommodate multiple operating modes, including de-rated operation. In this study, we propose the “scaled power curve” as a robust feature that is less affected by operating modes, with its effectiveness verified through anomaly detection. This method showed improved anomaly detection accuracy compared to using the original power curve as a feature; moreover, in the present case, the method remained effective under de-rated operation. By using this feature, it is expected that a lightning damage detection model can be developed, contributing to improved availability of wind turbines. Full article

The jet structure plays an important role in both the prompt and afterglow emission phases of gamma-ray bursts (GRBs). Whether GRB jets are better described by uniform (top-hat) or structured models remains an open question. We use the afterglowpy Python package to numerically model the late X-ray afterglow light curves of a large sample of long and short GRBs, and apply the Bayesian Information Criterion (BIC) to compare the performance of top-hat and Gaussian structured jet models. Within our adopted modeling framework, we find that the top-hat model is preferred by the BIC for ∼78.9% (150/190) of long GRBs and 70% (7/10) of short GRBs. GRB 180205A and GRB 140515A exhibit $\Delta$BIC < 2 for all three model comparisons, indicating that top-hat, Gaussian, and power-law jets provide equivalent fits to their afterglow light curves. This large-sample analysis suggests that uniform jets may be more common than structured jets in the observed GRB population, although this conclusion is subject to the limitations of our model assumptions and the BIC-based model selection criterion. Furthermore, we find that the best-fit distributions of observer angle ${\theta }_{\mathrm{obs}}$, electron energy fraction ${ϵ}_e$, and isotropic equivalent energy $E_0$ differ significantly between the top-hat and Gaussian jet models, with ${\theta }_{\mathrm{obs}}$ showing the most pronounced distinction. Full article

The development of eco-efficient construction materials requires optimisation strategies that reduce cement consumption, valorise industrial by-products, and enhance performance without increasing material demand. Clay–cement sealing suspensions used in geotechnical engineering offer significant sustainability potential due to their high mineral content and compatibility with supplementary cementitious materials such as siliceous fly ash. The early-age rheological properties are essential for the design of geotechnical sealing barriers, yet the influence of chemical additive sequencing on flow behaviour remains poorly understood. This study examines how the priority of sodium silicate addition—introduced either before cement and siliceous fly ash (the “Prior” series) or after them (the “After” series)—affects the flow curves, yield stress, thixotropy, and equilibrium shear stress of clay–cement–fly ash sealing suspensions. Ascending flow curves were fitted to the Casson, Herschel–Bulkley, and Ostwald–de Waele models, and a shear-rate-resolved thixotropic power density analysis was applied to decompose the hysteresis behaviour. The results demonstrate that the Prior series produces deflocculated colloidal clay networks with localised cementitious agglomerates, exhibiting lower shear stresses at low shear rates but markedly higher yield stress amplitudes and larger hysteresis loop areas. The After series yields more uniformly distributed nucleation–coagulation networks with smaller hysteresis loops and pronounced structural rebuilding at low shear rates during the ramp-down phase. These findings provide a physicochemical framework for tailoring the early-age rheology of clay–cement suspensions through controlled additive sequencing, with direct implications for pumpability, injectability, and post-placement structural recovery in geotechnical applications. Full article

Achieving sustainability requires that energy transition generates measurable environmental benefits beyond the power sector, yet it remains unclear whether renewable electricity expansion is associated with lower waste sector methane intensity, a major source of short-lived climate forcing. Using a global country–year panel and two-way fixed effects, we examine whether this relationship, and its sustainability implications, varies with development stage, institutional quality, and waste system characteristics. We find no robust inverted-U Environmental Kuznets Curve once country and year fixed effects are included. Instead, higher renewable electricity shares are consistently associated with lower waste sector methane intensity, and this association strengthens with income. A 10-percentage-point increase in renewable share corresponds to about 2.7%, 4.2%, and 6.0% lower intensity at the 25th, 50th, and 75th income percentiles. The negative association is stronger in countries with higher governance quality, while waste management capacity and organic waste composition reveal additional heterogeneity in the observed association. Overall, electricity decarbonization alone is not a uniform instrument for reducing diffuse biological emissions; sustainable methane mitigation likely requires coordinated governance linking renewable transition with waste system modernization. Full article

Understanding why cities diverge in motor vehicle ownership trajectories is critical for designing differentiated and sustainable transport policies. This study develops an integrated national–city analytical framework to examine heterogeneous urban motorization processes in China. A national Gompertz curve is first estimated to represent the benchmark income–ownership relationship. City-specific deviations are then decomposed into two interpretable dimensions: a horizontal stage parameter (h), capturing relative advancement or delay in motorization timing, and a vertical scaling parameter (s), reflecting persistent ownership intensity differences conditional on income. Results show substantial and multi-dimensional heterogeneity across cities. Stage timing (h) and ownership intensity (s) are only weakly correlated, indicating that earlier transition into higher motorization stages does not necessarily imply above-benchmark ownership intensity. Random forest models with time-forward validation demonstrate strong explanatory power (R² ≈ 0.88 for h and 0.80 for s). SHAP-based interpretation reveals that stage deviation is primarily associated with transport supply and urban structural characteristics, whereas ownership intensity deviation is more strongly linked to urban spatial scale and economic structure. Regulatory measures, including purchase and driving restrictions, exhibit comparatively smaller and heterogeneous effects. By disentangling timing and intensity dimensions of urban motorization, this study refines conventional income-based diffusion models and provides quantitative evidence that structural urban characteristics play a more fundamental role than regulatory interventions in shaping inter-city motorization differences. Full article

High-precision wind power prediction improves grid stability and reduces curtailment losses. Existing methods face three limitations: static graphs cannot capture dynamic spatial correlations under weather changes, time series models miss multi-scale temporal features, and frequency-domain analyses lack physical constraints. We propose: (1) a dynamic distance correlation weighted graph that adaptively combines geographic and power correlations for weather–terrain coupling; (2) a spatio-temporal-frequency fusion framework integrating graph networks, bidirectional GRUs, and a patchwise sparse time–frequency module; (3) a turbine power curve-constrained frequency mixer for physical consistency. On the SDWPF dataset, our model achieves MAE reductions of 37.47–43.32% and RMSE reductions of 37.93–42.70% versus baselines, outperforming state-of-the-art methods. The approach demonstrates superior performance in complex spatio-temporal scenarios. Full article

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article

Urban travel intensity is a practical proxy for human mobility, but direct mobility data are often costly, geographically restricted, and privacy sensitive. UTScan uses continuous ambient seismic data to estimate urban travel intensity in a passive, non-intrusive manner. Model development used 10 cities in Hubei Province during January–April 2020, and external validation used 84 non-Hubei cities that satisfied the study’s data-quality criteria. From each hourly power spectral density (PSD) curve, we extracted 13 features in the 2–20 Hz anthropogenic band, applied a station-wise low-activity baseline subtraction, and then modeled daily travel intensity with a CatBoost–LSTM framework. Under the calendar-based forward-validation protocol, the final UTScan implementation (FusionB) achieved a mean RMSE of 0.537 ± 0.214 and a mean Pearson correlation of 0.768 ± 0.076 across the internal Hubei folds and a mean RMSE of 0.789 ± 0.229 and a mean Pearson correlation of 0.605 ± 0.370 across the 84-city external validation set. Additional sensitivity analyses using alternative validation windows and light-touch outlier handling indicated that the main conclusions were stable, while single-station representativeness remained the principal limitation. Ambient seismic noise is therefore a useful passive proxy for estimating city-scale mobility dynamics, especially for abrupt mobility disruptions, but its interpretation remains conditional on station siting, source mixture, and the proxy nature of the Baidu travel-intensity target. Full article