Search Results (10,935)

Frequency diverse array (FDA) sonar achieves a range- and azimuth-dependent transmit beam by applying a small frequency increment to each transmitting element. However, beam position is difficult to control due to range–azimuth coupling and time-varying characteristics. While existing FDA research has primarily focused on uniform linear arrays, there remains a lack of analysis on the Uniform Circular Frequency Diverse Array (UCFDA). Moreover, studies on transmit beampatterns have largely concentrated on continuous waveforms, resulting in time-varying beam characteristics. In the field of sonar, however, pulse signals are commonly employed for target detection. Therefore, to more accurately characterize the behavior of the beampattern under such conditions, further investigation is warranted. This paper focuses on the UCFDA sonar, specifically studying the time-varying characteristics and “dot”-shaped beam synthesis under pulsed operation. First, the time-varying and spatial scanning characteristics of the UCFDA transmit beam under linear frequency offset are analyzed. Second, a nonlinear frequency offset model is constructed, and its characteristics of high range sidelobes and significant trailing are analyzed. Then, a time-modulated nonlinear frequency offset model is built, and the relationship between the time variable in the frequency offset term and the time variable in the signal propagation term is studied in detail. When the two are identical, cancellation can theoretically eliminate the beam’s time variance. However, their physical meanings differ: the time variable in the frequency offset term reflects the signal generation moment, while the signal propagation time variable reflects the propagation law of the signal in space; they cannot cancel each other out. Finally, a nonlinear multi-carrier frequency offset model is constructed. Simulation experiments on the transmit beams under these three models are conducted to synthesize dynamically propagating “dot”-shaped transmit beams. Comparative results verify that the multi-carrier frequency offset model yields the lowest range sidelobes. Full article

Pancreatic surgery is a technically demanding field associated with frequent morbidity, with pancreatic fistula representing the dominant driver of major complications in pancreaticoduodenectomy (PD). Although refinements in operative technique, perioperative management, and institutional systems have contributed to incremental improvements, the overall incidence of clinically relevant complications has remained largely unchanged over recent decades. This narrative review provides a comprehensive overview of current strategies aimed at reducing morbidity and mortality after pancreaticoduodenectomy, focusing on modifiable technical, pharmacological, nutritional, and systems-based interventions, whilst acknowledging the underlying biological determinants that remain difficult to alter. This review synthesises contemporary evidence on fistula risk modelling, anastomotic reconstruction, and adjunctive operative techniques. The role of pharmacological interventions is examined alongside an evaluation of perioperative nutritional optimisation and enhanced recovery frameworks. Systems-based strategies such as centralisation, failure-to-rescue performance, protocolised pathways, and algorithm-driven postoperative surveillance are highlighted as emerging areas with substantial potential to impact survival independently of complication rates. Finally, this review explores future directions, including radiomics-based risk stratification, intraoperative imaging, and tailored postoperative care. Together, these domains provide a platform for reducing complication severity, standardising postoperative care, and ultimately improving patient outcomes. By integrating these perspectives, this review aims to present a comprehensive and in-depth narrative of how to reduce complications in pancreas surgery. Overall, this narrative review proposes that meaningful improvements in outcomes after PD likely do not arise from the elimination of complications altogether, but rather from improved prediction, prevention where possible, and critically, more effective systems of care that reduce the severity and consequences of complications when they occur. Full article

Wildfire monitoring in tropical regions requires robust frameworks capable of transforming heterogeneous satellite detections into consistent, event-level information suitable for decision support. This study presents the DescrEVE Fogo (Descrição de Eventos de Fogo) framework, a relational and scalable system that models daily fire events in Brazil by integrating Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections within a unified Structured Query Language (SQL)/PostGIS environment. The framework formalizes a mathematical and computational model that defines and tracks fire fronts and multi-day fire events based on explicit spatio-temporal rules and geometry-based operations. Using database-native functions, DescrEVE Fogo aggregates daily fronts into events and computes intrinsic and environmental descriptors, including duration, incremental area, Fire Radiative Power (FRP), number of fronts, rainless days, and fire risk. Applied to the 2003–2025 archive of the Brazilian National Institute for Space Research (INPE) Queimadas Program, the framework reveals that the integration of VIIRS increases the fraction of multi-front events and enhances detectability of larger and longer-lived events, while the overall regime remains dominated by small, short-lived occurrences. A simple, prototype fire-type rule distinguishes new isolated fire events, possible incipient wildfires, and wildfires, indicating that fewer than 10% of events account for more than 40% of the area proxy and nearly 60% of maximum FRP. For the 2025 operational year, daily ignition counts show strong temporal coherence with the Global Fire Emissions Database version 5 (GFEDv5), albeit with a systematic positive bias reflecting differences in sensors and event definitions. A case study of the 2020 Pantanal wildfire illustrates how front-level metrics and environmental indicators can be combined to characterize persistence, spread, and climatic coupling. Overall, the database-native design provides a transparent and reproducible basis for large-scale, near-real-time wildfire analysis in Brazil, while current limitations in sensor homogeneity, typology, and validation point to clear avenues for future refinement and operational integration. Full article

Drought is a slow-onset hazard whose economic impacts can propagate across sectors with multi-year delays. This study develops a non-linear autoregressive model with exogenous drought inputs (ARX) to assess whether U.S. drought severity, measured by the Drought Severity and Coverage Index (DSCI), contains incremental predictive information for monthly stock returns. Using weekly DSCI and stock price data from 2013 to 2023, we constructed monthly compound returns and multi-year drought lags spanning 1–5 years for four sector-representative firms: a water utility (American Water Works, AWK), two food service firms (Chipotle Mexican Grill, CMG; Starbucks, SBUX), and an industrial manufacturer (Tesla, TSLA). We compared regularized linear ARX baselines (Elastic Net, Ridge) with a non-linear Histogram Gradient Boosting Regressor (HGB) ARX model and used permutation importance to diagnose drought-relevant lag horizons. Results showed a clear, delayed drought signal for the water utility, with a dominant ~48-month drought lag, consistent with multi-year transmission through operations, regulation, and investment cycles. In contrast, drought lags added limited or unstable information for the food service firms and negligible information for TSLA, whose dynamics were dominated by non-drought drivers. Overall, the findings indicate that drought–return relationships are sector-specific and can emerge at multi-year lags, and that non-linear ARX models provide a flexible tool for detecting these delayed climate-risk signals. Full article

Background: Apical aneurysms have long been considered a critical risk marker for poor clinical outcomes in hypertrophic cardiomyopathy (HCM) individuals. This study aims to identify apical features associated with adverse outcomes and explore their incremental predictive value beyond the traditional sudden cardiac death (SCD) risk score model. Methods: From December 2019 to November 2024, 2318 HCM patients were diagnosed at Tongji Hospital. Ultimately, 65 HCM patients with apical aneurysms were included in the analysis, each having undergone conventional and contrast echocardiography, as well as speckle tracking echocardiography (STE). Results: With a median follow-up of 26 months, composite events occurred in 25 (38%) patients, while none occurred in 40 (62%). Multivariate Cox regression revealed that abnormal apical longitudinal strain average (LS-avg) significantly increased composite event risk (HR: 1.23; 95% CI: 1.02–1.48). For patients with a 5-year SCD risk score < 4% or aneurysm diameter < 20 mm, survival differed significantly between apical LS-avg ≥ −6.6% and <−6.6% (p < 0.05). Correct reclassification was 10.8% (7/65) for reduced 5-year SCD risk scores and 15.4% (10/65) for smaller aneurysms. Incorporating apical LS-avg into 5-year SCD risk score or aneurysm diameter assessment improved risk assessment (NRI: 67.7% and 66.2% for adverse event prediction). A likelihood ratio test showed that apical LS-avg enhanced prognostic accuracy in patients, with lower 5-year SCD risk scores and smaller aneurysms (all p < 0.001). Conclusions: Apical LS-avg may be associated with an increased risk of adverse cardiovascular events in HCM individuals who had apical aneurysms. On the basis of the conventional 5-year SCD risk score and aneurysm size, apical LS-avg may have the potential to be used to individually identify the high-risk group of this patient cohort, particularly among those with a 5-year SCD risk score < 4% and an aneurysm diameter < 20 mm. Full article

This paper develops a unified analytical approach for pricing a broad class of volatility-linked financial derivatives under the sub-mixed fractional geometric Brownian motion model. The proposed framework captures key empirical features of financial markets, including correlated non-stationary Gaussian increments and long-memory dependence, while preserving the semimartingale property required for arbitrage-free pricing. We present the exact distribution of the realized variance as a quadratic form of correlated non-stationary Gaussian increments, which leads to a closed-form expression for the cumulative distribution function via a Laguerre-series expansion. These distributional results enable analytical pricing formulas for an extensive family of volatility-linked derivatives. Monte Carlo simulations confirm the accuracy and computational efficiency of the proposed formulas, while numerical investigations illustrate the significant impact of non-stationarity, long-memory effects, and the Hurst parameter on derivative values. These results contribute to a deeper theoretical understanding and more effective computational methods for pricing nonlinear volatility derivatives in markets characterized by persistent temporal dependence and non-stationary stochastic dynamics. Full article

Background: Acute kidney injury (AKI) is a frequent and prognostically relevant complication of COVID-19. However, reliance on static creatinine values or binary AKI definitions may overlook clinically meaningful early renal dynamics. We evaluated whether early renal function trajectories within the first 24–48 h of hospitalization provide incremental prognostic information. Methods: We conducted a retrospective, single-center cohort study of adults hospitalized with laboratory-confirmed COVID-19 between December 2020 and December 2021. Early renal function patterns were defined using KDIGO-based changes in serum creatinine between admission and 24–48 h, classifying patients as stable, early improvement, or early deterioration. The primary outcome was in-hospital mortality. Multivariable logistic regression adjusted for age, sex, chronic kidney disease, comorbidities, inflammatory burden (C-reactive protein), nutritional status (albumin), pulmonary involvement, and treatment variables. Results: Among 721 patients, 65.2% had stable renal function, 22.5% had early improvement, and 12.3% had early deterioration. In-hospital mortality differed significantly across dynamic patterns (p = 0.007). Mortality was lowest in the stable group (35.1%) and higher in both early improvement (48.1%) and early deterioration (44.9%). After multivariable adjustment, early improvement remained independently associated with higher in-hospital mortality compared with stable renal function (adjusted OR 1.53, 95% CI 1.03–2.28), while early deterioration showed a directionally similar but non-significant association. Early improvement was also associated with higher AKI burden and increased need for acute de novo hemodialysis. Conclusions: Early renal function change patterns within the first 24–48 h of hospitalization carry prognostic value beyond static creatinine measures. Apparent early creatinine improvement may reflect recovery from prior injury or systemic instability rather than true renal recovery, identifying a subgroup at heightened risk. Classification based on early renal function assessment may enhance early risk stratification in hospitalized patients with COVID-19. Full article

This study examines Chinese farmers’ stated preferences and the compensation they would be willing to accept (willingness to accept; WTA) in return after adopting the Visualization System (VS), a promising method of smart agricultural technology. Using discrete choice experiments and a mixed logit model, we investigate farmers’ preferences under uncertain price premiums. Specifically, premium is defined as the additional price increment associated with VS adoption, reflecting the potential market reward for improved transparency, traceability, and other benefits. Uncertainty is measured by different fluctuation levels of this premium. We also assess the impacts of farmers’ individual characteristics on their WTA. Results (n = 348) show that farmers prefer higher premiums and lower fluctuations. Better VS knowledge reduces farmers’ WTA by 0.439 CNY/kg, and younger farmers tend to be more tolerant of fluctuations. Among younger farmers, those without off-farm income are more sensitive to fluctuations than those with off-farm income. Importantly, enhancing farmers’ VS knowledge leads to a 50.3% decrease in the implied price relative to the reference price, suggesting it may be more effective than mitigating fluctuations or targeting younger farmers. Overall, our findings highlight the potential of smart agriculture in China and suggest that enhancing farmers’ awareness and understanding of the VS is key to accelerating adoption. Full article

The non-renewable nature of traditional fossil fuels, along with the environmental and health hazards posed by their emissions, underscores the urgent need to reduce transmission losses in power grids. This study employs single-variable experiments, first-principles calculations, and thermodynamic calculations. The results show that, although the mass fraction and increment of Si are greater than those of Mn and V, the increase in electrical resistivity of 8030 aluminum rods caused by Si is only slightly higher than that caused by Mn and V. In contrast, trace additions of Mn and V significantly increase electrical resistivity, with respective increments of about 0.353 ± 0.011 nΩ·m/0.01 wt.% (Mn) and 0.373 ± 0.009 nΩ·m/0.01 wt.% (V). Si has a weaker effect on electrical resistivity, with an increment of approximately 0.052 ± 0.001 nΩ·m/0.01 wt.% (Si), and the increase in electrical resistivity diminishes as the Si mass fraction increases. The study also shows that at 700 °C for 30 min, a stable, high-density VB₂ phase forms. With an average density more than twice that of the melt, VB₂ settles at the bottom of the melt and effectively removes V. These findings are significant for producing 8030 aluminum rods with lower electrical resistivity. Full article

Purpose: This study develops a predictive model for radiation pneumonitis (RP) risk in lung cancer patients after volume-modulated arc therapy (VMAT) that leverages high-dimensional dosiomics and dose–volume histogram (DVH) features within IDLSS (incremental-dose interval-based lung subregion) lung subregions. Methods: We retrospectively analyzed data from 136 lung cancer patients treated with VMAT between 2015 and 2022, including 39 patients who developed RP greater than Grade 2. Using the IDLSS method, seven regions of interest (ROIs), including the Planning Target Volume (PTV), normal lung, and five subdivided lung areas, were delineated on pretreatment Computed Tomography (CT) images. DVH, radiomics, and dosiomics features were extracted from these ROIs and organized into nine distinct feature sets. A comprehensive pipeline was applied, integrating IDLSS-defined lung subregions, high-dimensional dosiomics features, LASSO-based feature selection, and SMOTE oversampling to address class imbalance in the training data. Logistic regression, random forest, and feedforward neural networks were constructed and optimized via tenfold cross-validation. Model performance across different feature sets was evaluated via the average AUC, F1 score, and other performance metrics. Results: LASSO regression revealed that BMI and volume within the 5–10 Gy and 10–20 Gy lung subregions were significant predictors of RP. The performance evaluation demonstrated that the dosiomics features consistently outperformed the DVH features across the models. Combining radiomics and dosiomics achieved the highest predictive accuracy (AUC = 0.91, ACC = 0.89, NPV = 0.95, PPV = 0.78, F1 score = 0.82, sensitivity = 0.88, specificity = 0.90). Applying SMOTE during training significantly improved sensitivity without compromising specificity, confirming the value of balancing strategies in enhancing model performance. Incorporating all the features together did not provide additional performance gains. Conclusions: Integrating radiomics and dosiomics features extracted from IDLSS-defined lung subregions significantly enhances the ability to predict RP after VMAT, surpassing traditional DVH metrics. The substantial contribution of dosiomics features highlights the importance of spatial dose heterogeneity in RP risk assessment. Full article

This study examines how aggregate financial risk tolerance (FRT), measured from repeated survey responses, co-evolves with stock-market dynamics over time. The observed FRT index is treated as a noisy preference signal containing both gradual drift and episodic deviations, and its market relevance is evaluated under time variation, frequency components, and stress regimes. Using monthly data that align the survey-based FRT index with market returns and risk measures, a three-part econometric design is implemented. First, a time-varying parameter VAR (TVP-VAR) characterizes bidirectional, non-constant linkages between FRT and market outcomes. Second, signal-extraction methods decompose FRT into a smooth “normal” component and a high-frequency “abnormal” component (with robustness to alternative filters) to test whether short-run deviations contain distinct information for volatility and downside risk. Third, a Markov-switching specification assesses state dependence by testing whether the FRT–market relationship differs between low-stress and high-stress regimes. Across specifications, the FRT–market linkage is strongly state dependent: the sign and magnitude of FRT effects drift over time and differ across regimes, with high-frequency FRT deviations aligning more closely with risk dynamics than the smooth component. Predictive validation is provided via out-of-sample forecasting of next-month market risk using elastic net and gradient boosting relative to an AR(1) benchmark; explainability analysis (SHAP) indicates that abnormal FRT contributes incremental predictive content beyond standard market-state variables. Overall, the framework offers a mathematically transparent approach to modeling survey-based preference signals in markets and supports regime-aware forecasting and risk-management applications. Full article

Mixed forests provide several ecosystem service benefits, and they also often show higher productivity than pure forests. In mixed forests, several interactions among tree species occur, with size-symmetric and size-asymmetric competition being particularly important. We studied diameter growth of European beech in pure stands and in mixtures with oak, maple, pine, spruce, fir, and spruce and fir combined on extremely diverse beech sites in Slovenia, using forest inventory sample plots (n = 26,793, 500 m² each). For each mixture, we developed models of 10-year individual tree diameter increment (id) using natural splines and incorporating tree, competition, stand, site, and climate variables that were mainly gathered in regular forest inventories. Competition was represented using simple indices: stand basal area (BA) for size-symmetric competition, basal area of overtopping trees (BAL) for size-asymmetric competition, and reduced competition due to harvesting (CUT). The models revealed differences among mixtures and a strong influence of competition. Id was among the lowest in pure stands and substantially higher in mixtures, indicating strong intraspecific competition. Overall, size-symmetric competition was more influential, but size-asymmetric competition appeared important in some mixtures. We recommend growing beech in mixtures with other species and applying a forest management approach that accounts for competition symmetry, which proved crucial in each mixture. Full article

Psoriasis is a common, chronic skin disorder that has negative impacts on patients’ quality of life, and is triggered by a combination of genetic and environmental factors. However, epidemiological evidence about the effect of air pollution on psoriasis risk is still limited and inconsistent. The generalized additive model (GAM) was applied to investigate the association between common air pollutants and daily psoriasis outpatient visits in Guangzhou, China from 2013 to 2019. The analysis focused on particulate matter with an aerodynamic diameter of less than 10 μm and 2.5 μm (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). To examine the effect modifications, stratified analyses were conducted by gender, age, and season. Population attributable fraction of psoriasis burden from ambient air pollution exposure was further calculated. A total of 145,034 psoriasis outpatient visits were included during the study period. Each 10 μg/m³ increment in PM_2.5, PM₁₀, SO₂, and NO₂ was significantly associated with an excess risk of psoriasis outpatient visits of 3.46% (95% CI: 2.53%, 4.39%), 2.51% (95% CI: 1.86%, 3.17%), 4.73% (95% CI: 2.67%, 6.82%), and 4.75% (95% CI: 3.78%, 5.73%) at lag05. Stratified analysis revealed notably stronger effects during the cold seasons. Based on the World Health Organization’s Ambient Air Quality Guidelines, PM_2.5, PM₁₀, NO₂, and SO₂ accounted for 9.08% (95% CI: 6.54%, 11.74%), 4.73% (95% CI: 3.45%, 6.06%), 8.93% (95% CI: 6.99%, 10.93%), and 0.18% (95% CI: 0.10%, 0.27%) of psoriasis outpatient visits, respectively. In conclusion, short-term air pollution exposure is an important risk factor for psoriasis outpatient visits, especially in cold seasons. PM_2.5 and NO₂ accounted for a relatively larger attributable burden among common air pollutants. Effective strategies are needed for air pollution control and prevention of psoriasis exacerbation. Full article

The increasing demand for sustainable active packaging necessitates the development of bio-based films with enhanced functional properties. This study aimed to functionalize a quince (Cydonia oblonga) by-product film, formulated in clove (Syzygium aromaticum) hydrosol by casting, incorporating varying concentrations (0–10% w/v) of broccoli (Brassica oleracea var. italica) by-product extract. Increasing the extract concentration led to increments in film thickness (102.2 to 120.2 µm), elongation at break (112.5 to 117.3%), tensile strength (1.5 to 4.2 MPa), opacity (20.2 to 24.0%), and water vapor permeability (2.0 to 2.3 × 10⁻⁸ g s⁻¹ m⁻¹ Pa⁻¹). The total phenolic content also increased from 17.6 to 24.3 mg GAE/g film, correlating with a decrease in transmittance. While Fourier-Transform Infrared spectra profiling revealed stable intermolecular interactions across all samples without chemical disruption; scanning electron microscopy analysis confirmed distinct morphological differences resulting from broccoli extract incorporation. Notably, while 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity remained stable across treatments, the 2.5% w/v extract concentration provided the highest antifungal efficacy against Aspergillus puulaauensis (15.7%), A. jensenii (8.2%) and Penicillium nordicum (5.8%) by the agar diffusion method. These results were comparable with a commercial natamycin-containing coating used as a positive control. The synergy of clove hydrosol and broccoli extract resulted in a quince-based film with superior mechanical and bioactive properties. Full article

In this study, CaO-SiO₂-Al₂O₃-MgO-TiO₂ slag was used as the research object to simulate the blast furnace ironmaking process. Based on the experimental data, the influences of basicity (R(w(CaO)/w(SiO₂))) and the magnesia–alumina ratio (w(MgO)/w(Al₂O₃)) on desulfurization ability are discussed. Additionally, the influences of dissimilarity, basicity, and the magnesia–alumina ratio on slag structure were analyzed using Fourier transform infrared spectroscopy (FT-IR). The results show that when w(Al₂O₃) = 20% and w(MgO)/w(Al₂O₃) = 0.50, sulfide capacity (lgC_s) accretion with the increment in R. Moreover, when w(Al₂O₃) = 20% and R = 1.30, sulfide capacity accretion with the increment in w(MgO)/w(Al₂O₃). Fourier transform infrared spectroscopy was used to confirm that, with increasing basicity and the magnesia–alumina ratio, the concentration of dissociated free oxygen ions (O²⁻) in slag increases, and these ions interact with the bridging oxygen (O⁰) of silicate to depolymerize the complex Si-O structure into simpler units. Full article