Search Results (4,027)

This work aims to evaluate the economic and environmental performance of hybrid additive manufacturing (HAM) chains with metal forming operations in comparison with conventional manufacturing approaches. The approach integrates processes such as Wire-Arc Directed Energy Deposition (DED-Arc), machining, and incremental sheet forming to combine material deposition, shaping, and finishing within a single processing chain. To support this, a process-based cost model (PBCM) was developed to estimate production costs by linking process parameters with technological and operational variables and implementing computer-assisted modeling of the processing chain for identification of the production costs and corresponding key cost drivers. In parallel, a cradle-to-gate Life Cycle Assessment (LCA) was performed to evaluate environmental impacts across the stages of the HAM chain. The results indicate that direct labor, material, and machine usage are the primary cost drivers in the HAM chain. Compared to conventional chains of machining from solid or die casting, HAM achieves high reductions in production cost, from 67.8% to 84.5%, and in environmental impact of up to one order of magnitude, due to lower material consumption and independence from dedicated tooling. Overall, this work provides an integrated framework for the economic and environmental assessment of HAM, laying the foundation for future industrial implementation. Full article

Background: Arterial stenosis produces nonlinear changes in vascular impedance that are challenging to investigate in real time using either benchtop flow phantoms or high-fidelity computational fluid dynamics (CFD) models. Objective: This study aimed to develop and evaluate a low-cost printed circuit board (PCB) analog capable of reproducing the hemodynamic effects of progressive arterial stenosis through an R–L–C mapping of vascular mechanics. Methods: A lumped-parameter (0D) electrical network was constructed in which voltage represented pressure, current represented flow, resistance modeled viscous losses, capacitance corresponded to vessel compliance, and inductance represented fluid inertance. A variable resistor simulated focal stenosis and was adjusted incrementally to represent progressive narrowing. Input U_in, output U_out, peak-to-peak V_pp, and mean V_avg voltages were recorded at a driving frequency of 50 Hz. Physiological correspondence was established using the canonical relationships. $R={\displaystyle \frac{8\mu l}{\pi r^4}}$, $L={\displaystyle \frac{pl}{\pi r^2}}$, $C={\displaystyle \frac{3\pi r^3}{2Eh}}$, where μ is blood viscosity, ρ is density, E is Young’s modulus, and h is wall thickness. A calibration constant was applied to convert measured voltage differences into pressure differences. Results: As simulated stenosis increased, the circuit exhibited a monotonic rise in U_out and V_pp, with a precise inflection beyond mid-range narrowing—consistent with the nonlinear growth in pressure loss predicted by fluid dynamic theory. Replicate measurements yielded stable, repeatable traces with no outliers under nominal test conditions. Qualitative trends matched those of surrogate 0D and CFD analyses, showing minimal changes for mild narrowing (≤25%) and a sharp increase in pressure loss for moderate to severe stenoses (≥50%). The PCB analog uses a simplified, lumped-parameter representation driven by a fixed-frequency sinusoidal excitation and therefore does not reproduce fully characterized physiological systolic–diastolic waveforms or heart–arterial coupling. In addition, the present configuration is intended for relatively straight peripheral arterial segments and is not designed to capture the complex geometry and branching of specialized vascular beds (e.g., intracranial circulation) or strongly curved elastic vessels (e.g., the thoracic aorta). Conclusions: The PCB analog successfully reproduces the characteristic hemodynamic signatures of arterial stenosis in real time and at low cost. The model provides a valuable tool for educational and research applications, offering rapid and intuitive visualization of vascular behavior. Current accuracy reflects assumptions of Newtonian, laminar, and lumped flow; future work will refine calibration, quantify uncertainty, and benchmark results against physiological measurements and full CFD simulations. Full article

Network fault diagnosis has evolved in response to the needs of modern networks, transitioning from traditional methods, such as passive and active monitoring, to advanced learning techniques. While conventional methods often introduce invasive traffic and control overhead, newer approaches face challenges such as increased internal processes and the need for extensive knowledge of network behavior. Learning-based methods offer an advantage by not requiring a complete network model, allowing the use of statistical and Machine Learning techniques to process historical data. However, existing learning methods face limitations, such as the need for extensive data samples and extended retraining periods, which can leave systems vulnerable to failures, particularly in dynamic environments. This work addresses these issues by proposing an incremental learning approach for continuous fault diagnosis in IP-based networks. The approach utilizes online learning to process symptoms in real-time, adapting to network changes while managing data imbalance through drift detection and rebalancing strategies, such as ADWIN and SMOTE. We evaluated the performance of this method using 25 incremental algorithms on the SOFI dataset. The results, assessed using metrics such as recall, G-mean, kappa, and MCC, demonstrated high performance over time, indicating the potential for resilient, adaptive fault detection processes in dynamic network environments. Additionally, a non-invasive process can be ensured through peripheral observation of failure symptoms, provided that data collection does not increase network traffic, overhead control, or internal network processes. Full article

Background: Measuring glycated hemoglobin (HbA1c) is essential for assessing long-term glycemic control, yet direct testing remains expensive and underutilized in many large-scale health surveys and resource-constrained settings. This study aims to (i) deliver a highly accurate and interpretable ML model for predicting HbA1c from routinely collected clinical, biochemical, and demographic data, (ii) reduce dependency on extensive laboratory panels by identifying a compact, cost-efficient subset of key predictors, and (iii) establish a transferable, explainable modeling framework applicable across chronic disease biomarkers. Unlike prior HbA1c prediction studies that focus primarily on classification or accuracy-driven models, this work introduces a unified framework for continuous HbA1c regression that jointly integrates cost-oriented feature parsimony, stratified regression validation, and explainability by design. Methods: We aggregated data from the National Health and Nutrition Examination Survey (NHANES) cycles 2007–2020, encompassing 66,148 records and 224 candidate features. We implemented a two-stage feature selection pipeline: Incremental Correlation Selection (ICS) to narrow the variable space, followed by Recursive Feature Elimination with Cross-Validation (RFECV) to isolate the most informative features. Model interpretability was assessed using partial dependence plots and feature importance analysis. Results: The optimal model, LightGBMRegressor with most-frequent imputation, achieved R² = 0.7161, MAE = 0.334, MSE = 0.304, and MAPE = 5.56%, while using only 40 selected features. Interpretability analysis revealed clinically coherent relationships that align with physiological expectations. Discussion: The proposed framework maintains robust predictive performance while substantially reducing the number of required input features, enabling cost-efficient HbA1c estimation together with transparent, physiologically coherent model insights. By consolidating continuous HbA1c prediction, cost-aware feature selection, stratified evaluation, and explainability within a single pipeline are enhanced. Conclusions: This study advances beyond existing approaches and offers a practical blueprint for scalable biomarker estimation in population health and clinical decision-support applications. Its explainable, efficient, and generalizable design positions it as a strong candidate for clinical decision-support and population-health applications. Full article

Background and Objectives: Anatomical staging alone insufficiently explains survival heterogeneity in patients with resected non-small cell lung cancer (NSCLC). Although inflammation-based biomarkers have demonstrated prognostic value, the clinical relevance of erythrocyte-derived indices—particularly mean corpuscular volume (MCV)—remains poorly characterized in this setting. This study evaluated the prognostic significance of preoperative MCV and examined whether its integration with the Noble and Underwood (NUn) score improves survival prediction. Methods: We retrospectively analyzed patients with stage I–IIIA NSCLC who underwent complete surgical resection. Associations between clinicopathological variables and overall survival (OS) were assessed using Cox proportional hazards models. Prognostic performance was evaluated using the concordance index and the integrated time-dependent area under the curve. Continuous variables were modeled on their original scale without dichotomization. Results: Model comparison using the Akaike Information Criterion indicated that incorporation of the composite NUn–MCV index into the intermediate model—comprising age, basal metabolic rate, American Society of Anesthesiologists physical status, pleural invasion, and pathological stage—provided a superior model fit compared with inclusion of the NUn score and MCV as separate covariates. On this basis, the composite NUn–MCV model was selected as the full model. Across all evaluations, the full model demonstrated consistently greater discriminative ability for survival prediction than both the intermediate model and the baseline model based solely on pathological stage. Conclusions: Preoperative MCV independently predicts OS in patients with resected stage I–IIIA NSCLC. Integration of MCV with the NUn score into a composite index provides incremental prognostic value beyond anatomical staging and established clinical factors, supporting its use as a complementary tool for postoperative risk stratification. Full article

The study aimed to determine whether placental lesions differ according to the duration of dystocia. Forty-seven placentas were obtained from 18 bitches that underwent emergency cesarean sections. For descriptive purposes, the cases were classified into four groups based on the duration of dystocia: Group A, up to 6 h; Group B, 6–11.9 h; Group C, 12–24 h; and Group D, more than 24 h. Forty-seven placentas were studied. Both macroscopic and microscopic characteristics were evaluated in each placenta. Descriptive data were presented, and logistic and multinomial regression models were used to assess whether dystocia duration (in hours) is associated with the presence and severity of placental macro- and microscopic lesions. An hour increment over the mean in the duration of dystocia showed a non-significant trend to increasing the presence of macroscopic necrosis (OR: 1.11, p = 0.09) and mineralization (OR: 1.10, p = 0.06), and it also increased the severity of macroscopic congestion (OR: 1.44; p = 0.01) and showed a non-significant trend to increasing the severity of polymorphonuclear neutrophil infiltrate (OR: 1.18; p = 0.06). These findings highlight the importance of early obstetric intervention in all cases of dystocia to minimize fetal hypoxia and improve neonatal outcomes. Moreover, the placenta could serve as a biomarker for fetal distress, as the presence of severe lesions indicates an increased risk for reduced neonatal survival. Full article

Systemic risk propagation in modern financial markets is characterized by non-linear contagion and rapid topological evolution, rendering traditional static monitoring methods ineffective. Existing Graph Neural Networks (GNNs) often struggle to capture “structural breaks” during crises due to their reliance on static adjacency assumptions and isotropic aggregation. To address these challenges, this study proposes the Temporal Attentive Graph Networks (TAGN), a dynamic framework designed for extreme volatility prediction and financial surveillance. TAGN constructs an incremental multi-scale graph by fusing high-frequency trading data, supply chain linkages, and institutional co-holdings to model heterogeneous risk transmission channels. Technically, it employs a deeply coupled GAT-GRU architecture, where the Graph Attention Network (GAT) dynamically assigns weights to contagion sources, and the Gated Recurrent Unit (GRU) memorizes the trajectory of structural evolution. Extensive experiments on the S&P 500 dataset (2018–2024) demonstrate that TAGN significantly outperforms state-of-the-art baselines, including WinGNN and PatchTST, achieving an AUC of 0.890 and a Precision at 50 of 61.5%. Notably, a risk early-warning index derived from TAGN exhibits a 1–2 week lead time over the VIX index during major market stress events, such as the Silicon Valley Bank collapse. This research facilitates a paradigm shift from historical statistical estimation to dynamic network-aware sensing, offering interpretable tools for RegTech applications. Full article

This paper addresses the question of how many autonomous aerial vehicles (UAVs or drones) can safely operate within a bounded three-dimensional airspace. First, we derive the absolute mathematical limits on drone density using geometric arguments from sphere packing and covering theory. Then, we verify these limits empirically by simulating a swarm controlled via model predictive control. We incrementally increase the number of drones until motion becomes impossible. Each drone is modeled as a double-integrator system with a bounded speed and acceleration and is surrounded by a radius spherical safety zone $r>0$. The drones are controlled via model predictive control with hard separation constraints. We formalize complete blockage as the loss of any feasible non-trivial trajectory set, either due to geometric crowding or dynamic limitations. Using tools from discrete geometry, we establish absolute upper bounds on a safe population via sphere-packing results and sufficient conditions for total immobilization via sphere-covering arguments. We extend these static bounds by incorporating dynamics through stopping-distance analysis, leading to an inflated exclusion radius that captures the effect of finite control authority. In addition, we prove min-cut style flow-capacity bounds that limit feasible throughput across bottlenecks and derive horizon-dependent conflict-graph conditions that capture MPC infeasibility at high densities. These results provide a rigorous theoretical framework for determining the transition from feasible multi-drone operation to inevitable gridlock, offering explicit quantitative thresholds that can inform airspace design, drone density regulation, and the tuning of predictive controllers. We evaluate our theoretical findings with a simulation environment. Full article

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

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