Search Results (13,973)

Receiver operating characteristic (ROC) surfaces provide a natural extension of ROC curves to three-class diagnostic problems. A key summary index is the volume under the surface (VUS), representing the probability that a randomly chosen observation from each of the three ordered groups is correctly classified. A parametric estimation of VUS typically assumes trinormality of the class distributions. However, a formal method for the verification of this composite assumption has not appeared in the literature. Our approach generalizes the two-class AUC-based GOF test of Zou et al. to the three-class setting by exploiting the parallel structure between empirical and trinormal VUS estimators. We propose a global goodness-of-fit (GOF) test for trinormal ROC models based on the difference between empirical and trinormal parametric estimates of the VUS. To improve stability, a probit transformation is applied and a bootstrap procedure is used to estimate the variance of the difference. The resulting test provides a formal diagnostic for assessing the adequacy of trinormal ROC modeling. Simulation studies illustrate the robustness of the assumption via the empirical size and power of the test under various distributional settings, including skewed and multimodal alternatives. The method’s application to COVID-19 antibody level data demonstrates the practical utility of it. Our findings suggest that the proposed GOF test is simple to implement, computationally feasible for moderate sample sizes, and a useful complement to existing ROC surface methodology. Full article

The use of probability distribution functions in deriving synthetic hydrographs has become a robust method for modeling the response of watersheds to precipitation events. This approach leverages statistical distributions to capture the temporal structure of runoff processes, providing a flexible framework for estimating peak discharge, time to peak, and hydrograph shape. The present study explores the application of various probability distributions in constructing synthetic hydrographs. The research evaluates parameter estimation techniques, analyzing their influence on hydrograph accuracy. The results highlight the strengths and limitations of each distribution in capturing key hydrological characteristics, offering insights into the suitability of certain probability distribution functions under varying watershed conditions. The study concludes that the approach based on the Cadariu rational function enhances the adaptability and precision of synthetic hydrograph models, thereby supporting flood forecasting and watershed management. Full article

This study examines cost and schedule deviations in secondary road infrastructure projects in Colombia, with a focus on the influence of public procurement characteristics. Despite the construction sector’s importance to national development, limited research has explored how procurement-related variables affect project performance. To address this gap, 149 completed road projects were analyzed using data from Colombia’s open procurement database, which provides publicly accessible, standardized information on contracting processes. A four-stage methodology was applied: data collection, exploratory analysis, bivariate analysis (including correlation and Kruskal–Wallis tests), and multivariate analysis using Random Forest and Bayesian networks. Schedule and cost deviations were used as dependent variables, with 17 independent variables. Results show that 81.9% of projects experienced some form of deviation, with a positive correlation between schedule and cost overruns. Significant factors were identified across different stages of the project life cycle. Variables significant for both deviations include the number of bidders, the number of valid bidders, the estimated cost, the final cost, the project intensity, and the type of award process. The findings provide data-driven arguments to improve award processes and support more informed planning of future projects, helping public entities reduce deviations and enhance the outcome of their infrastructure. Full article

Part-time employment is an increasingly important feature of the U.S. labor market, yet little is known about how earnings determinants differ between full-time and part-time pharmacists. Few prior studies have compared earnings models across these groups, but most have relied on small or geographically limited samples. Moreover, the dynamic and rapidly evolving nature of the labor market makes this study especially timely, as most prior research on pharmacist earnings is based on older data. This study examined earnings determination separately for full-time and part-time pharmacists, estimating the influence of work input, human capital, demographic characteristics, and job-related features within each group. Data were obtained from the 2019–2022 American Community Survey (ACS), a large, continuous, nationally representative survey conducted annually by the U.S. Census Bureau. The sample included 12,064 pharmacists (4667 men and 7397 women) aged 25–64 years, practicing in the U.S. Ordinary least-squares equations were estimated separately for male and female pharmacists within each employment category, allowing comparison of the direction, magnitude, and statistical significance of covariates across groups. Results revealed notable differences in the earnings effects of several factors between full-time and part-time pharmacists, highlighting the interaction of individual choices and structural market forces in shaping compensation. These findings can inform workforce planning and guide the development of targeted job-related incentives to support retention and satisfaction across employment types. Full article

Soil organic carbon (SOC) is a critical indicator of soil health, directly influencing crop productivity, soil structure, and environmental sustainability. Existing SOC estimation techniques using satellite reflectance data are effective for large-scale applications; however, their accuracy is reduced due to various types of noisy samples caused by vegetation interference, sensor-related anomalies, atmospheric effects, and other spectral distortions. This study proposes a robust data refinement framework capable of handling any soil sample, whether clean or noisy, by identifying and correcting noisy samples to enable more accurate SOC estimation outcomes. The approach first explores complex global relationships among spectral bands to understand and represent subtle patterns in soil reflectance using the Transformer network. To remove redundancy and retain only essential information of the transformed features, we apply a dimensional reduction technique for efficient analysis. Building upon this refined representation, noisy samples are detected without relying on strict data distribution assumptions, ensuring effective identification of noisy samples in diverse conditions. Finally, instead of excluding these noisy samples, the proposed framework corrects their reflectance through a conditional Generative Adversarial Network (cGAN) to align with expected soil spectral characteristics, thereby preserving valuable information for more accurate SOC estimation. The proposed approach was evaluated on benchmark satellite datasets, demonstrating superior performance over existing noise correction techniques. Experimental validation using the Landsat 8 dataset demonstrated that the proposed framework improved SOC estimation performance by increasing $R^2$ by 1.52%, reducing RMSE by 4.45%, and increasing RPD by 5.14% compared to the best baseline method (OC-SVM + Kriging). These results confirm the framework’s effectiveness in enhancing SOC estimation under noisy conditions. This scalable framework supports accurate SOC monitoring across diverse conditions, enabling informed soil management and advancing precision agriculture. Full article

In response to the growing safety challenges posed by large-scale and specialized freight transportation on China’s rapidly expanding highway network, this study investigates the operational characteristics of trucks in interchange diverging areas—a critical segment with elevated accident risks. Leveraging high-frequency trajectory data collected from 16 interchanges, we analyze speed profiles and acceleration behavior of heavy trucks across key sections: the diversion influence zone, preparation zone, transition segment, and deceleration lane. A key contribution of this work is the development of a continuous speed prediction model based on Partial Least Squares Regression, which integrates road geometric parameters and driving behavior features to estimate speeds at four critical cross-sections of the diverging process. Furthermore, we propose a comprehensive safety evaluation framework incorporating three novel indicators: longitudinal speed consistency, lateral stability, and deceleration comfort. The model demonstrates strong performance, with all mean absolute percentage errors below 10% during validation using data from four independent interchanges. Comparative analysis with existing safety standards confirms the practical applicability and accuracy of the proposed methodology. This research offers three major contributions: (1) a systematic approach for processing large-scale trajectory data and predicting truck speeds in diverging areas; (2) a safety assessment framework tailored for geometric design consistency evaluation; and (3) empirical support for optimizing traffic safety facilities in interchange design and operation. The findings address a significant gap in current highway design guidelines and provide actionable insights for enhancing safety in truck-dominated transportation environments. Full article

Background: Massive intraoperative blood loss (IBL) is a serious complication in complex spine surgeries such as deformity correction, multilevel fusion, tumor resection, and revision procedures. While no strict definition exists, blood loss exceeding 1500 mL or 20% of estimated blood volume is generally considered clinically significant. Excessive bleeding increases the risk of hemodynamic instability, transfusion-related complications, postoperative infection, and prolonged hospitalization. Methods: This narrative review summarizes the current understanding of the incidence, risk factors, anatomical vulnerabilities, and evidence-based strategies for managing IBL in spine surgery through comprehensive literature analysis of recent studies and clinical guidelines. Results: Key risk factors include patient characteristics (anemia, obesity, advanced age, medication use), surgical variables (multilevel instrumentation, revision status, operative time), and pathological conditions (hypervascular tumors, severe deformity). Perioperative medication management is critical, requiring discontinuation of NSAIDs (5–7 days), antiplatelet agents (5–7 days), and NOACs (48–72 h) preoperatively to minimize bleeding risk. The thoracolumbar junction and hypervascular spinal lesions are especially prone to bleeding due to dense vascular anatomy. Evidence-based management strategies include comprehensive preoperative optimization, intraoperative hemostatic techniques, antifibrinolytic agents, topical hemostatic products, cell salvage technology, and structured transfusion protocols. Conclusions: Effective management of massive IBL requires a multimodal approach combining preoperative risk assessment and medication optimization, intraoperative hemostatic strategies including tranexamic acid administration, advanced monitoring techniques, and coordinated transfusion protocols. Particular attention to perioperative management of anticoagulant and antiplatelet medications is essential for bleeding risk mitigation. Understanding patient-specific risk factors, surgical complexity, and anatomical considerations enables surgeons to implement targeted prevention and management strategies, ultimately improving patient outcomes and reducing complications in high-risk spine surgery procedures. Full article

This study presents an amplitude-based non-destructive testing (NDT) approach for estimating reinforcement bar diameter in reinforced concrete members using ground-penetrating radar (GPR). The novelty of this work lies in the use of normalized amplitude-diameter-depth (NADD) relationships, which link the reflected electromagnetic wave amplitude to both rebar diameter and cover depth through an exponential attenuation model. Normalization was applied to remove the influence of varying signal energy and antenna coupling, thereby allowing consistent comparison of amplitudes across different depths and improving the reliability of amplitude-depth interpretation. The NADD equation was developed from GPR measurements obtained on a reinforced concrete slab containing bars with diameters ranging from 9.5 mm (#3 bar) to 25.4 mm (#8 bar) and then validated using data from three prestressed concrete box beams recovered from a decommissioned bridge managed by the West Virginia Department of Highways. The normalized amplitude prediction error (E_a) was calculated to quantify model performance. The minimum mean error of approximately 4.7% corresponded to the 12.7 mm (#4 bar), which matched the actual reinforcement used in the beams. The results demonstrate that the proposed normalization-based approach effectively captures the amplitude-depth-diameter relationship, offering a quantitative framework for interpreting GPR data and improving the evaluation of reinforcement characteristics in existing concrete structures. Full article

The state of health (SOH) of lithium-ion batteries (LIBs) is a key parameter that is crucial for delaying their lifespan degradation and ensuring safe use. To further explore the potential of charge curves in SOH estimation for LIBs, this paper proposes a method based on multi-dimensional health features and a genetic algorithm–particle swarm optimization (GAPSO)–bidirectional gated recurrent unit (BiGRU) neural network for SOH estimation. First, we extracted differential thermal voltammetry curves from the charging curve and defined the peak, valley, and their positions. Then, based on the charging temperature curve, we defined the time at which the maximum charging temperature occurs and the average charging temperature. Subsequently, we validated the correlation between the aforementioned six health features and SOH using the Pearson correlation coefficient. Finally, we used the multi-dimensional health features as model inputs to construct the BiGRU estimation model and employed the GAPSO hybrid strategy to achieve global adaptive optimization of the model’s hyperparameters. Experimental results on different LIBs show that the proposed method has relatively high accuracy, with an average absolute error and root mean square error of no more than 0.2771%. The comparison results with various methods further verify the superiority of the proposed method. Full article

Unmanned aerial vehicle (UAV)-assisted wireless communication systems often employ the carrier aggregation (CA) technique to alleviate the issue of insufficient bandwidth. However, in high-mobility UAV communication scenarios, the dynamic channel characteristics pose significant challenges to channel estimation (CE). Given these challenges, this paper proposes a line-of-sight (LoS) and echo sensing-based CE scheme for CA-enabled UAV-assisted communication systems. Firstly, LoS sensing and echo sensing are employed to obtain sensing-assisted prior information, which refines the CE for the primary component carrier (PCC). Subsequently, the path-sharing property between the PCC and secondary component carriers (SCCs) is exploited to reconstruct SCC channels in the delay-Doppler (DD) domain through a three-stage process. The simulation results demonstrate that the proposed method effectively enhances the CE accuracy for both the PCC and SCCs. Furthermore, the proposed scheme exhibits robustness against parameter variations. Full article

Accurate modeling of industrial and biomedical data is often challenging due to skewness, heavy tails, and complex variability, which traditional probability distributions fail to capture. To address this, we propose the Induced Ailamujia Lifetime Distribution (IALD), a flexible generalization of the Ailamujia distribution developed via an induced transformation. The IALD accommodates diverse dataset characteristics through a wide range of probability density and hazard rate shapes. Several key statistical properties are derived, including moments, reliability measures, quantile and generating functions, probability weighted moments, and entropy measures. Model parameters are estimated using six classical methods, with their performance assessed through simulation. The practical utility of the IALD is demonstrated using two real datasets from biomedical and industrial fields, where it consistently outperforms existing lifetime models. These results confirm the IALD as a powerful and promising tool for reliability, engineering, and biomedical data analysis. Full article

Recent issues that have emerged in regard to raccoon dog (Nyctereutes procyonoides) include interaction with humans and disease transmission. Therefore, understanding their habitat characteristics and preferences is crucial in the effort to limit conflicts with humans. A total of thirteen raccoon dogs were captured from three regions in South Korea, each with distinct habitat characteristics. GPS trackers were attached for tracking the raccoon dogs’ movements. Utilizing GPS tracking data, Kernel Density Estimation (KDE), Minimum Convex Polygon (MCP), and Jacobs Index were applied to learn more about the habitat preferences of the raccoon dogs. According to the results, the habitat composition ratios for KDE and MCP showed that forests had the largest proportion. However, a habitat composition ratio similar to the land proportion of the area that they inhabit indicated that raccoon dogs had the ability to adapt to various habitats. Jacobs Index analysis revealed different habitat selection patterns compared to KDE and MCP, with forests showing neutral to negative selection despite comprising large proportions of home ranges. Our results highlight important methodological considerations when inferring habitat preferences from spatial data, suggesting that multiple analytical approaches provide complementary insights into animal space use. Full article

Background/Objectives: Despite their low incidence, popliteal artery aneurysms (PAAs) are the most common aneurysms of the peripheral arteries and carry a high risk of limb loss. The optimal treatment, either with open (OR) or endovascular repair (ER), remains debated due to the lack of high-level evidence. Methods: In accordance with STROBE guidelines, we conducted a retrospective observational study with a prospective follow-up. All patients presenting with a PAA who underwent elective OR in the Vascular and Endovascular Surgery Unit of Agostino Gemelli Hospital (Rome, Italy) over the last four years were enrollved. Urgent and endovascular cases were excluded. Clinical examination, Doppler ultrasound (DUS), and contrast-enhanced computed tomography angiography (CTA) were performed preoperatively. Clinical and DUS follow-up was performed at 1, 3, 6, and 12 months postoperatively and annually thereafter. Endpoints were the primary, primary assisted, and secondary patency calculated using a Kaplan–Maier estimate based on the “first event” (arterial stenosis, occlusion, or reintervention) after the procedure. Amputation rate and overall mortality were also assessed. The results were compared with the success and complication rates reported in contemporary endovascular series. Results: Overall, 62 open interventions for popliteal artery aneurysms were performed during the study period; 49 patients (100% male, 70.3 SD ± 8.8 years) were included for a total of 52 PAAs treated electively (median diameter 30.5 mm, range 20–75; 92.3% fusiform). Aneurysm involved P1 segment in 38.5% of cases (20), P2 in 48.1% of cases (25), and P3 in 13.5% of cases (7). Two runoff vessels were present in most patients (37, 71.2%). Surgery consisted of the aneurysm’s exclusion through ligation and autologous vein or prosthetic bypass (25, 48.1%) or aneurysmectomy and interposition graft or end-to-end anastomosis (27, 51.9%). At a mean follow-up of 18 months (SD ± 17.7), the primary, the primary assisted, and the secondary patency were 94.3%, 100%, and 100% respectively. No minor nor major amputations and no deaths were reported. Conclusions: In the endovascular era, our results highlight that regardless the specific characteristics—including age, comorbidities, and aneurysm anatomy—OR provides excellent early and mid-term outcomes with high patency and low complication rate compared with contemporary endovascular series reported in the literature. Full article

In tightly coupled Industrial Control Systems (ICS), abnormal disturbances often propagate throughout the process, triggering a large number of time-correlated alarms that exceed the handling capacity of the operator. Consequently, a key challenge is how to leverage the directional and temporal characteristics of disturbance propagation to alleviate alarm overload. This paper proposes a delay-sensitive causal inference approach for industrial alarm analysis to address this problem. On the one hand, time delay estimation is introduced to precisely align the responses of two sensor sequences to disturbances, thereby improving the accuracy of causal relationship identification in the temporal domain. On the other hand, a multi-scale subgraph fusion strategy is designed to address the inconsistency in causal strength caused by disturbances of varying intensities. By integrating significant causal subgraphs from multiple scenarios into a unified graph, the method reveals the overall causal structure among alarm variables and provides guidance for alarm mitigation. To validate the proposed method, a case study is conducted on the Tennessee Eastman Process. The results demonstrate that the approach identifies causal relationships more accurately and reasonably and can effectively reduce the number of alarms by up to 51.6%. Full article

Background: Small cell lung cancer (SCLC) is an aggressive type of cancer known for its rapid progression and poor prognosis. While several chemotherapeutic agents, including topotecan, are approved for use in the second-line treatment setting, their clinical benefits have been modest and often limited by toxicity. As a result, there is a significant need for more effective treatment strategies. Given the high rate of brain metastases in patients with SCLC and temozolomide’s (TMZ) ability to penetrate the central nervous system, combining TMZ with irinotecan (IRI) presents a potentially effective therapeutic approach. This study aimed to evaluate the clinical outcomes of the TMZ and IRI combination compared to other second-line treatment regimens in a real-world patient population. Methods: We conducted a retrospective review of the medical records of 37 patients with relapsed SCLC who underwent second-line therapy at a tertiary oncology center from January 2018 to December 2023. Among these patients, 24 were treated with a combination of TMZ+IRI, while 13 received alternative regimens, which included topotecan, irinotecan, paclitaxel, docetaxel, vinorelbine, or gemcitabine. We collected baseline demographic and clinical data and assessed survival outcomes. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method, and prognostic factors were analyzed using Cox regression models. Results: A total of 37 patients were included (mean age 59.7 years, 86.5% male). Baseline characteristics were similar between groups, except for body mass index, which was higher in the TMZ+IRI group (27.9 vs. 24.6, p = 0.033). Median OS was significantly longer in patients treated with TMZ+IRI compared to controls (25 vs. 8 months, p = 0.002). One-year OS rates were 58.2% and 25.4%, respectively. In multivariate analysis, brain metastases (HR 0.37, 95% CI 0.14–0.95, p = 0.039) and receipt of non-TMZ+IRI regimens (HR 2.82, 95% CI 1.03–7.72, p = 0.044) were independent predictors of poor OS. Median PFS did not differ significantly between groups (8 vs. 7 months, p = 0.733), and no independent predictors of PFS were identified. Conclusions: The combination of temozolomide and irinotecan was associated with a significant overall survival benefit compared with other second-line regimens in relapsed SCLC, despite similar progression-free survival. These findings suggest that TMZ+IRI may provide a clinically meaningful option for appropriately selected patients, particularly those with preserved performance status. Prospective randomized studies are warranted to confirm these results and better define the role of this regimen in treatment sequencing. Full article