Search Results (8,782)

Controlling dissolved nitrogen is critical to meeting increasingly stringent steel quality targets, yet the variable kinetics of gas absorption and removal across production stages complicate real-time decision-making. Leveraging a total of 291 metal samples, the research applied ordinary least squares (OLS) regression, enhanced by cointegration diagnostics, to develop four stage-specific models covering pig iron after desulfurization, crude steel in the basic oxygen furnace (BOF) before tapping, steel at the beginning and end of secondary metallurgy processing. Predictor selection combined thermodynamic reasoning and correlation analysis to produce prediction equations that passed heteroscedasticity, normality, autocorrelation, collinearity, and graphical residual distribution tests. The k-fold cross-validation method was also used to evaluate models’ performance. The models achieved an adequate accuracy of 77.23–83.46% for their respective stages. These findings demonstrate that statistically robust and physically interpretable regressions can capture the complex interplay between kinetics and the various processes that govern nitrogen pick-up and removal. All data are from U. S. Steel Košice, Slovakia; thus, the models capture specific setup, raw materials, and production practices. After adaptation within the knowledge transfer, implementing these models in process control systems could enable proactive parameter optimization and reduce laboratory delays, ultimately minimizing excessive nitrogenation in finished steel. Full article

Background: Delayed diagnosis of Chronic Obstructive Pulmonary Disease (COPD) in primary care is common and contributes to preventable morbidity. A deeper understanding of pre-diagnostic patterns is needed to develop targeted detection strategies. We aimed to characterize diagnostic delay and missed diagnostic opportunities (MDOs) and identify high-risk clinical profiles. Methods: We conducted a retrospective cohort study of 167 patients newly diagnosed with COPD in primary care centers in Madrid, Spain. Healthcare utilization in the 12 months preceding diagnosis was analyzed. Multivariable logistic regression was used to identify predictors of MDOs, and K-means clustering was used to identify patient phenotypes. Results: Diagnostic delay (>30 days) was present in 45.5% of patients, and MDOs in 47.3%. MDO-positive patients had significantly worse lung function (mean FEV₁: 1577 vs. 1898 mL, p = 0.008), greater symptom burden (CAT score ≥ 10: 79.7% vs. 59.1%, p = 0.003), and more frequent pre-diagnostic exacerbations (mean: 1.24 vs. 0.71, p = 0.032). After multivariable adjustment, diagnostic delay remained a powerful independent predictor of MDOs (OR 10.25, 95% CI 4.39–24.88; p < 0.001). Cluster analysis identified three distinct clinical phenotypes: ‘Paucisymptomatic–Preserved’, ‘Frequent Attenders/High-Risk’, and ‘Silent Decliners’. Conclusions: The pre-diagnostic period in COPD is a dynamic window of detectable, and potentially preventable, clinical deterioration driven by diagnostic inertia. The identification of distinct patient phenotypes suggests that a proactive, stratified, and personalized approach, rather than a one-size-fits-all strategy, is required to improve early diagnosis in primary care. Full article

To improve the traffic efficiency of connected and automated vehicles (CAVs) in on-ramp merging areas, this study proposes a novel region-level multi-agent reinforcement learning framework, Regionally Coordinated Nash-Advantage Decomposition Deep Q-Network with Conflict-Aware Q Fusion (RC-NashAD-DQN). Unlike existing vehicle-level control methods, which suffer from high computational overhead and poor scalability, our approach abstracts on-ramp and main road areas as region-level control agents, achieving coordinated yet independent decision-making while maintaining control precision and merging efficiency comparable to fine-grained vehicle-level approaches. Each agent adopts a value–advantage decomposition architecture to enhance policy stability and distinguish action values, while sharing state–action information to improve inter-agent awareness. A Nash equilibrium solver is applied to derive joint strategies, and a conflict-aware Q-fusion mechanism is introduced as a regularization term rather than a direct action-selection tool, enabling the system to resolve local conflicts—particularly at region boundaries—without compromising global coordination. This design reduces training complexity, accelerates convergence, and improves robustness against communication imperfections. The framework is evaluated using the SUMO simulator at the Taishan Road interchange on the S1 Yongtaiwen Expressway under heterogeneous traffic conditions involving both passenger cars and container trucks, and is compared with baseline models including C-DRL-VSL and MADDPG. Extensive simulations demonstrate that RC-NashAD-DQN significantly improves average traffic speed by 17.07% and reduces average delay by 12.68 s, outperforming all baselines in efficiency metrics while maintaining robust convergence performance. These improvements enhance cooperation and merging efficiency among vehicles, contributing to sustainable urban mobility and the advancement of intelligent transportation systems. Full article

Background and Aims: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) delay gastric emptying, raising concerns about periprocedural safety in elective endoscopy. We aimed to evaluate the association between pre-procedural GLP-1 RA use and post-procedural complications such as aspiration pneumonia. Methods: In this retrospective cohort study, adults (18–89 years) undergoing outpatient esophagogastroduodenoscopy or colonoscopy within the HCA Healthcare network from 1 July 2021 to 31 March 2024 were identified. Patients were classified as GLP-1 RA users (n = 953) or non-users (n = 3289) based on home medication records. Primary outcomes included aspiration, post-procedural oxygen requirement, hypotension, hospitalization, ICU admission, length of stay, and all-cause inpatient mortality. Multivariable logistic and negative-binomial regression models, incorporating an interaction term for anesthesia type, were adjusted for age, sex, body mass index, ASA class, and key comorbidities. Results: No aspiration events were reported in either group. GLP-1 RA use was associated with lower odds of post-procedural oxygen requirement (OR 0.43, 95% CI 0.25–0.76), hospitalization (OR 0.73, 95% CI 0.39–1.36), and mortality (0.1 vs. 0.9%, p = 0.014), and a shorter hospital stay (IRR 0.54, 95% CI 0.40–0.71). Rates of hypotension and ICU admission were similar between both groups. In anesthesia-stratified analysis among GLP-1 RA users, those receiving MAC/MS had higher odds of hospitalization compared with GA (OR 1.87, 95% CI 1.23–2.85, p = 0.003), whereas other outcomes were not significant. Conclusions: Pre-procedural GLP-1 RA therapy was not associated with increased peri-procedural complications. Although hospitalization was more frequent with MAC/MS, this difference did not extend to other clinically significant outcomes. Further prospective studies are needed to clarify the clinical implications of anesthesia choice. Full article

During diabetic retinopathy (DR), cell death has been characterized in all of the major retinal cell types, but was observed initially in the microvasculature, particularly the mural cells: pericytes and vascular smooth muscle cells (VSMCs). Indeed, our ability to identify the mural cell corpses called “ghost cells” within the vascular basement membranes (BMs) in eyes of diabetic patients and animal models is indicative that removal of dead cells, or efferocytosis (EF), is dysfunctional during this disease. EF is the process whereby apoptotic cells are eliminated through phagocytic engulfment and digestion and is essential to maintain tissue integrity and immune homeostasis. The process occurs in three distinct phases: finding and recognition, engulfment, and digestion, under the direction of “find me” and “eat me” signals and a large array of their cognate receptors and bridging molecules. Efferocytosis can be performed by many cell types, but most efficiently by professional phagocytes, and with such rapidity that the process is extremely difficult to detect in healthy tissues. As delayed EF is a recognized cause of autoimmune and inflammatory disease, mural cell death in DR may create inflammatory foci in the neurovascular unit (NVU). Here we discuss the basic mechanisms of EF in the context of DR and the impact of diabetic metainflammation on EF effector cell dysfunction. Full article

This study investigated the aerodynamic structures generated by transverse jet injection in supersonic flows around high-speed vehicles. The unsteady evolution of these structures was analyzed using an improved delayed detached Eddy simulation (IDDES) approach based on the Reynolds stress model (RSM). The simulations successfully reproduced experimentally observed shock systems and vortical structures. The time-averaged flow characteristics were compared with the experimental results, and good agreement was observed. The flow characteristics were analyzed, with particular emphasis on the formation of counter-rotating vortex pairs in the downstream region, as well as complex near-field phenomena, such as flow separation and shock wave/boundary layer interactions. Time-resolved spectral analysis at multiple monitoring locations revealed the presence of a global oscillation within the flow dynamics. Within these regions, pressure fluctuations in the recirculation zone lead to periodic oscillations of the upstream bow shock. This dynamic interaction modulates the instability of the windward shear layer and generates large-scale vortex structures. As these shed vortices convect downstream, they interact with the barrel shock, triggering significant oscillatory motion. To further characterize this behavior, dynamic mode decomposition (DMD) was applied to the pressure fluctuations. The analysis confirmed the presence of a coherent global oscillation mode, which was found to simultaneously govern the periodic motions of both the upstream bow shock and the barrel shock. Full article

Regenerative chatter is an unfavorable phenomenon that severely affects machining efficiency and surface finish in milling operations. The prediction of chatter stability is an important way to obtain the stable cutting zone. Based on implicit multistep schemes, this paper presents the third-order and fourth-order implicit exponentially fitted methods (3rd IEM and 4th IEM) for milling stability prediction. To begin with, the delay differential equations (DDEs) with time-periodic coefficients are employed to describe the milling dynamics models, and the principal period of the coefficient matrix is firstly decomposed into two different subintervals according to the cutting state. Subsequently, the fourth-step and fifth-step implicit exponential fitting schemes are applied to more accurately estimate the state term. Two benchmark milling models are utilized to illustrate the effectiveness and advantages of the high-order implicit exponentially fitted methods by making comparisons with the three typical existing methods. Under different radial immersion conditions, the numerical results demonstrate that the 3rd IEM and the 4th IEM exhibit both faster convergence rates and higher prediction accuracy than the other three existing prediction methods, without much loss of computational efficiency. Finally, in order to verify the feasibility of the 3rd IEM and the 4th IEM, a series of experimental verifications are conducted using a computer numerical control machining center. It is clearly visible that the stability boundaries predicted by the 3rd IEM and the 4th IEM are mostly consistent with the cutting test results, which indicates that the proposed high-order exponentially fitted methods achieve significantly better prediction performance for actual milling processes. Full article

A novel skin test for an in vivo assessment of SARS-CoV-2-specific T-cell immunity was developed using CoronaDermPS, a multiepitope recombinant polypeptide encompassing MHC II–binding CD4+ T-cell epitopes of the SARS-CoV-2 structural proteins (S, E, M) and full length nucleocapsid (N). In silico epitope prediction and modeling guided antigen design, which was expressed in Escherichia coli, was purified (>95% purity) and formulated for intradermal administration. Preclinical evaluation in guinea pigs, mice, and rhesus macaques demonstrated a robust delayed type hypersensitivity (DTH) response at optimal doses (10–75 µg), with no acute or chronic toxicity, mutagenicity, or adverse effects on reproductive organs. An integrated clinical analysis included 374 volunteers stratified by vaccination status (EpiVacCorona, Gam-COVID-Vac, CoviVac) prior to COVID-19 infection (Wuhan/Alpha, Delta, Omicron variants), and SARS-CoV-2–naïve controls. Safety assessments across phase I–II trials recorded 477 adverse events, of which >88% were mild and self-limiting; no severe or anaphylactic reactions occurred. DTH responses were measured at 24 h, 72 h, and 144 h post-injection by papule and hyperemia measurements. Overall, 282/374 participants (75.4%) exhibited a positive skin test. Receiver operating characteristic analysis yielded an overall AUC of 0.825 (95% CI: 0.726–0.924), sensitivity 79.5% (95% CI: 75.1–83.3%), and specificity 85.5% (95% CI: 81.8–88.7%), with comparable diagnostic accuracy across vaccine, and variant subgroups (AUC range 0.782–0.870). CoronaDerm-PS–based skin testing offers a simple, reproducible, and low-cost method for qualitative evaluation of T-cell–mediated immunity to SARS-CoV-2, independent of specialized laboratory equipment (Eurasian Patent No. 047119). Its high safety profile and consistent performance across diverse cohorts support its utility for mass screening and monitoring of cellular immune protection following infection or vaccination. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article

Groundwater resource management involves complex socio-hydrological systems characterized by dynamic feedback, uncertainty, and common misconceptions among decision-makers. While deterministic models like MODFLOW simulate physical hydrology effectively, they fall short in capturing the social, legal, and behavioral dynamics shaping groundwater use. System dynamics (SD) modeling offers a robust alternative by incorporating feedback loops, delays, and nonlinearities. Yet, model conceptualization remains one of the most challenging steps in SD practice. Experienced modelers often apply a “Lego-like” approach—assembling new models from pre-validated sub-models. However, this strategy depends on access to well-documented sub-model libraries, which are typically unavailable to newcomers. To address this barrier, we systematically review and classify socio-economic sub-models from existing groundwater SD literature, organizing them by system archetypes and generic structures. The resulting modular library offers a practical resource that supports newcomers in building structured, scalable models. This approach improves conceptual clarity, enhances model reusability, and facilitates faster development of SD models tailored to groundwater systems. The study concludes by identifying directions for future research, including expanding the sub-model library, clarifying criteria for base-model selection, improving integration methods, and applying these approaches through diverse case studies to further strengthen the robustness and utility of groundwater SD modeling. Full article

In dense urban environments and large-scale events, Internet infrastructure often becomes overloaded due to high communication demand. Many of these communications are local and short-lived, exchanged between users in close proximity but still relying on global infrastructure, leading to unnecessary network stress. In this context, delay-tolerant networks (DTNs) offer an alternative by enabling device-to-device (D2D) communication without requiring constant connectivity. However, DTNs face significant challenges in routing due to unpredictable node mobility and intermittent contacts, making reliable delivery difficult. Considering these challenges, this paper presents a hybrid Beyond 5G (B5G) DTN architecture to provide private context-aware routing in dense scenarios. In this proposal, dynamic contextual notifications are shared among relevant local nodes, combining federated learning (FL) and edge artificial intelligence (AI) to estimate the optimal relay paths based on variables such as mobility patterns and contact history. To keep the local FL models updated with the evolving context, edge nodes, integrated as part of the B5G architecture, act as coordinating entities for model aggregation and redistribution. The proposed architecture has been implemented and evaluated in simulation testbeds, studying its performance and sensibility to the node density in a realistic scenario. In high-density scenarios, the architecture outperforms state-of-the-art routing schemes, achieving an average delivery probability of 77%, with limited latency and overhead, demonstrating relevant technical viability. Full article

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments. Full article

The purpose of this paper is to study the shear performance of reinforced concrete corbels under a synergistic change in the main stirrup inclination angle to explore the synergistic mechanism of the main reinforcement and the stirrup inclination angle, and to evaluate the applicability of existing design specifications. The shear performance test was carried out by designing RC corbel specimens with an inclination angle of the main reinforcement and stirrup. The test results show that a 15° inclination scheme significantly improves the shear performance: the yield load is increased by 28.3%, the ultimate load is increased by 23.6%, the strain of the main reinforcement of the 15° specimen is reduced by 51.3%, the stirrup shows a delayed yield (the yield load is increased by 11.6%) and lower strain level (250 kN is reduced by 23.7%), and the oblique reinforcement optimizes the internal force transfer path and delays the reinforcement yield. A CDP finite element model was established for verification, and the failure mode and crack propagation process of the corbel were accurately reproduced. The prediction error of ultimate load was less than 2.27%. Based on the test data, the existing standard method is tested and a modified formula of the triangular truss model based on the horizontal inclination angle of the tie rod is proposed. The prediction ratio of the bearing capacity is highly consistent with the test value. A function correlation model between the inclination angle of the steel bar and the bearing capacity is constructed, which provides a quantitative theoretical tool for the optimal design of RC corbel inclination parameters. Full article

This study assesses the impact of heatwaves on hospital admissions in the Matam region of Senegal by combining climatic indices with machine learning methods. Using daily maximum temperature (TMAX) and heat index (HI), heatwave events were identified from 2017 to 2022. Hospital data from Ourossogui Regional Hospital were analyzed, and three predictive models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs), were compared. A bootstrapping approach with 1000 iterations was used to evaluate model robustness. The findings reveal a significant delayed effect of heatwaves, with increased hospitalizations occurring three to five days after the event. RF outperformed the other models with R² values ranging from 0.51 to 0.72. These findings highlight the need to enhance heatwave monitoring and promote the integration of impact-based climate forecasting into health early warning systems, particularly to protect vulnerable groups such as the elderly, children, and outdoor workers. Full article

Understanding mixing behavior at river confluences is essential for effective watershed management in response to increasing environmental issues such as algal blooms and chemical pollution. This study focused on the confluence of the Nakdong and Geumho Rivers, employing high-resolution field measurements using an ADCP (M9) and YSI EXO sensors. Water temperature (°C) and electrical conductivity (μS/cm) data were collected under three representative conditions, including flow ratios of 0.91, 0.45, and 0.29, as well as 0.05, with a maximum temperature difference of up to 6 °C. Mixing behavior was three-dimensionally analyzed by integrating cross-sectional and longitudinal data, and the accuracy of visualization was evaluated using IDW and Kriging spatial interpolation techniques. The analysis revealed that under low flow ratio conditions, vertical mixing was delayed; the thermal stratification persisted up to approximately 3 km downstream from the confluence (Line 3), and complete mixing was not achieved until about 7 km downstream (Line 5) due to density currents. Quantitative comparison indicated that IDW (R² = 0.901, RMSE = 31.522) outperformed Kriging (R² = 0.79, RMSE = 35.458). This study provides a quantitative criterion for identifying the mixing completion zone, thereby addressing the limitations of previous studies that relied on numerical models or limited field data, and offering practical evidence for water quality monitoring and sustainable river management. Full article