Search Results (15,570)

In order to predict cerebrovascular event occurrences, this work introduces a novel method that combines computational fluid dynamics (CFD), structural analysis, and machine learning (ML). The study presents a multidisciplinary approach to evaluate the risk of carotid atherosclerosis and cerebrovascular event prediction by utilizing both imaging and non-imaging data. The study uses blood-flow simulations and 3D reconstruction techniques to identify important properties of plaque that may indicate cerebrovascular events. The analysis shows high accuracy of the model in predicting these events and is validated on a dataset of 134 asymptomatic individuals with carotid artery disease. The goal of this work is to improve clinical decision-making by providing a tool that blends machine learning algorithms, structural analysis, and CFD. The dataset imbalance was treated with two approaches in order to select the optimal one for the training of the Gradient Boosting Tree (GBT) classifier. The best GBT model yielded a balanced accuracy of 88%, having a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. Full article

Abstract: From a value-added trade perspective, this study utilizes the world input–output tables and the water footprint data of each industry in each country in the Eora database to explore the virtual water resources of 19 countries (the G20 countries excluding the European Union) in 2016. We calculated nine value chain decompositions and the pattern of virtual water flows and then explored the implied virtual water use due to the trade of intermediate goods and final goods, and we also analyzed the unequal trade of virtual water and added value among countries. The results indicate the following. Firstly, in most countries, the largest portion of virtual water is attributed to exports of intermediate inputs that are produced in the source country and fully utilized by the direct import countries, followed by the foreign value-added component of intermediate goods, while the smallest share of virtual water is returned to the country. Secondly, in value-added trade, excluding the rest of the world (ROW), China, France, Italy, Japan, Mexico, South Korea, South Africa, Saudi Arabia, and Germany are net importers in the virtual water trade between G20 countries, and the USA is the largest net exporter of virtual water. Thirdly, intermediate product trade is the dominant form of implied virtual water trade among countries, which leads to a net flow ratio of implied virtual water of about 80% to 90%. Lastly, the Virtual Water Inequality Index shows that thirteen country combinations, including Brazil and Argentina, exhibit significant inequality, and most countries are in a relatively equal state. In addition, the virtual water and added value of the relatively economically developed regions benefit more from the virtual water trade. Therefore, it is crucial for countries to reduce their consumption of virtual water when trading intermediate products to develop high-value-added and low-water-consumption industries and to choose appropriate virtual water trade targets. Full article

Oil temperature plays a crucial role in hydraulic closed-circuit systems (HCS), and conventional thermal equilibrium models and coupled simulation models face challenges in terms of accuracy, efficiency, and cost when calculating oil temperature. This study introduces an innovative HCS oil temperature precise prediction model and oil refilling parameter optimization method. The initial sample space was determined through a Sobol sensitivity analysis and improved Latin hypercube sampling, leading to the development of a combinatorial agent model (CAM) suitable for oil temperature prediction with superior accuracy and stability compared to other methods. Based on CAM, the optimal oil refilling flow rates under various operational conditions are computed. To validate the efficacy of the theoretical analysis, an HCS experiment platform was established. The data indicates that the temperature prediction error range of the CAM model falls between 0.30 °C and 1.05 °C, and optimizing the oil refilling flow rate can effectively enhance system efficiency while ensuring that the oil temperature remains within permissible limits. The research methodology and findings are applicable in engineering practice and can be extended to optimize the design of other hydraulic systems. Full article

Wastewater treatment plants (WWTPs) connected to combined sewer systems must cope with high flows during wet-weather conditions, often leading to bypass and thus pollution of water bodies. Radar rainfall forecasts coupled with a rainfall-runoff model provides flow and volume forecasts that can be used for deciding when to switch from normal to wet-weather operation, which temporarily allows for higher inflow. However, forecasts are by definition uncertain and may lead to potential mismanagement, e.g., false alarms and misses. Our study focused on two years of operational data from the Damhuså sewer catchment and WWTP. We used the Relative Economic Value (REV) framework to optimize the control parameters of a baseline control strategy (thresholds on flow measurements and radar flow prognosis) and to test new control strategies based on volume instead of flow thresholds. We investigated two situations with different objective functions, considering higher negative impact from misses than false alarms and vice versa, and obtained in both cases a reduction of the rate of false alarms, higher flow thresholds and lower bypass compared to the baseline control. We also assess a new control strategy that employs thresholds of predicted accumulated volume instead of predicted flow and achieved even better results. Full article

This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time-series prediction across diverse catchments and compares its results against SWAT predictions. We found strong evidence of RF’s better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [NSE] > 0.5), whereas SWAT generally yielded unsatisfactory results (NSE < 0.5) and tended to overestimate daily streamflow by up to 27% (PBIAS). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth. Full article

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability. Full article

Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. Full article

The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation test cases known for their complex shock–boundary and shock–shock interactions: (a) a flat plate in hypersonic flow, (b) a Mach 20.2 flow over a 70-degree interplanetary probe, (c) a hypersonic flow around a flared cylinder, and (d) a hypersonic flow around a biconic. Part A of this paper covers the first two cases, while Part B will discuss the remaining cases. These scenarios have been extensively used by researchers to validate prominent parallel DSMC solvers, due to the challenging nature of the flow features involved. The validation requires meticulous selection of simulation parameters, including particle count, grid density, and time steps. This work evaluates the SPARTA (Stochastic Parallel Rarefied-gas Time-Accurate Analyzer) kernel’s accuracy against these test cases, highlighting its parallel processing capability via domain decomposition and MPI communication. This method promises substantial improvements in computational efficiency and accuracy for complex hypersonic vehicle simulations. Full article

This research focuses on improving flood management of the Malwathu Oya River in Anuradhapura Historical City, Sri Lanka, by designing an efficient gate system for the weir of Halpan Ela in the Malwathu Oya River. Frequent flooding threatens agriculture, infrastructure, and public safety in this region. This research aims to enhance water level control in the upper reach of Halpan Ela Anicut by evaluating rainfall patterns, tank spillway efficiency, and gate operation challenges. Historical data on rainfall and tank spillage were analyzed. Flow simulations revealed significant pressure differences, with the existing gate structure showing an upstream pressure of 114,492.5 Pa at a maximum flow of 1740 m³/s, compared to 105,406 Pa for the new flap gate system at the same flow rate. This represents a pressure difference of 9 kPa, equivalent to a 0.9 m water head. Despite the system’s estimated cost of USD 0.1 million, the potential reduction in river flood damage, which currently exceeds USD 0.2 million annually, demonstrates its value. This research emphasizes the effectiveness of the flap gate system in reducing flood risks in Anuradhapura City compared to the existing gate type, though it is only a part of a broader flood mitigation strategy. Full article

High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of exquisite models with good prediction results. However, they ignore the reality that traffic flows can propagate backwards on road networks when modeling spatial relationships, as well as associations between distant nodes. In addition, more effective model components for modeling temporal relationships remain to be developed. To address the above challenges, we propose a local–global features fusion temporal convolutional network (LGTCN) for spatio-temporal traffic flow prediction, which incorporates a bidirectional graph convolutional network, probabilistic sparse self-attention, and a multichannel temporal convolutional network. To extract the bidirectional propagation relationship of traffic flow on the road network, we improve the traditional graph convolutional network so that information can be propagated in multiple directions. In addition, in spatial global dimensions, we propose probabilistic sparse self-attention to effectively perceive global data correlations and reduce the computational complexity caused by the finite perspective graph. Furthermore, we develop a multichannel temporal convolutional network. It not only retains the temporal learning capability of temporal convolutional networks, but also corresponds each channel to a node, and it realizes the interaction of node features through output interoperation. Extensive experiments on four open access benchmark traffic flow datasets demonstrate the effectiveness of our model. Full article

Combustion through detonation marks an important leap in efficiency over standard deflagration methods. This research introduces a Pulsed Detonation Combustor (PDC) model that uses Hydrogen as fuel and Oxygen as an oxidizer, specifically targeting carbon-free combustion efforts. The PDC aerodynamic features boost operating cycle frequency and facilitate Deflagration-to-Detonation Transition (DDT) within distances less than 200 mm by means of Hartmann–Sprenger resonators and cross-flow fuel/oxidizer injection. The achievement of quality mixing in a short-time filling process represents not only higher cycle operation but also enhanced performances. The scope of this paper is to assess the impact of different fuel injectors with different opening areas on the performances of the PDC. This assessment, expressed as a function of the Equivalence Ratio (ER), is conducted using two primary methods. Instantaneous static pressures are recorded and processed to extract the maximum and average cycle pressure and characterize the pressure augmentation. Thrust measurements obtained using a load cell are averaged over the detonation cycle to calculate the time-averaged thrust. The specific impulse is subsequently determined based on these thrust measurements and the corresponding mass flow data. Full article

This study focuses on optimizing the storage capacity of an underground natural gas storage facility through numerical modeling and simulation techniques. The reservoir, characterized by an elongated dome structure, was discretized into approximately 16,000 cells. Simulations were conducted using key parameters such as permeability (10–70 mD) and porosity (12–26%) to assess the dynamics of gas injection and pressure distribution. The model incorporated core and petrophysical data to accurately represent the reservoir’s behavior. By integrating new wells in areas with storage deficits, the model demonstrated improvements in storage efficiency and pressure uniformity. The introduction of additional wells led to a significant increase in storage volume from 380 to 512 million Sm³ and optimized the injection process by reducing the storage period by 25%. The study concludes that reservoir performance can be enhanced with targeted well placement and customized flow rates, resulting in both increased storage capacity and economic benefits. Full article

Background: The application of normo- and hyperbaric O₂ is a common therapy option in various disease patterns. Thereby, the applied O₂ affects the whole body, including the blood and its components. This study investigates influences of pressure and oxygen fraction on human blood plasma, nutrient media, and the functions of neutrophil granulocytes (PMNs). Methods: Neutrophil migration, reactive oxygen species (ROS) production, and NETosis were examined by live cell imaging. The treatment of various matrices (Roswell Park Memorial Institute 1640 medium, Dulbecco’s Modified Eagle’s Medium, H₂O, human plasma, and isolated PMNs) with hyperbaric oxygen (HBO) was performed. In addition, the expression of different neutrophil surface epitopes (CD11b, CD62L, CD66b) and the oxidative burst were investigated by flow cytometry (FACS). The application of cold atmospheric plasma (CAP) to normoxic and normobaric culture media served as a positive control. Soluble reaction products such as H₂O₂, reactive nitrogen species (RNS: NO₂⁻ and NO₃⁻), and ROS-dependent dihydrorhodamine oxidation were quantified by fluoro- and colorimetric assay kits. Results: Under normobaric normoxia, PMNs migrate slower and shorter in comparison with normobaric hyper- or hypoxic conditions and hyperbaric hyperoxia. The pressure component has less effect on the migration behavior of PMNs than the O₂ concentration. Individual PMN cells produce prolonged ROS at normoxic conditions. PMNs showed increased expression of CD11b in normobaric normoxia, lower expression of CD62L in normobaric normoxia, and lower expression of CD66b after HBO and CAP treatment. Treatment with CAP increased the amount of ROS and RNS in common culture media. Conclusions: Hyperbaric and normobaric O₂ influences neutrophil functionality and surface epitopes in a measurable way, which may have an impact on disorders with neutrophil involvement. In the context of hyperbaric experiments, especially high amounts of H₂O₂ in RPMI after hyperbaric oxygen should be taken into account. Therefore, our data support a critical indication for the use of normobaric and hyperbaric oxygen and conscientious and careful handling of oxygen in everyday clinical practice. Full article

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NO_x concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas. Full article

Nonlinear acoustic effects become prominent when acoustic waves propagate through an orifice, particularly at higher pressure amplitudes, potentially generating vortex rings and transferring acoustic energy into the flow. This study develops and validates a predictive theoretical model for acoustic behaviour both within and outside an orifice under linear conditions. Using transfer matrices, the model predicts the external acoustic field, while finite element numerical simulations are employed to validate the theoretical predictions in the linear regime. The experimental setup includes an impedance tube with a plate and orifice, supported by a custom-built system, where a loudspeaker generates acoustic waves. A single microphone is used to measure acoustic particle velocity and characterize the phenomenon, enabling the identification of the onset of nonlinearity. The experimental data show good agreement with the linear theoretical predictions. This work represents the first observation of nonlinear effects in a free-field environment within a semi-anechoic chamber, eliminating reflections from external surfaces, and demonstrates the efficacy of a purely acoustic-based system (speaker and two microphones) for evaluating speaker velocity and the resulting velocity within the orifice. Full article