Search Results (478)

The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this transformation, leading to complex vehicular networks that are crucial yet susceptible to cyber threats. The complexity and openness of these networks expose them to a plethora of cyber-attacks, from passive eavesdropping to active disruptions like Denial of Service and Sybil attacks. These not only compromise the safety and efficiency of vehicular networks but also pose a significant risk to the stability and resilience of the Internet of Vehicles. Addressing these vulnerabilities, this paper proposes a Dynamic Forest-Structured Ensemble Network (DFSENet) specifically tailored for the Internet of Vehicles (IoV). By leveraging data-balancing techniques and dimensionality reduction, the DFSENet model is designed to detect a wide range of cyber threats effectively. The proposed model demonstrates high efficacy, with an accuracy of 99.2% on the CICIDS dataset and 98% on the car-hacking dataset. The precision, recall, and f-measure metrics stand at 95.6%, 98.8%, and 96.9%, respectively, establishing the DFSENet model as a robust solution for securing the IoV against cyber-attacks. Full article

Background: The pathogenesis of MASLD (metabolic dysfunction-associated steatotic liver disease) is driven by environmental, genetic, metabolic, immune, and inflammatory factors. IL-17 and TLR4 determine hepatic steatosis, inflammation, and finally fibrosis. Objectives: To explore the associations between the plasma levels of inflammatory markers, TLR4, and the cytokines IL17A/F, as well as their connections with the degree of hepatic steatosis and the risk of hepatic fibrosis (defined by the FIB-4 score) in MASLD patients. Methods: The study cohort included 80 patients diagnosed with MASLD. The IL-17A/F and TLR4 serum concentrations were determined using the ELISA method. Results: We found a significant difference in the CAR levels (C-reactive protein to albumin ratio) when comparing MASLD patients with severe steatosis to those with mild/moderate steatosis (Student’s t test, t (71) = 2.32, p = 0.023). The PIV (pan-immune inflammatory value) was positively correlated with the SII (systemic immune inflammation index), (r = 0.86, p < 0.0001) and the CAR (r = 0.41, p = 0.033) in MASLD patients with severe steatosis. In contrast, increased values of the LMR (lymphocyte to monocyte ratio) were significantly associated, with decreased levels of the SII (ρ = −0.38, p = 0.045). We also found a positive correlation between the CAR and the SII (r = 0.41, p = 0.028). In patients with mild/moderate steatosis, a significant positive correlation was observed between the SII and IL17A (r = 0.36, p = 0.010), the PIV and the CAR (r = 0.29, p = 0.011), the PIV and the SII (r = 0.87, p < 0.0001) and the PIV and IL17A (r = 0.3, p = 0.036). A negative correlation was observed between the LMR and the SII (r = −0.55, p < 0.0001) and the CAR and IL17F (r = −0.37, p = 0.011). Regarding the inflammatory markers, the PIV (336.4 vs. 228.63, p = 0.0107), and the SII (438.47 vs. 585.39, p = 0.0238) had significantly lower levels in patients with an intermediate–high risk of hepatic fibrosis as compared with the patients with a low risk of hepatic fibrosis. The PNI (prognostic nutritional index) (47.16 vs. 42.41, p = 0.0392) had significantly different levels in patients with the likelihood of hepatic fibrosis than those with a low risk of hepatic fibrosis. Conclusions: Regarding the inflammatory markers, the PIV and the SII hold promise as biomarkers for discriminating between MASLD patients with an intermediate–high risk and those with a low risk of hepatic fibrosis. Our findings underscore the role of IL-17A and its potential relationship with inflammatory markers in MASLD pathogenesis and the progression to hepatic fibrosis. Full article

Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss. Full article

In the context of intelligent connected environments, this study explores methods to guide the speed of mixed traffic flow to improve intersection efficiency. First, the composition of traffic flow is analyzed, and a car-following model for mixed traffic flow is established, considering reaction time and the psychology of human drivers. Secondly, considering the uncertainty factors of the leading vehicle, we establish a speed guidance model for mixed traffic flow platoons. Finally, a simulation environment is built using Python and SUMO, evaluating the speed guidance effect from the perspectives of different traffic volumes and CAV penetration rates based on average stop times and average delays. The research findings indicate that the speed guidance algorithm proposed in this paper can reduce the number of parking times and delays at intersections. When the mixed traffic flow remains constant, the higher the penetration rate of CAV, the more effective the guidance becomes. However, when the traffic flow reaches a certain level, congestion intensifies, and the effectiveness of the guidance gradually diminishes. Therefore, this study is more applicable to long-distance intersections or key intersections on interconnected roads outside urban areas. Full article

The predictors of urban trip mode choice and one of its important components, public transit ridership, have still not been thoroughly investigated using case studies in Central Europe. Therefore, this study attempts to clarify the correlates of mode choices for commute travel and shopping, and entertainment travel to distant places, as well as the frequencies of public transit use of university students, using a wide range of explanatory variables covering individual, household, and socio-economic attributes as well as their perceptions, mobility, and the nearby built environment. The correlation hypothesis of these factors, especially the role of the street network, was tested by collecting the data from 1288 university students in Krakow and developing Binary Logistic and Ordinal Probit models. The results show that gender, age, car ownership, main daily activity, possession of a driving license, gross monthly income, duration of living in the current home, daily shopping area, sense of belonging to the neighborhood, quality of social/recreational facilities of the neighborhood, and commuting distance can predict commute and non-commute mode choices, while gender, daily activity, financial dependence from the family, entertainment place, quality of social/recreational facilities, residential self-selection, number of commute trips, time living in the current home, and street connectivity around home are significantly correlated with public transit use. Some of these findings are somewhat different from those regarding university students in Western Europe or other high-income countries. These results can be used for policy making to reduce students’ personal and household car use and increase sustainable modal share in Poland and similar neighboring countries. Full article

The continuous advancement of connected and automated driving technologies has garnered considerable public attention regarding the safety and reliability of automated vehicles (AVs). Comprehensive and efficient testing is essential before AVs can be deployed on public roads. Current mainstream testing methods involve high costs in real-world settings and limited immersion in numerical simulations. To address these challenges and facilitate testing in mixed traffic scenarios involving both human-driven vehicles (HDVs) and AVs, we propose a testing and evaluation approach using a driving simulator. Our methodology comprises three fundamental steps. First, we systematically classify scenario elements by drawing insights from the scenario generation logic of the driving simulator. Second, we establish an interactive traffic scenario that allows human drivers to manipulate vehicles within the simulator while AVs execute their decision and planning algorithms. Third, we introduce an evaluation method based on this testing approach, validated through a case study focused on car-following models. The experimental results confirm the efficiency of the simulation-based testing method and demonstrate how car-following efficiency and comfort decline with increased speeds. The proposed approach offers a cost-effective and comprehensive solution for testing, considering human driver behavior, making it a promising method for evaluating AVs in mixed traffic scenarios. Full article

Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model’s adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems. Full article

This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments. Full article

The modern Internet of Autonomous Vehicles (IoVs) has enabled the development of autonomous vehicles that can interact with each other and their surroundings, facilitating real-time data exchange and communication between vehicles, infrastructure, and the external environment. The lack of security procedures in vehicular networks and Controller Area Network (CAN) protocol leaves vehicles exposed to intrusions. One common attack type is the message injection attack, which inserts fake messages into original Electronic Control Units (ECUs) to trick them or create failures. Therefore, this paper tackles the pressing issue of cyber-attack detection in modern IoV systems, where the increasing connectivity of vehicles to the external world and each other creates a vast attack surface. The vulnerability of in-vehicle networks, particularly the CAN protocol, makes them susceptible to attacks such as message injection, which can have severe consequences. To address this, we propose an intelligent Intrusion detection system (IDS) to detect a wide range of threats utilizing machine learning techniques. However, a significant challenge lies in the inherent imbalance of car-hacking datasets, which can lead to misclassification of attack types. To overcome this, we employ various imbalanced pre-processing techniques, including NearMiss, Random over-sampling (ROS), and TomLinks, to pre-process and handle imbalanced data. Then, various Machine Learning (ML) techniques, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Naive Bayes (NB), and K-Nearest Neighbors (k-NN), are employed in detecting and predicting attack types on balanced data. We evaluate the performance and efficacy of these techniques using a comprehensive set of evaluation metrics, including accuracy, precision, F1_Score, and recall. This demonstrates how well the suggested IDS detects cyberattacks in external and intra-vehicle vehicular networks using unbalanced data on vehicle hacking. Using k-NN with various resampling techniques, the results show that the proposed system achieves 100% detection rates in testing on the Car-Hacking dataset in comparison with existing work, demonstrating the effectiveness of our approach in protecting modern vehicle systems from advanced threats. Full article

In recent years, the Internet of Things (IoT) and the Internet of Vehicles (IoV) represent rapidly developing technologies. The majority of car manufacturing companies invest large amounts of money in the field of connected autonomous vehicles. Applications of connected and autonomous vehicles (CAVs) relate to smart transport services and offer benefits to both society and the environment. However, the development of autonomous vehicles may create vulnerabilities in security systems, through which attacks could harm both vehicles and their drivers. To this end, CAV development in vehicular ad hoc networks (VANETs) requires secure wireless communication. However, this kind of communication is vulnerable to a variety of cyber-attacks, such as spoofing. In essence, this paper presents an in-depth analysis of spoofing attack impacts under realistic road conditions, which may cause some traffic congestion. The novelty of this work has to do with simulation scenarios that take into consideration a set of cross-layer parameters, such as packet delivery ratio (PDR), acceleration, and speed. These parameters can determine the integrity of the exchanged wave short messages (WSMs) and are aggregated in a central trusted authority (CTA) for further analysis. Finally, a statistical metric, coefficient of variation (CoV), which measures the consequences of a cyber-attack in a future crash, is estimated, showing a significant increase (12.1%) in a spoofing attack scenario. Full article

Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model’s precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future. Full article

The aim of this study was to assess the relationship of different chronic diseases with immunonutritional markers in the senior population. Methods: this study included 1190 hospitalized geriatric patients. The criteria to participate were ability to communicate, given consent and C-reactive protein (CRP) lower than 6 mg/dL. Results: the mean age of the study population was 81.7 ± 7.6 years. NLR (neutrophil-to-lymphocyte ratio), LMR (lymphocyte-to-monocyte ratio), MWR (monocyte-to-white blood cell ratio), SII (systemic immune–inflammation index), PNI (prognostic nutritional index) and CAR (C-reactive protein-to-albumin ratio) were related to age. NLR and MWR were higher, while LMR, PLR (platelet-to-lymphocyte ratio and SII were lower in men. All markers were related to BMI. NLR, LMR, LCR (lymphocyte-to-CRP ratio), MWR, PNI and CAR were related to several concomitant chronic diseases. In multivariate analyses, age and BMI were selected as independent predictors of all studied immunonutritional markers. Atrial fibrillation, diabetes mellitus and dementia appear most often in the models. PNI presented the most consistent statistical association with age, BMI and concomitant chronic diseases. Conclusions: this study reveals the pivotal role of aging and BMI in inflammatory marker levels and the association of immunonutritional markers with different chronic diseases. Atrial fibrillation seems to have the most dominant connection to the immunonutritional markers. Full article

This study presents an energy regeneration model and some theory required to construct a regeneration braking system. Due to the effects of carbon dioxide (CO₂) emissions, there is increasing interest in the use of electric vehicles (EVs), electric bikes, electric bicycles, electric buses and electric aircraft globally. In order to promote the use of electric transportation systems, there is a need to underscore the impact of net zero emissions. The development of EVs requires regenerating braking system. This study presents the advantages of regenerative braking. This system is globally seen in applications such as electric cars, trams, and trains. In this study, the design specification, design methodology, testing configurations, Simulink model, and recommendations will be outlined. A unique element of this work is the practical experiment that was carried out using 1.5 Amps with no load and 2.15 Amps with a load. The discharge voltage was purely from the 22 W bulb load connected to the capacitor bank as we limited this study to the use of 1.5 Amps and it took 15 min for a full discharge cycle, after which no charge was left in the capacitor bank. The results showed that the discharge rate and charging rate for the regenerative braking system were effective but could be improved. The objective of this paper is to investigate how a supercapacitor works alongside a battery in regenerative braking applications. This study demonstrates that the superconductor used can deliver maximum power when required. Also, it can also withstand elevated peaks in charging or discharging current via the supercapacitor. Combining a battery with a supercapacitor reduces the abrupt load on the battery by shifting it to the capacitor. When these two combinations are used in tandem, the battery pack’s endurance and lifespan are both boosted. Full article

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized. Full article

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks. Full article