Search Results (82)

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system’s overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with $99\%$ probability. The average AoI quantity is maintained within the 5–10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties. Full article

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R² values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems. Full article

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. Full article

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions. Full article

Objectives: This study investigated a novel strategy for improving regenerative cartilage outcomes. It combines fractional laser treatment with the implantation of neocartilage generated from autologous dynamic Self-Regenerating Cartilage (dSRC). Methods: dSRC was generated in vitro from harvested autologous swine chondrocytes. Culture was performed for 2, 4, 8, 10, and 12 weeks to study matrix maturation. Matrix formation and implant integration were also studied in vitro in swine cartilage discs using dSRC or cultured chondrocytes injected into CO₂ laser-ablated or mechanically punched holes. Cartilage discs were cultured for up to 8 weeks, harvested, and evaluated histologically and immunohistochemically. Results: The dSRC matrix was injectable by week 2, and matrices grew larger and more solid with time, generating a contiguous neocartilage matrix by week 8. Hypercellular density in dSRC at week 2 decreased over time and approached that of native cartilage by week 8. All dSRC groups exhibited high glycosaminoglycan (GAG) production, and immunohistochemical staining confirmed that the matrix was typical of normal hyaline cartilage, being rich in collagen type II. After 8 weeks in cartilage lesions in vitro, dSRC constructs generated a contiguous cartilage matrix, while isolated cultured chondrocytes exhibited only a sparse pericellular matrix. dSRC-treated lesions exhibited high GAG production compared to those treated with isolated chondrocytes. Conclusions: Isolated dSRC exhibits hyaline cartilage formation, matures over time, and generates contiguous articular cartilage matrix in fractional laser-created microenvironments in vitro, being well integrated with native cartilage. Full article

In this paper, an irregular octagonal two-port MIMO patch antenna is designed specifically for New Radio (NR) 5G applications in the mid-band sub-6 GHz. The proposed antenna comprises an irregularly shaped patch antenna equipped with a regular 50-ohm feed line and a parasitic strip line antenna, and is partially grounded. Jeans material serves as a substrate with an effective dielectric constant of 1.6 and a thickness of 1 mm. This material is studied experimentally. The proposed antenna design undergoes analysis and optimization using the ANSYS HFSS tool. Furthermore, the design incorporates the influence of the slot on both the ground plane and the parasitic strip line to optimize performance, enhance isolation, and improve impedance matching among antenna elements. The dimensions of the jeans substrate are 40 mm × 50 mm. The simulated impedance bandwidth ranged from 3.6 GHz to 7 GHz and the measured bandwidth was slightly narrower, from 4.35 GHz to 7 GHz. The simulation results demonstrated an isolation level greater than 12 dB between antenna elements, while the measured results reached 28.5 dB, and the peak gain for this proposed antenna stood at 6.74 dB. These qualities made this proposed antenna suitable for various New Radio mid-band 5G wireless applications within the sub-6 GHz band, such as N79, Wi-Fi-5/6, V2X, and DSRC applications. Full article

Cooperative communications advancements in Vehicular-to-Everything (V2X) are bolstering the autonomous driving paradigm. V2X nodes are connected through communication technology, such as a short-range communication mode (Dedicated Short Range Communication (DSRC) and Cellular-V2X) or a long-range communication mode (Uu). Conventional vehicular networks employ static wireless vehicular communication technology without considering the traffic load on any individual V2X communication technology and the traffic dynamics in the vicinity of the V2X node, and are hence inefficient. In this study, we investigate hybrid V2X communication and propose an autonomous and intelligent technology selection algorithm using a decision tree. The algorithm uses the information from the received Cooperative Intelligent Transport Systems (C-ITS) Cooperative Awareness Messages (CAMs) to collect statistics such as inter vehicular distance, one-way end-to-end latency and CAM density. These statistics are then used as input for the decision tree for selecting the appropriate technology (DSRC, C-V2X PC5 or 5G) for the subsequent scheduled C-ITS message transmission. The assessment of the intelligent hybrid V2X algorithm’s performance in our V2X test setup demonstrates enhancements in one-way end-to-end latency, reliability, and packet delivery rate when contrasted with the conventional utilization of static technology. Full article

Angiopoietin-like protein 3 (ANGPTL3) is a plasmatic protein that plays a crucial role in lipoprotein metabolism by inhibiting the lipoprotein lipase (LPL) and the endothelial lipase (EL) responsible for the hydrolysis of phospholipids on high-density lipoprotein (HDL). Interest in developing new pharmacological therapies aimed at inhibiting ANGPTL3 has been growing due to the hypolipidemic and antiatherogenic profile observed in its absence. The goal of this study was the in silico characterization of the interaction between ANGPTL3 and EL. Because of the lack of any structural information on both the trimeric coiled-coil N-terminal domain of ANGPTL3 and the EL homodimer as well as data regarding their interactions, the first step was to obtain the three-dimensional model of these two proteins. The models were then refined via molecular dynamics (MD) simulations and used to investigate the interaction mechanism. The analysis of interactions in different docking poses and their refinement via MD allowed the identification of three specific glutamates of ANGPTL3 that recognize a positively charged patch on the surface of EL. These ANGPTL3 key residues, i.e., Glu154, Glu157, and Glu160, could form a putative molecular recognition site for EL. This study paves the way for future investigations aimed at confirming the recognition site and at designing novel inhibitors of ANGPTL3. Full article

With the rapid deployment of intelligent transportation systems in real-life applications, both dedicated short-range communications (DSRC) and cellular Vehicle-to-Everything (C-V2X), utilized to enable V2X communication, are undergoing extensive development to meet the quality of service (QoS) demands of advanced vehicular applications and scenarios. Compared to C-V2X, which lacks fully validated effective reliability, DSRC has undergone extensive field testing worldwide, ensuring its practicality. IEEE 802.11bd, as the next-generation V2X (NGV) standard within DSRC, is expected to greatly exceed the performance of its predecessor, 802.11p. However, existing studies mention that the ambient traffic environment will influence the performance of V2X due to the cyber-physical properties of V2X. To fully assess the advancements of NGV, this study presents a comparative analysis of IEEE 802.11bd and IEEE 802.11p, focusing on dynamic traffic conditions. Specifically, the technical advancements of the IEEE 802.11bd standard are first theoretically examined, emphasizing significant enhancements in aspects like modulation and coding schemes, coding rates, and channel coding. Subsequently, these critical technical enhancements are implemented in Veins, a simulation framework for the Internet of Vehicles (IoV), encompassing large-scale dynamic traffic scenarios. The simulation results indicate that the IEEE 802.11bd standard significantly enhances the data transfer rate compared to IEEE 802.11p, achieving a stable twofold increase. Furthermore, the data transmission latency is reduced by over half compared to IEEE 802.11p, while the data transmission reliability experiences a noteworthy 20% enhancement. Notably, the enhanced data transmission mode of the IEEE 802.11bd standard requires an increased signal-to-noise ratio (SNR). Additionally, this research evaluates the data dissemination properties in the IoV and finds that the traffic volume has a limited impact on the data propagation speed. Full article

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios. Full article

With the development of the Internet of Things (IoT) and communication networks, the concept of a smart city emerged spontaneously. In the traffic control of a smart city, the vehicular network plays an important role. If the driving information of vehicles can be collected from the vehicular network and then aggregated into a smart city system, traffic control facilities can be adjusted in real time to improve traffic or increase traffic safety. This study is based on WAVE/DSRC under the IEEE 802.11p and IEEE 1609 standards. When the vehicle is moving, the On-Board Unit (OBU) on the vehicle and the Roadside Unit (RSU) transmit data through the 5.9 GHz (5.85–5.925 GHZ) frequency band to establish vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure communication (V2I). The immediacy of the transmission of beacon messages is also discussed over a vehicular network. A beacon message is essential for communication in a vehicular network, including various safety messages, such as driving directions, driving speed, and location. We introduce the Age-of-Information (AoI) indicator to reflect the immediacy of information. AoI is used for the elapsed time after the sender samples the message until the receiver receives the message. We propose a centralized AoI-based protocol and a decentralized AoI-based protocol. By using Random-Walk and SUMO, we simulate driving dynamics and various RSU setting scenarios. Finally, we verify the performance of the proposed AoI-based protocol through experimental simulations. Full article

This retrospective cohort study included one hundred fifty-seven medium and large-size dogs with the aim of evaluating the effect of signalment and echocardiographic features on complications, outcomes and left ventricular modifications before and after patent ductus arteriosus (PDA) closure. The patients were divided in two groups based on the heart remodeling after closure: Group A included dogs that had a reduction in the end-systolic volume index (ESVI) after closure compared to the ESVI measured before; Group B included dogs without a reduction in ESVI after closure. Body weight, minimal ductal diameter (MDD) of PDA, end-diastolic volume index and presence of arrhythmias at presentation were significantly higher in Group B compared to Group A. The shortening fraction and ejection fraction after closure were reduced in both groups, but in Group B there was a major reduction, and the mean values indicated a possible systolic dysfunction. Complications during the procedure and death due to cardiac reasons were greater in Group B compared to Group A. In conclusion, a higher body weight, a larger MDD, a more severe heart enlargement or arrhythmias at presentation increased the risk of developing a worsening structural and functional condition after ductal closure, and this can be associated with perioperative complications and cardiac death. Full article

Background: In children, laparoscopic cholecystectomy (LC) is now considered the gold standard for gallbladder (GB) removal. In the past, hemolytic disorders associated with cholelithiasis represented the most frequent conditions requiring LC; this is being overtaken by cholelithiasis and biliary conditions in overweight or ex-premature children. Aims: This study aims to describe current indications and timing for LC in pediatric patients. Methods: Retrospective study. Data on previous medical therapy, ultrasound, pre- and intraoperative aspects, and histology were collected for patients treated in 2020–2023. Results: In total, 45 patients were enrolled: 15 who underwent urgent surgery and 30 electives. Groups differed in terms of obesity rate, symptoms, ultrasound features, and intraoperative status. The most relevant risk factors for surgical complexity were age and pubertal stage, elevated cholestasis indexes, and gallbladder wall thickness > 3 mm at ultrasound. GB wall thickening ≥3 mm, US Murphy sign, fluid collections, and gallbladder distention on ultrasound correlated with high surgical scores. Conclusions: Indications for laparoscopic cholecystectomy in children seem to evolve caused by changing characteristics of the pediatric population. Patients with overweight/obesity may develop more complex GB diseases. Asymptomatic patients should be considered for surgery after observation, considering age and/or pubertal maturation when other risk factors are absent. Full article

(1) Background. Exploring the evolution of SARS-CoV-2 load and clearance from the upper respiratory tract samples is important to improving COVID-19 control. Data were collected retrospectively from a laboratory dataset on SARS-CoV-2 load quantified in leftover nasal pharyngeal swabs (NPSs) collected from symptomatic/asymptomatic individuals who tested positive to SARS-CoV-2 RNA detection in the framework of testing activities for diagnostic/screening purpose during the 2020 and 2021 winter epidemic waves. (2) Methods. A Statistical approach (quantile regression and survival models for interval-censored data), novel for this kind of data, was applied. We included in the analysis SARS-CoV-2-positive adults >18 years old for whom at least two serial NPSs were collected. A total of 262 SARS-CoV-2-positive individuals and 784 NPSs were included: 193 (593 NPSs) during the 2020 winter wave (before COVID-19 vaccine introduction) and 69 (191 NPSs) during the 2021 winter wave (all COVID-19 vaccinated). We estimated the trend of the median value, as well as the 25th and 75th centiles of the viral load, from the index episode (i.e., first SARS-CoV-2-positive test) until the sixth week (2020 wave) and the third week (2021 wave). Interval censoring methods were used to evaluate the time to SARS-CoV-2 clearance (defined as Ct < 35). (3) Results. At the index episode, the median value of viral load in the 2021 winter wave was 6.25 log copies/mL (95% CI: 5.50–6.70), and the median value in the 2020 winter wave was 5.42 log copies/mL (95% CI: 4.95–5.90). In contrast, 14 days after the index episode, the median value of viral load was 3.40 log copies/mL (95% CI: 3.26–3.54) for individuals during the 2020 winter wave and 2.93 Log copies/mL (95% CI: 2.80–3.19) for those of the 2021 winter wave. A significant difference in viral load shapes was observed among age classes (p = 0.0302) and between symptomatic and asymptomatic participants (p = 0.0187) for the first wave only; the median viral load value is higher at the day of episode index for the youngest (18–39 years) as compared to the older (40–64 years and >64 years) individuals. In the 2021 epidemic, the estimated proportion of individuals who can be considered infectious (Ct < 35) was approximately half that of the 2020 wave. (4) Conclusions. In case of the emergence of new SARS-CoV-2 variants, the application of these statistical methods to the analysis of virological laboratory data may provide evidence with which to inform and promptly support public health decision-makers in the modification of COVID-19 control measures. Full article

Transportation efficiency and safety are crucial development areas nowadays. Cooperative Intelligent Transport Systems (C-ITSs), relying on Vehicle-to-Everything (V2X) communication, are a promising group of technologies and applications aimed at solving several issues related to road safety or efficiency. The C-Roads Platform was brought to life to ensure the cross-border harmonization of C-ITS at a European level, guiding several pilot activities in national deployment projects and providing a harmonized pan-European C-ITS service perspective. Because of the safety relevance of V2X technologies, it is essential to ensure that the crucial parameters of wireless communication are within an acceptable range to serve C-ITS applications appropriately. In this work, we developed a simulation pipeline to evaluate future V2X deployments using the real-world traffic and map data of a C-Roads harmonized major Hungarian C-ITS deployment site. First, we selected three time periods representing different traffic patterns. Then, we reconstructed the flow-based traffic data from the real-world traffic counters for the selected time periods. We developed an approach based on linear equations to perform the conversion. Eventually, we used the real-world data to simulate the effects of various DSRC (ITS-G5-based) C-ITS services and V2X penetration rates on the Channel Busy Ratio (CBR) parameter of the radio access environment. Full article