Analytical Models of the Performance of C-V2X Mode 4 Vehicular Communications

The C-V2X or LTE-V standard has been designed to support V2X (Vehicle to Everything) communications. The standard is an evolution of LTE, and it has been published by the 3GPP in Release 14. This new standard introduces the C-V2X or LTE-V Mode 4 that is specifically designed for V2V communications using the PC5 sidelink interface without any cellular infrastructure support. In Mode 4, vehicles autonomously select and manage their radio resources. Mode 4 is highly relevant since V2V safety applications cannot depend on the availability of infrastructure-based cellular coverage. This paper presents the first analytical models of the communication performance of C-V2X or LTE-V Mode 4. In particular, the paper presents analytical models for the average PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver, and for the four different types of transmission errors that can be encountered in C-V2X Mode 4. The models are validated for a wide range of transmission parameters and traffic densities. To this aim, this study compares the results obtained with the analytical models to those obtained with a C-V2X Mode 4 simulator implemented over Veins

Recent Results for Supercritical Controlled Branching Processes with Control Random Functions

2000 Mathematics Subject Classification: 60J80, 60F05In this paper we are concerned with the controlled branching processes with random control function. Recently, we have considered them under the condition of asymptotically linear growth of the mathematical expectations associated to the random control variables. We present a review of the main results obtained until now, mainly, in the supercritical case.Research supported by the Ministerio de Ciencia y Tecnologi a and the FEDER through the Plan Nacional de Investigacion Cientifi ca, Desarrollo e Innovaci on Tecnologica, grant BFM2003-06074

Controlled Branching Processes with Continuous Time

A class of controlled branching processes with continuous time is introduced and some limiting distributions are obtained in the critical case. An extension of this class as regenerative controlled branching processes with continuous time is proposed and some asymptotic properties are considered

Semantic Approach for Discovery and Visualization of Academic Information Structured with OAI-PMH

There are different channels to communicate the results of a scientific research; however, several research communities state that the Open Access (OA) is the future of acad emic publishing. These Open Ac cess Platforms have adopted OAI - PMH (Open Archives Initiative - the Protocol for Metadata Harvesting) as a standard for communication and interoperability. Nevertheless, it is significant to highlight that the open source know ledge discovery services based on an index of OA have not been developed. Therefore, it is necessary to address Knowledge Discovery (KD) within these platforms aiming at studen ts, teachers and/ or researchers , to recover both , the resources requested and th e resources that are not explicitly requested – which are also appropriate . This objective represents an important issue fo r structured resources under OAI - PMH. This fact is caused because interoperability with other developments carried out outside their implementation environment is generally not a priority (Level 1 "Shared term definitions"). It is here , where the Semantic Web (SW) beco mes a cornerstone of this work. Consequently, we propose OntoOAIV, a semantic approach for the selective knowledge disco very an d visu alization into structured information with OAI - PMH, focused on supporting the activities of scientific or academic research for a specific user. Because of the academic nature of the structured resources with OAI - PMH, the field of application chosen is the context information of a student. Finally, in order to validate the proposed approach, we use the RUDAR (Roskilde University Digital Archive) and REDALYC (Red de Revistas Científicas de América Latina y el Caribe, España y Portugal) repositor ies, which imple ment the OAI - PMH protocol , as well as one s tudent profile for carrying out KD

Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communications

The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community

Improving traffic-related air pollution estimates by modelling minor road traffic volumes

Accurately estimating annual average daily traffic (AADT) on minor roads is essential for assessing traffic-related air pollution (TRAP) exposure, particularly in areas where most people live. Our study assessed the direct and indirect external validity of three methods used to estimate AADT on minor roads in Melbourne, Australia. We estimated the minor road AADT using a fixed-value approach (assuming 600 vehicles/day) and linear and negative binomial (NB) models. The models were generated using road type, road importance index, AADT and distance of the nearest major road, population density, workplace density, and weighted road density. External measurements of traffic counts, as well as black carbon (BC) and ultrafine particles (UFP), were conducted at 201 sites for direct and indirect validation, respectively. Statistical tests included Akaike information criterion (AIC) to compare models’ performance, the concordance correlation coefficient (CCC) for direct validation, and Spearman’s correlation coefficient for indirect validation. Results show that 88.5% of the roads in Melbourne are minor, yet only 18.9% have AADT. The performance assessment of minor road models indicated comparable performance for both models (AIC of 1,023,686 vs. 1,058,502). In the direct validation with external traffic measurements, there was no difference between the three methods for overall minor roads. However, for minor roads within residential areas, CCC (95% confidence interval [CI]) values were − 0.001 (− 0.17; 0.18), 0.47 (0.32; 0.60), and 0.29 (0.18; 0.39) for the fixed-value approach, the linear model, and the NB model, respectively. In the indirect validation, we found differences only on UFP where the Spearman’s correlation (95% CI) for both models and fixed-value approach were 0.50 (0.37; 0.62) and 0.34 (0.19; 0.48), respectively. In conclusion, our linear model outperformed the fixed-value approach when compared against traffic and TRAP measurements. The methodology followed in this study is relevant to locations with incomplete minor road AADT data

Improving traffic-related air pollution estimates by modelling minor road traffic volumes

Accurately estimating annual average daily traffic (AADT) on minor roads is essential for assessing traffic-related air pollution (TRAP) exposure, particularly in areas where most people live. Our study assessed the direct and indirect external validity of three methods used to estimate AADT on minor roads in Melbourne, Australia. We estimated the minor road AADT using a fixed-value approach (assuming 600 vehicles/day) and linear and negative binomial (NB) models. The models were generated using road type, road importance index, AADT and distance of the nearest major road, population density, workplace density, and weighted road density. External measurements of traffic counts, as well as black carbon (BC) and ultrafine particles (UFP), were conducted at 201 sites for direct and indirect validation, respectively. Statistical tests included Akaike information criterion (AIC) to compare models' performance, the concordance correlation coefficient (CCC) for direct validation, and Spearman's correlation coefficient for indirect validation. Results show that 88.5% of the roads in Melbourne are minor, yet only 18.9% have AADT. The performance assessment of minor road models indicated comparable performance for both models (AIC of 1,023,686 vs. 1,058,502). In the direct validation with external traffic measurements, there was no difference between the three methods for overall minor roads. However, for minor roads within residential areas, CCC (95% confidence interval [CI]) values were -0.001 (-0.17; 0.18), 0.47 (0.32; 0.60), and 0.29 (0.18; 0.39) for the fixed-value approach, the linear model, and the NB model, respectively. In the indirect validation, we found differences only on UFP where the Spearman's correlation (95% CI) for both models and fixed-value approach were 0.50 (0.37; 0.62) and 0.34 (0.19; 0.48), respectively. In conclusion, our linear model outperformed the fixed-value approach when compared against traffic and TRAP measurements. The methodology followed in this study is relevant to locations with incomplete minor road AADT data

Periostin in the relation between periodontal disease and atherosclerotic coronary artery disease: A pilot randomized clinical study

Objective: The aim of this study was to analyze the effects of periodontal treatment on markers of atherosclerotic coronary artery disease and circulating levels of periostin. Background: Periostin is necessary for periodontal stability, but it is highly present in atherosclerotic plaques. Treatment of periodontal disease, with low levels of local periostin, is thought to reduce systemic levels of periostin. Thus, this may contribute to cardiovascular health. Methods: A pilot randomized controlled clinical trial was designed to include patients with severe periodontal disease and history of atherosclerotic coronary artery disease. Samples of gingival crevicular fluid (GCF) and serum were collected before and after periodontal treatment by periodontal surgery or non-surgical therapy. The levels of several markers of inflammation and cardiovascular damage were evaluated including CRP, IFN-γ, IL-1ß, IL-10, MIP-1α, periostin, and TNF-α in GCF and CRP, Fibrinogen, IFN-γ, IL-1ß, IL-6, IL-10, L-Selectin, MIP-1α, Periostin, TNF-α, and vWF in serum. Results: A total of 22 patients with an average of 56 years old were recruited for participating in this study. Twenty of them were male. Most of them (82%) had suffered an acute myocardial event and underwent surgery for placing 1, 2, or 3 stents in the coronary arteries more than 6 months ago but less than 1 year. The treatment of periodontal disease resulted in an overall improvement of all periodontal parameters. Regarding the evaluation of GCF and serum, a significant increase of periostin in the GCF was observed after periodontal surgery. In contrast, although other markers in GCF and serum improved, no significant correlations were found. Conclusion: Treatment of periodontal disease through periodontal surgery induces a local and transient increase in the levels of periostin in the gingival crevicular fluid. The effects on systemic markers of inflammation and cardiovascular function have not been confirmed.Funding for open access charge: Universidad de Granada/CBUAResearch Groups #CTS-138, #CTS-1028 (Junta de Andalucía, Spain)Andalucía Talent Hub Program from the Andalusian Knowledge Agency, a program co-funded by the European Union's Seventh Framework Program, Marie Skłodowska-Curie actions (COFUND – Grant Agreement no. 291780) and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucí

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.</jats:p

Carboxymethyl cellulose coated magnetic nanoparticles transport across a human lung microvascular endothelial cell model of the blood–brain barrier

Sustained and safe delivery of therapeutic agents across the blood–brain barrier (BBB) is one of the major challenges for the treatment of neurological disorders as this barrier limits the ability of most drug molecules to reach the brain. Targeted delivery of the drugs used to treat these disorders could potentially offer a considerable reduction of the common side effects of their treatment. The preparation and characterization of carboxymethyl cellulose (CMC) coated magnetic nanoparticles (Fe3O4@CMC) is reported as an alternative that meets the need for novel therapies capable of crossing the BBB. In vitro assays were used to evaluate the ability of these polysaccharide coated biocompatible, water-soluble, magnetic nanoparticles to deliver drug therapy across a model of the BBB. As a drug model, dopamine hydrochloride loading and release profiles in physiological solution were determined using UV-Vis spectroscopy. Cell viability tests in Human Lung Microvascular Endothelial (HLMVE) cell cultures showed no significant cell death, morphological changes or alterations in mitochondrial function after 24 and 48 h of exposure to the nanoparticles. Evidence of nanoparticle interactions and nanoparticle uptake by the cell membrane was obtained by electron microscopy (SEM and TEM) analyses. Permeability through a BBB model (the transwell assay) was evaluated to assess the ability of Fe3O4@CMC nanoparticles to be transported across a densely packed HLMVE cell barrier. The results suggest that these nanoparticles can be useful drug transport and release systems for the design of novel pharmaceutical agents for brain therapy