Strategic and Tactical Guidance for the Connected and Autonomous Vehicle Future

Autonomous vehicle (AV) and Connected vehicle (CV) technologies are rapidly maturing and the timeline for their wider deployment is currently uncertain. These technologies are expected to have a number of significant societal benefits: traffic safety, improved mobility, improved road efficiency, reduced cost of congestion, reduced energy use, and reduced fuel emissions. State and local transportation agencies need to understand what this means for them and what they need to do now and in the next few years to prepare for the AV/CV future. In this context, the objectives of this research are as follows: Synthesize the existing state of practice and how other state agencies are addressing the pending transition to AV/CV environment Estimate the impacts of AV/CV environment within the context of (a) traffic operations—impact of headway distribution and traffic signal coordination; (b) traffic control devices; (c) roadway safety in terms of intersection crashes Provide a strategic roadmap for INDOT in preparing for and responding to potential issues This research is divided into two parts. The first part is a synthesis study of existing state of practice in the AV/CV context by conducting an extensive literature review and interviews with other transportation agencies. Based on this, we develop a roadmap for INDOT and similar agencies clearly delineating how they should invest in AV/CV technologies in the short, medium, and long term. The second part assesses the impacts of AV/CVs on mobility and safety via modeling in microsimulation software Vissim

Finding MRI features to obviate the need of repeat spinal biopsies in clinically suspected persistent or recurrent spinal osteomyelitis

Purpose: The aim of this study was to determine magnetic resonance imaging (MRI) features that could help differentiate the bone destruction due to persistent/recurrent spine infection from worsening bone destruction due to mechanical factors, which could help obviate the need for repeat spine biopsy. Material and methods: A retrospective study was performed on selected subjects who were more than 18 years of age, were diagnosed with infectious spondylodiscitis, underwent at least 2 spinal interventions for the diagnosis at the same level, and had MRI prior to each image-guided intervention. Both MRI studies were analysed for vertebral body changes, paravertebral collections, epidural thickening and collections, bone marrow signal changes, loss of vertebral body height, abnormal signal in intervertebral disc, and loss of disc height. Results: We observed that worsening of changes in paravertebral and epidural soft tissue were statistically more significant predictors of recurrent/persistent spine infection (p < 0.05). However, worsening destruction of vertebral body and intervertebral disc, abnormal vertebral marrow signal changes, and abnormal signal in intervertebral disc did not necessarily indicate worsening infection or recurrence. Conclusions: In patients of infectious spondylitis with suspected recurrence, the most common and pronounced MRI findings of worsening osseous changes can be deceiving and can result in negative repeat spinal biopsy. Changes in paraspinal and epidural soft tissues are more helpful in identifying the cause of worsening bone destruction. Correlation with clinical examination, inflammatory markers, and observing soft tissue changes on follow-up MRI is a more reliable way to identify patients who may benefit from repeat spine biopsy

Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News

COVID-19 impacted every part of the world, although the misinformation about the outbreak traveled faster than the virus. Misinformation spread through online social networks (OSN) often misled people from following correct medical practices. In particular, OSN bots have been a primary source of disseminating false information and initiating cyber propaganda. Existing work neglects the presence of bots that act as a catalyst in the spread and focuses on fake news detection in 'articles shared in posts' rather than the post (textual) content. Most work on misinformation detection uses manually labeled datasets that are hard to scale for building their predictive models. In this research, we overcome this challenge of data scarcity by proposing an automated approach for labeling data using verified fact-checked statements on a Twitter dataset. In addition, we combine textual features with user-level features (such as followers count and friends count) and tweet-level features (such as number of mentions, hashtags and urls in a tweet) to act as additional indicators to detect misinformation. Moreover, we analyzed the presence of bots in tweets and show that bots change their behavior over time and are most active during the misinformation campaign. We collected 10.22 Million COVID-19 related tweets and used our annotation model to build an extensive and original ground truth dataset for classification purposes. We utilize various machine learning models to accurately detect misinformation and our best classification model achieves precision (82%), recall (96%), and false positive rate (3.58%). Also, our bot analysis indicates that bots generated approximately 10% of misinformation tweets. Our methodology results in substantial exposure of false information, thus improving the trustworthiness of information disseminated through social media platforms

Accuracy of smartphone based electrocardiogram for the detection of rhythm abnormalities in limb lead: a cross sectional study, non-randomised, single blinded and single-center study

Background: For the identification of arrhythmia and abnormal instances, researchers are examining the reliability of the interpretation offered by smartphone-based portable ECG monitors. The indicator of an unclear alteration in the electrical activity of the heart is a cardiac abnormality. As a result, its early and accurate identification can avoid myocardial infarction and even sudden cardiac death. Objectives of this study were to evaluate and validate the Spandan 12 lead ECG interpretation for accuracy in detection of the cardiac arrhythmias in comparison to the cardiologist diagnosis, and to evaluate the accuracy of the arrhythmia detection of Spandan ECG in comparison to the 12 lead ECG machine. Methods: This cross-sectional study, non-randomised, single blinded and single-center study was carried out at Shri Mahant Indresh Hospital (SMIH), Dehradun, Uttarakhand, India from 1st August 2022 to 31st January 2023. All patients (n=312) visiting the electrocardiogram (ECG) room at the department of cardiology of the SMIH, Dehradun with the prescription of ECG screening during the study period were included in the study were included in the study. Results: In total, 1528 patients with or without a history of cardiovascular disease were enrolled from outpatient and emergency departments of cardiology. A final total of 312 participants considered for accuracy of interpretation of cardiac arrhythmias detected by the standard 12 lead ECG and smartphone ECG in comparison to cardiologists’ diagnosis. Mean age (SD) was 53.90±14.52 years. The male gender (68.78%) showed the maximum frequency than female gender. True Positive cases derived from confusion matrix for 12 lead standard ECG and smartphone ECG in comparison to cardiologist diagnosis was 264 as compared to 273 from 12 lead gold standard. Sensitivity of smartphone Spandan ECG (81.23%) was comparable to gold standard 12 Lead ECG (81.49%). And, specificity, PPV and NPV of smartphone Spandan ECG was recorded to be better than gold standard 12 Lead ECG. Arrhythmia was detected correctly in 403 (70.8%) cases and 431 (61.86%) cases by smartphone ECG and 12 lead gold standards, respectively. Conclusions: Spandan ECG device scored a high accuracy and sensitivity and high specificity. The overall accuracy of smartphone ECG in detecting the rhythm abnormalities increase by 9%, the significance rises in accuracy of computer interpretation when compared to the cardiologist’s diagnosis

Rescue of Pressure Overload-Induced Heart Failure by Estrogen Therapy.

BackgroundEstrogen pretreatment has been shown to attenuate the development of heart hypertrophy, but it is not known whether estrogen could also rescue heart failure (HF). Furthermore, the heart has all the machinery to locally biosynthesize estrogen via aromatase, but the role of local cardiac estrogen synthesis in HF has not yet been studied. Here we hypothesized that cardiac estrogen is reduced in HF and examined whether exogenous estrogen therapy can rescue HF.Methods and resultsHF was induced by transaortic constriction in mice, and once mice reached an ejection fraction (EF) of ≈35%, they were treated with estrogen for 10 days. Cardiac structure and function, angiogenesis, and fibrosis were assessed, and estrogen was measured in plasma and in heart. Cardiac estrogen concentrations (6.18±1.12 pg/160 mg heart in HF versus 17.79±1.28 pg/mL in control) and aromatase transcripts (0.19±0.04, normalized to control, P&lt;0.05) were significantly reduced in HF. Estrogen therapy increased cardiac estrogen 3-fold and restored aromatase transcripts. Estrogen also rescued HF by restoring ejection fraction to 53.1±1.3% (P&lt;0.001) and improving cardiac hemodynamics both in male and female mice. Estrogen therapy stimulated angiogenesis as capillary density increased from 0.66±0.07 in HF to 2.83±0.14 (P&lt;0.001, normalized to control) and reversed the fibrotic scarring observed in HF (45.5±2.8% in HF versus 5.3±1.0%, P&lt;0.001). Stimulation of angiogenesis by estrogen seems to be one of the key mechanisms, since in the presence of an angiogenesis inhibitor estrogen failed to rescue HF (ejection fraction=29.3±2.1%, P&lt;0.001 versus E2).ConclusionsEstrogen rescues pre-existing HF by restoring cardiac estrogen and aromatase, stimulating angiogenesis, and suppressing fibrosis

Impact of radio channel characteristics on the longitudinal behaviour of truck platoons in critical car-following situations

Truck platooning is an application of cooperative adaptive cruise control (CACC) which relies on vehicle-to-vehicle communications facilitated by vehicle ad-hoc networks. Communication uncertainties can affect the performance of a CACC controller. Previous research has not considered the full spectrum of possible car-following scenarios needed to understand how the longitudinal behaviour of truck platoons would be affected by changes in the communication network. In this paper, we investigate the impact of radio channel parameters on the string stability and collision avoidance capabilities of a CACC controller governing the longitudinal behaviour of truck platoons in a majority of critical car-following situations. We develop and use a novel, sophisticated and open-source VANET simulator OTS-Artery, which brings microscopic traffic simulation, network simulation, and psychological concepts in a single environment, for our investigations. Our results indicate that string stability and safety of truck platoons are mostly affected in car-following situations where truck platoons accelerate from the standstill to the maximum speed and decelerate from the maximum speed down to the standstill. The findings suggest that string stability can be improved by increasing transmission power and lowering receiver sensitivity. However, the safety of truck platoons seems to be sensitive to the choice of the path loos model

Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.publishedVersio

Activation and Induction of Antigen-Specific T Follicular Helper Cells Play a Critical Role in Live-Attenuated Influenza Vaccine-Induced Human Mucosal Anti-influenza Antibody Response

There is increasing interest recently in developing intranasal vaccines against respiratory tract infections. The antibody response is critical for vaccine-induced protection, and T follicular helper cells (TFH) are considered important for mediating the antibody response. Most data supporting the role for TFH in the antibody response are from animal studies, and direct evidence from humans is limited, apart from the presence of TFH-like cells in blood. We studied the activation and induction of TFH and their role in the anti-influenza antibody response induced by a live-attenuated influenza vaccine (LAIV) in human nasopharynx-associated lymphoid tissue (NALT). TFH activation in adenotonsillar tissues was analyzed by flow cytometry, and anti-hemagglutinin (anti-HA) antibodies were examined following LAIV stimulation of tonsillar mononuclear cells (MNC). Induction of antigen-specific TFH by LAIV was studied by flow cytometry analysis of induced TFH and CD154 expression. LAIV induced TFH proliferation, which correlated with anti-HA antibody production, and TFH were shown to be critical for the antibody response. Induction of TFH from naive T cells by LAIV was shown in newly induced TFH expressing BCL6 and CD21, followed by the detection of anti-HA antibodies. Antigen specificity of LAIV-induced TFH was demonstrated by expression of the antigen-specific T cell activation marker CD154 upon challenge by H1N1 virus antigen or HA. LAIV-induced TFH differentiation was inhibited by BCL6, interleukin-21 (IL-21), ICOS, and CD40 signaling blocking, and that diminished anti-HA antibody production. In conclusion, we demonstrated the induction by LAIV of antigen-specific TFH in human NALT that provide critical support for the anti-influenza antibody response. Promoting antigen-specific TFH in NALT by use of intranasal vaccines may provide an effective vaccination strategy against respiratory infections in humans. IMPORTANCE Airway infections, such as influenza, are common in humans. Intranasal vaccination has been considered a biologically relevant and effective way of immunization against airway infection. The vaccine-induced antibody response is crucial for protection against infection. Recent data from animal studies suggest that one type of T cells, TFH, are important for the antibody response. However, data on whether TFH-mediated help for antibody production operates in humans are limited due to the lack of access to human immune tissue containing TFH. In this study, we demonstrate the induction of TFH in human immune tissue, providing critical support for the anti-influenza antibody response, by use of an intranasal influenza vaccine. Our findings provide direct evidence that TFH play a critical role in vaccine-induced immunity in humans and suggest a novel strategy for promoting such cells by use of intranasal vaccines against respiratory infections