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At least 397 records · Page 22

Single-shot complete spatiotemporal measurement of terawatt laser pulses

In this work, we demonstrate, for the first time, a single-shot, complete spatiotemporal measurement of pulses from a terawatt-scale, multi-stage-amplified, low repetition-rate laser source. The ultrashort pulse electric field, E(x,y,z,t), is spatiotemporally complex due to distortions that accrue from multiple chirped-pulse amplifiers, which requires a complete characterization. Meanwhile, the instability of the laser source introduces field profiles that vary significantly from pulse to pulse, which, together with the low repetition-rate (15 shots/hour), requires the use of a single-shot measurement technique. To accomplish the measurements, we used a wavelength-multiplexed, digital-holographic technique called Spatially and Temporally Resolved Intensity and Phase Evaluation Device: Full Information from a Single Hologram, specially tailored to measure picosecond pulses at a wavelength of about 1 μm. Specifically, individual pulses from the compact multipulse terawatt laser were measured, with up to 0.3 J per shot of energy and ~2 ps pulse durations, at 1052 nm. With these measurements, we characterized several major spatiotemporal distortions that affect the peak intensity at the laser focus, as well as the pulse-shape instability on a shot-to-shot basis. Our technique allows detailed diagnosis of laser pulses (especially high-order spatiotemporal distortions) and provides straightforward four-dimensional animations of pulse propagation to a focus.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Cell degradation quantification—a performance metric-based approach

A safe and reliable battery operation needs effective diagnostic tools. A quantitative failure analysis (FA) to enable cell qualification and quantify its effectiveness for reliable and safe operation of rechargeable Li batteries (RLB) is shown here. The method can identify and quantify potential failure based on the state of charge (SOC) under any operating conditions. A precise and accurate electrochemical analytic diagnosis (eCAD) of 14 rechargeable Li || NMC622 cells of the same build are used as an example. The FA by eCAD can quantitatively decipher good, bad and ugly cells in cycle aging. The cell qualification is based on thermodynamic SOC, not experimental conditions. The method provides a quantitative failure mode and effect analysis (FMEA) to reveal diverse “dead Li” formation that affects the reversibility of the Li anode and charge retention in the cell. This cell qualification method highlights the potential to improve cell quality for safe operation, with strong implications for early fault detection, FA, risk mitigation, state estimation and life prediction for reliable and safe RLB operations

25 ENERGY STORAGE↗

In situ XAS study of the local structure of the nano-Li 2 FeSiO 4 /C cathode

Despite the challenges in achieving its full theoretical capacity of reversible extraction of two Li ions, the Li 2 FeSiO 4 (LFS) cathode shows a remarkable cycling stability once its low electronic conductivity is addressed. By studying the local structure around the iron during electrochemical cycling using in situ x-ray absorption spectroscopy (XAS), it is possible to gain insight into the factors which determine the electrochemical properties of this material. In order to practically perform in situ XAS studies, the charge/discharge of LFS was maximized using two approaches: (a) reducing the particle size of LFS samples from micro-scale to nano-scale in order to reduce the diffusion path for intercalating ions; and (b) applying a conductive coating to each nanoparticle to facilitate electron transfer. A family of LFS materials was synthesized and characterized using x-ray diffraction, and scanning electron microscopy with energy dispersive analysis for structural and morphological analysis, as well as cyclic voltammetry and cycling tests for electrochemical performance diagnosis. This material was then characterized by in situ XAS. The results provide insight into the stable electrochemical performance of LFS and suggest new synthetic routes to reaching the theoretical capacity.

25 ENERGY STORAGE↗

Persistent minimal sequences of SARS-CoV-2

Abstract Motivation Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 14 million cases and more than half million deaths. Given the absence of implemented therapies, new analysis, diagnosis and therapeutics are of great importance. Results Analysis of SARS-CoV-2 genomes from the current outbreak reveals the presence of short persistent DNA/RNA sequences that are absent from the human genome and transcriptome (PmRAWs). For the PmRAWs with length 12, only four exist at the same location in all SARS-CoV-2. At the gene level, we found one PmRAW of size 13 at the Spike glycoprotein coding sequence. This protein is fundamental for binding in human ACE2 and further use as an entry receptor to invade target cells. Applying protein structural prediction, we localized this PmRAW at the surface of the Spike protein, providing a potential targeted vector for diagnostics and therapeutics. In addition, we show a new pattern of relative absent words (RAWs), characterized by the progressive increase of GC content (Guanine and Cytosine) according to the decrease of RAWs length, contrarily to the virus and host genome distributions. New analysis shows the same property during the Ebola virus outbreak. At a computational level, we improved the alignment-free method to identify pathogen-specific signatures in balance with GC measures and removed previous size limitations. Availability and implementation https://github.com/cobilab/eagle. Supplementary information Supplementary data are available at Bioinformatics online.

Pratas, Diogo↗

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗

The lifetime risk and impact of vitiligo across sociodemographic groups: a UK population-based cohort study

Abstract Background Vitiligo is an autoimmune skin disorder characterized by depigmented patches of skin, which can have significant psychological impacts. Objectives To estimate the lifetime incidence of vitiligo, overall, by ethnicity and across other sociodemographic subgroups, and to investigate the impacts of vitiligo on mental health, work and healthcare utilization. Methods Incident cases of vitiligo were identified in the Optimum Patient Care Database of primary care records in the UK between 1 January 2004 and 31 December 2020. The lifetime incidence of vitiligo was estimated at age 80 years using modified time-to-event models with age as the timescale, overall and stratified by ethnicity, sex and deprivation. Depression, anxiety, sleep disturbance, healthcare utilization and work-related outcomes were assessed in the 2 years after vitiligo diagnosis and compared with matched controls without vitiligo. The study protocol for this retrospective observational study was registered with ClinicalTrials.gov (NCT06097494). Results In total, 9460 adults and children were newly diagnosed with vitiligo during the study period. The overall cumulative lifetime incidence was 0.92% at 80 years of age [95% confidence interval (CI) 0.90–0.94]. Cumulative incidence was similar in female (0.94%, 95% CI 0.92–0.97) and male patients (0.89%, 95% CI 0.86–0.92). There were substantial differences in lifetime incidence across ethnic groups, listed by Office for National Statistics criteria [Asian 3.58% (95% CI 3.38–3.78); Black 2.18% (95% CI 1.85–2.50); Mixed/multiple 2.03% (95% CI 1.58–2.47); Other 1.05% (95% CI 0.94–1.17); and White 0.73% (95% CI 0.71–0.76)]. Compared with matched controls, people with vitiligo had an increased risk of depression [adjusted odds ratio (aOR) 1.08, 95% CI 1.01–1.15]; anxiety (aOR 1.19, 95% CI 1.09–1.30); depression or anxiety (aOR 1.10, 95% CI 1.03–1.17); and sleep disturbance [adjusted hazard ratio (aHR) 1.15, 95% CI 1.02–1.31]. People with vitiligo also had a greater number of primary care encounters (adjusted incidence rate ratio 1.29, 95% CI 1.26–1.32) and a greater risk of time off work (aHR 1.15, 95% CI 1.06–1.24). There was little evidence of disparities in vitiligo-related impacts across ethnic subgroups. Conclusions Clinicians should be aware of the markedly increased incidence of vitiligo in people belonging to Asian, Black, Mixed/multiple and Other groups. The negative impact of vitiligo on mental health, work and healthcare utilization highlights the importance of monitoring people with vitiligo to identify those who need additional support.

Eleftheriadou, Viktoria↗

Automatic information extraction from childhood cancer pathology reports

The International Classification of Childhood Cancer (ICCC) facilitates the effective classification of a heterogeneous group of cancers in the important pediatric population. However, there has been no development of machine learning models for the ICCC classification. We developed deep learning-based information extraction models from cancer pathology reports based on the ICD-O-3 coding standard. In this article, we describe extending the models to perform ICCC classification. We developed 2 models, ICD-O-3 classification and ICCC recoding (Model 1) and direct ICCC classification (Model 2), and 4 scenarios subject to the training sample size. We evaluated these models with a corpus consisting of 29206 reports with age at diagnosis between 0 and 19 from 6 state cancer registries. Our findings suggest that the direct ICCC classification (Model 2) is substantially better than reusing the ICD-O-3 classification model (Model 1). Applying the uncertainty quantification mechanism to assess the confidence of the algorithm in assigning a code demonstrated that the model achieved a micro-F1 score of 0.987 while abstaining (not sufficiently confident to assign a code) on only 14.8% of ambiguous pathology reports. Our experimental results suggest that the machine learning-based automatic information extraction from childhood cancer pathology reports in the ICCC is a reliable means of supplementing human annotators at state cancer registries by reading and abstracting the majority of the childhood cancer pathology reports accurately and reliably.

60 APPLIED LIFE SCIENCES↗

Cervical Precancers and Cancers Attributed to HPV Types by Race and Ethnicity: Implications for Vaccination, Screening, and Management

Abstract Background Racial and ethnic variations in attribution of cervical precancer and cancer to human papillomavirus (HPV) types may result in different HPV vaccine protection, screening test coverage, and clinical management. Methods Pooling data from 7 US studies, we calculated the proportional attribution of precancers and cancers to HPV types using HPV DNA typing from diagnosis. All statistical tests were 2-sided. Results For all racial and ethnic groups, most cases of cervical intraepithelial neoplasia grade 3 (CIN3) (84.2%-90.8% of 5526) and squamous cell carcinoma (SCC) (90.4%-93.8% of 1138) were attributed to types targeted by the 9-valent vaccine. A higher proportion of CIN3s were attributed to nonvaccine HPV types among non-Hispanic Black women (15.8%) compared with non-Hispanic Asian or Pacific Islander (9.7%; P = .002), non-Hispanic White (9.2%; P < .001), and Hispanic (11.3%; P = .004) women. The proportion of SCCs attributed to 9-valent types was similar by race and ethnicity (P = .80). A higher proportion of CIN3s were attributed to nonvaccine HPV35 among non-Hispanic Black (9.0%) compared with non-Hispanic Asian or Pacific Islander (2.2%), non-Hispanic White (2.5%), and Hispanic (3.0%; all P < .001) women. Compared with CIN3, the proportion of SCCs attributed to HPV35 among non-Hispanic Black women (3.2%) was lower and closer to other groups (0.3%-2.1%; P = .70). Conclusion The 9-valent HPV vaccine will prevent nearly all cervical precancers and invasive cancers among major racial and ethnic groups in the United States. Adding HPV35 to vaccines could prevent a small percentage of CIN3s and SCCs, with greater potential impact for CIN3s among Black women. HPV screening tests target high-risk HPV types, including HPV35. Future genotyping triage strategies could consider the importance of HPV35- and other HPV16-related types.

Mix, Jacqueline (ORCID:0000000164187706)↗

A comparative analysis of the chemical compositions of Gaia-Enceladus/Sausage and Milky Way satellites using APOGEE

ABSTRACT We use data from the 17th data release of the Apache Point Observatory Galactic Evolution Experiment (APOGEE 2) to contrast the chemical composition of the recently discovered Gaia Enceladus/Sausage system (GE/S) to those of 10 Milky Way (MW) dwarf satellite galaxies: LMC, SMC, Boötes I, Carina, Draco, Fornax, Sagittarius, Sculptor, Sextans, and Ursa Minor. Our main focus is on the distributions of the stellar populations of those systems in the [Mg/Fe]–[Fe/H] and [Mg/Mn]–[Al/Fe] planes, which are commonly employed in the literature for chemical diagnosis and where dwarf galaxies can be distinguished from in situ populations. We show that, unlike MW satellites, a GE/S sample defined purely on the basis of orbital parameters falls almost entirely within the locus of ‘accreted’ stellar populations in chemical space, which is likely caused by an early quenching of star formation in GE/S. Due to a more protracted history of star formation, stars in the metal-rich end of the MW satellite populations are characterized by lower [Mg/Mn] than those of their GE/S counterparts. The chemical compositions of GE/S stars are consistent with a higher early star formation rate (SFR) than MW satellites of comparable and even higher mass, suggesting that star formation in the early universe was strongly influenced by other parameters in addition to mass. We find that the direction of the metallicity gradient in the [Mg/Mn]–[Al/Fe] plane of dwarf galaxies is an indicator of the early SFR of the system.

79 ASTRONOMY AND ASTROPHYSICS↗

Directed evolution expands CRISPR–Cas12a genome-editing capacity

CRISPR-Cas12a enzymes are versatile RNA-guided genome-editing tools with applications encompassing viral diagnosis, agriculture, and human therapeutics. However, their dependence on a 5'-TTTV-3' protospacer adjacent motif (PAM) next to DNA target sequences restricts Cas12a's gene targeting capability to only ∼1% of a typical genome. To mitigate this constraint, we used a bacterial-based directed evolution assay combined with rational engineering to identify variants of Lachnospiraceae bacterium Cas12a with expanded PAM recognition. The resulting Cas12a variants use a range of noncanonical PAMs while retaining recognition of the canonical 5'-TTTV-3' PAM. In particular, biochemical and cell-based assays show that the variant Flex-Cas12a utilizes 5'-NYHV-3' PAMs that expand DNA recognition sites to ∼25% of the human genome. With enhanced targeting versatility, Flex-Cas12a unlocks access to previously inaccessible genomic loci, providing new opportunities for both therapeutic and agricultural genome engineering.

Ma, Enbo↗

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

Abstract Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

99 GENERAL AND MISCELLANEOUS↗

DNA Sequence-Based Identification of Fusarium : A Work in Progress

Accurate species-level identification of an etiological agent is crucial for disease diagnosis and management because knowing the agent’s identity connects it with what is known about its host range, geographic distribution, and toxin production potential. This is particularly true in publishing peer-reviewed disease reports, where imprecise and/or incorrect identifications weaken the public knowledge base. This can be a daunting task for phytopathologists and other applied biologists that need to identify Fusarium in particular, because published and ongoing multilocus molecular systematic studies have highlighted several confounding issues. Paramount among these are: (i) this agriculturally and clinically important genus is currently estimated to comprise more than 400 phylogenetically distinct species (i.e., phylospecies), with more than 80% of these discovered within the past 25 years; (ii) approximately one-third of the phylospecies have not been formally described; (iii) morphology alone is inadequate to distinguish most of these species from one another; and (iv) the current rapid discovery of novel fusaria from pathogen surveys and accompanying impact on the taxonomic landscape is expected to continue well into the foreseeable future. To address the critical need for accurate pathogen identification, our research groups are focused on populating two web-accessible databases (FUSARIUM-ID v.3.0 and the nonredundant National Center for Biotechnology Information nucleotide collection that includes GenBank) with portions of three phylogenetically informative genes (i.e., TEF1, RPB1, and RPB2) that resolve at or near the species level in every Fusarium species. The objectives of this Special Report, and its companion in this issue ( Torres-Cruz et al. 2022 ), are to provide a progress report on our efforts to populate these databases and to outline a set of best practices for DNA sequence-based identification of fusaria.

Plant Sciences↗

Locoregional Treatment of Glioblastoma With Targeted α Therapy: [213Bi]Bi-DOTA–Substance P Versus [225Ac]Ac-DOTA–Substance P—Analysis of Influence Parameters

Background Glioblastoma (GB) is the most malignant primary brain tumor. Therefore, introduction of new treatment options is critically important. The aim of this study was to assess local treatment with α emitters [ 213 Bi]Bi-DOTA–substance P (SP) and [ 225 Ac]Ac-DOTA-SP. Methods Treatment was performed as salvage therapy in patients with recurrent primary and secondary GB. [ 213 Bi]Bi-DOTA-SP with injected activity 1.85 GBq per cycle was used in 20 primary (48.2 ± 11.8 years old) and in 9 secondary (38.8 ± 10.8 years old) GB patients and [ 225 Ac]Ac-DOTA-SP in 15 primary (45.1 ± 9.9 years old) and in 6 secondary (37.8 ± 6.4 years old) GB patients with a dose escalation scheme (10, 20, and 30 MBq). Results Local treatment with [ 213 Bi]Bi-DOTA-SP and [ 225 Ac]Ac-DOTA-SP was well tolerated with only few adverse effects. There was no statistically significant difference between [ 213 Bi]Bi-DOTA-SP and [ 225 Ac]Ac-DOTA-SP groups in survival parameters. For primary GB, survival parameters of patients treated with [ 213 Bi]Bi-DOTA-SP and [ 225 Ac]Ac-DOTA-SP were as follows(in months): progression-free survival time, 2.7 versus 2.4; OS-d (overall survival from time of diagnosis to death from any cause), 23.6 versus 21.0; OS-t (overall survival from the start of treatment to death from any cause), 7.5 versus 5.0; and OS-r (overall survival from recurrence in primary tumors to death from any cause), 10.9 versus 12.0. Survival parameters of secondary GB patients treated with [ 213 Bi]Bi-DOTA-SP and [ 225 Ac]Ac-DOTA-SP were as follows (in months): progression-free survival time, 5.8 versus 2.4; OS-d, 52.3 versus 65.0; OS-t, 16.4 versus 16.0; and OS-c (overall survival from conversion into secondary GB multiforme to death from any cause), 18.4 versus 36.0. Conclusions The similarity results of 213 Bi or 225 Ac may suggest that the local treatment of brain tumors can be greatly simplified. The experience to date shows that local radioisotope treatment of brain tumors requires further dosimetry studies, taking into account the complexity of biological processes.

Radiology, Nuclear Medicine & Medical Imaging↗

Deep medical image analysis with representation learning and neuromorphic computing

Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

60 APPLIED LIFE SCIENCES↗

Genomic reconstruction of Bacillus anthracis from complex environmental samples enables high-throughput identification and lineage assignment in Pakistan

Bacillus anthracis, the causative agent of anthrax, is a highly virulent zoonotic pathogen primarily affecting domesticated and wild herbivores. Human exposure to B. anthracis is primarily through contact with infected animals or contaminated animal products. In Pakistan, where livestock vaccines are largely unavailable and infected carcasses are often disposed of improperly, the risk to humans, wildlife and livestock is significant. Currently, the diagnosis of anthrax infections and outbreak tracing necessitates the isolation and culturing of B. anthracis, a process that requires BSL-3 facilities. In this study, we show that positive identification, genome reconstruction and lineage assignment can be accomplished using bioinformatic analysis of DNA extracted directly from environmental samples that would otherwise provide the starting material for isolation and culturing. This approach does not require laboratory target enrichment as is necessary for other pathogens, due in part to the extremely high bacterial load in the bloodstream in the deceased animals. Using these methods, we greatly expand the knowledge of endemic B. anthracis in Pakistan. We provide the first reference B. anthracis genomes from Pakistan since the 1970s and identify A.Br.014 Aust94 as a minor circulating sublineage alongside the dominant A.Br.047 Vollum. Future work will focus on the limits of detection and will determine if this bioinformatic method can be expanded more broadly for B. anthracis or other pathogens to replace typical culture-based methods.

A.Br.047 Vollum↗

C 13 ( n , 2 n γ ) C 12 γ -ray production in the 14–16 MeV incident neutron energy range

We report γ-ray emission from 12 C and 13 C samples irradiated with deuterium-tritium fusion neutrons was experimentally measured at the Omega Laser Facility and at the Ohio University Edwards Accelerator Laboratory. The intent of these measurements was to determine the feasibility of using 13 C-based plastic ablators with embedded 12 C layers for “dark mix” diagnosis of inertial confinement fusion implosions. Spectrally resolved measurements at Ohio University identified significant 4.44-MeV γ-ray emission from the 13 C(n, 2nγ) 12 C-L1 reaction channel. The recorded 4.44-MeV 13 C signal was compared against emission from an identically irradiated 12 C target with known 12 C(n, n'γ) 12 C-L1 cross section, which resulted in an average 13 C(n, 2nγ) 12 C-L1 cross section of 117 ± 17 mb over the incident neutron energy distribution range from 14.4 to 15.8 MeV. Integrated 13C γ -ray signals above 2.9 MeV recorded with the Gas Cherenkov Detector at Omega exceeded MCNP6.1 predictions by a factor of 3. The additional signal was attributed to 4.44-MeV γs resulting in an inferred 13 C(n, 2nγ) 12 C-L1 cross section of 95 ± 11 mb at 14.1-MeV average incident neutron energy. As a result, the 13 C-based “dark mix” diagnostic concept was deemed infeasible.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Impact of strong magnetization in cylindrical plasma implosions with applied B-field measured via x-ray emission spectroscopy

Magnetization is a key strategy for enhancing inertial fusion performance, though accurate characterization of magnetized dense plasmas is needed for a better comprehension of the underlying physics. Measured spectra from imploding Ar-doped D 2 -filled cylinders at the OMEGA laser show distinctive features with and without an imposed magnetic field. A multizone spectroscopic diagnosis leads to quantitative estimates of the plasma conditions, namely revealing a 50% core temperature rise at half mass density when a 30-T seed field is applied. Concurrently, experimental spectra align well with predictions from extended-magnetohydrodynamics simulations, providing strong evidence that the attained core conditions at peak compression are consistent with the impact of a 10-kT compressed field. These results pave the way for the validation of magnetized transport models in dense plasmas and for future magnetized laser implosion experiments at a larger scale. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Collaborative Fault Tolerant Control of Non-Signalized Intersections for Connected and Autonomous Vehicles

With the potential of increased penetration of connected and autonomous vehicles (CAVs), intersectional signal control faces new challenges in terms of its operation and implementation. One possibility is to fully make use of the communication capabilities of CAVs so that intersectional signal control can be realized by CAVs alone – this leads to non-signalized intersectional operation for traffic networks in urban areas. In this paper, the state-of-the-art on collaborative fault tolerant control schemes for complex systems will be briefly described. This is then followed by the formulation of operational fault tolerant control that realizes the collaborative fault tolerance functionality at CAVs operational level in response to possible individual vehicle faults, where detailed modelling using vehicle movement dynamics will be described together with the construction of fast fault diagnosis and a collaborative fault tolerant control algorithm. A simple example will be given as well to demonstrate the proposed algorithm together with the discussions on other issues such as randomness of the system, communication errors and full energy consideration. These leads to several future directions of the research for the traffic flow control of non-signalized intersections with 100% penetration of CAVs.

Wang, Hong↗