bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of recurrent neural networks considering maintenance to predict urban road performance in Beijing, China
Authors: Deng Yutong , Li Feng , Zhou Siqi , Zhang Song , Yang Yang , Zhang Qiang , Li Yanfei 
Published: July 2023

A correct understanding of the pavement performance change law forms the premise of the scientific formulation of maintenance decisions. This paper aims to develop a predictive model taking into account the costs of different types of maintenance works that reflects the continuous true usage performance of the pavement. The model proposed in this study was trained on a dataset containing five-year maintenance work data on urban roads in Beijing with pavement performance indicators for the corresponding years. The same roads were matched and combined to obtain a set of sequences of pavement performance changes with the features of the current year; with the recurrent-neural-network-based long short-term memory (LSTM) network and gate recurrent unit (GRU) network, the prediction accuracy of highway pavement performance on the test set was significantly increased. The prediction result indicates that the generalization ability of the improved recurrent neural network model is satisfactory, with the
R
2
achieving 0.936, and of the two models the GRU model is more efficient, with an accuracy that reaches almost the same level as LSTM but with the training convergence time reduced to 25 s. This study demonstrates that data generated by the work of maintenance units can be used effectively in the prediction of pavement performance.

This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
A machine learning-based approach to assess impacts of autonomous vehicles on pavement roughness
Authors: Chen Chenxi , Song Yang , Wang Yizhuang David , Hu Xianbiao , Liu Jenny 
Published: July 2023

Studies have been initiated to investigate the potential impact of connected and automated vehicles (CAVs) on transportation infrastructure. However, most existing research only focuses on the wandering patterns of CAVs. To bridge this gap, an apple-to-apple comparison is first performed to systematically reveal the behavioural differences between the human-driven vehicle (HDV) and CAV trajectory patterns for the first time, with the data collected from the camera-based next generation simulation dataset and autonomous driving co-simulation platform, CARLA and SUMO, respectively. A gradient boosting-based ensemble learning model for pavement performance (i.e. international roughness index) prediction is then developed with the input features including three driving pattern features, namely, lateral wandering deviation, longitudinal car-following distance and driving speed, plus 20 other context variables. A total of 1707 observations is extracted from the long-term pavement performance database for model training purposes. The result indicates that the trained model can accurately predict pavement deterioration and that CAV deteriorates pavement faster than HDV by 8.1% on average. According to the sensitivity analysis, CAV deployment will create a greater impact on the younger pavements, and the rate of pavement deterioration is found to be stable under light traffic, whereas it will increase under congested traffic.
This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
Authors: Hou Yue , Dong Qiao , Wang Dawei , Liu Jenny 
Published: July 2023

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyse the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material-based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyse the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multi-scale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc. In the future, AI techniques will be more effective and promising for data collection, transmission, fusion, mining and analysis, which will help engineers quickly detect, analyse and finally prevent the engineering failures of transportation infrastructure and materials. This theme issue presents the latest developments of AI in failure analysis of transportation infrastructure and materials.This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Passenger flow anomaly detection in urban rail transit networks with graph convolution network–informer and Gaussian Bayes models
Authors: Liu Bing , Ma Xiaolei , Tan Erlong , Ma Zhenliang 
Published: July 2023

Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in managing surging demand and informing effective operations planning and controls in the network. Existing studies have primarily focused on identifying the source of anomalies at a single station by analysing the time-series characteristics of passenger flow. However, they ignored the high-dimensional and complex spatial features of passenger flow and the dynamic behaviours of passengers in URTNs during anomaly detection. This article proposes a novel anomaly detection methodology based on a deep learning framework consisting of a graph convolution network (GCN)–informer model and a Gaussian naive Bayes model. The GCN–informer model is used to capture the spatial and temporal features of inbound and outbound passenger flows, and it is trained on normal datasets. The Gaussian naive Bayes model is used to construct a binary classifier for anomaly detection, and its parameters are estimated by feeding the normal and abnormal test data into the trained GCN–informer model. Experiments are conducted on a real-world URTN passenger flow dataset from Beijing. The results show that the proposed framework has superior performance compared to existing anomaly detection algorithms in detecting network-level passenger flow anomalies.
This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining
Authors: Li Xin , Xue Yiguo , Li Zhiqiang , Kong Fanmeng , Zhou Binghua 
Published: July 2023

Due to improper operation of the shield construction process and unknown geological surveys, shield construction faces many risks in passing through complex strata, among which the excavation face instability is the most serious, potentially leading to disastrous accidents. To address these issues, this research focuses on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, an analytical formula for the limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is used to assess the excavation face stability quantitatively. Then the rough set algorithm is used to analyse the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The back propagation (BP) neural network is used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10−4is established. The prediction performance of BP is verified by comparison with the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability.This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
Global Safety Assessment of Adverse Events of Special Interest Following 2 Years of Use and 772 Million Administered Doses of mRNA-1273
Authors: Urdaneta Veronica , Esposito Daina B. , Dharia Priyadarshani , Moraga Margot Stam , Anteyi Kate , Oduyebo-Omotosho Titi , Rossi Melissa , Burton Paul , Vega José M. , Dawson Rachel , Straus Walter 
Published: July 2023

AbstractBackgroundThe large-scale use of mRNA COVID-19 vaccines during the pandemic was associated with enhanced safety monitoring to ensure accurate and timely review of safety. We reviewed the mRNA-1273 (original strain) safety profile following 2 years of use (>772 million administered doses), primarily focusing upon predefined safety topics (ie, adverse events of special interest [AESI]) proposed in advance of COVID-19 vaccine use.MethodsCumulative mRNA-1273 safety data from spontaneous adverse event (AE) cases reported to the Moderna, Inc., global safety database (GSDB) between December 18, 2020, and December 17, 2022, were included. Reporting rates of AESI were calculated per 1 million doses of mRNA-1273 administered. Observed-to-expected (OE) ratios were computed by comparing observed rates of AESI to the background/expected rate for these events to evaluate potential associations with mRNA-1273.ResultsThere were 658,767 identified case reports, associated with 2,517,669 AEs. Most AEs were non-serious (83.4%); 0.7% were fatal. AESI represented 13.7% of all AEs, with reporting rates for most AESI below the expected background incidence. Exceptions included anaphylaxis (OE ratio 3 days after vaccination, 2.19 [95% CI, 2.02-2.37]), myocarditis (OE ratio 7 days after vaccination, 1.41 [1.32-1.51]; among men aged 12-40 years, 9.75 [7.74-12.3]; and individuals aged 12-40 years, 3.51 [3.19-3.86]), and pericarditis (OE ratio 7 days after vaccination in individuals aged 12-40 years, 2.54 [2.16-2.99]).ConclusionsWith the exceptions of anaphylaxis, myocarditis, and pericarditis, this safety analysis of mRNA-1273 did not find evidence to suggest an increased risk for AESI identified for enhanced monitoring ahead of COVID-19 vaccine use.
ADMINISTRATION OF LEVODOPA IN A PARKINSON’S PATIENT USING A NOVEL ORAL AND MAXILLOFACIAL ROUTE – A FIRST IN HUMAN STUDY
Authors: Thirunavukarasu Suresh , Ramanan Balasubramanian Bala Venkata , Suresh Sathya Krishnan , Antonisamy Vincent Jayakumar , Varadharaj Devi , Shanmugam Paranjothi , Verma Kavita , Elumalai Canmany , Selvakumar Gladson , Elumalai Ahila , Prabahar Lydia , Janardhanan Hridwik Adiyeri , UR Anoop 
Published: July 2023

ABSTRACTParkinson’s disease is characterized by loss of nigrostriatal dopaminergic neurons in the brain. Dopamine cannot be administered systemically as a treatment because dopamine does not cross the blood-brain-barrier. Oral levodopa is the gold standard till date. Currently, in patients who show poor response to oral levodopa, the drug can be delivered through alternate routes like inhalation and continuously by intestinal and subcutaneous routes. In this report, a novel oral and maxillofacial route was used for the first time in the world to administer levodopa in a Parkinson’s patient and its efficacy was compared with the oral route of administration.
Validity and reliability of an app-based medical device to empower individuals in evaluating their physical capacities
Authors: Mazéas Alexandre , Blond Marine , Chalabaev Aïna , Duclos Martine 
Published: July 2023

AbstractBackgroundCardiorespiratory fitness and muscle strength are valid markers of health and strong predictors of mortality and morbidity. The tests used to measure these variables require in-person visits with specialized equipment and trained personnel – leading to organizational constraints both for patients and hospitals, and making them difficult to implement at a large scale. In this context, technologies embedded in smartphones offer new opportunities to develop remote tests.ObjectivesThis study aimed to test the validity and reliability ofMediEval, a newly developed app-based medical device that allows individuals to perform the 6-minute walk test (6MWT) and the 30-second sit-to-stand (30s-STS) test on their own using GPS signal and camera detection with a skeleton extraction algorithm.MethodsA total of 53 healthy adults performed the two tests in three different sessions to determine the intra- and inter-day reproducibility. Test validity was assessed by comparing the results obtained from the app to gold standard measures. Pearson correlations and concordance correlation coefficients, the relative measurement error, intraclass correlation coefficients, the standard error of measure and the minimal detectable change were computed for each test.ResultsThe results revealed high to excellent validity of the app in comparison to gold standards (ρ=0.84 for the 6MWT andρ=0.99 for the 30s-STS test) with low relative measurement error. The mean differences between the app and the gold standard measures were 8.96m for the 6MWT and 0.28 repetition for the 30s-STS test. Both tests had good test-retest reliability (ICCs = 0.77). The minimal detectable changes were respectively 97.56 meters for the 6MWT and 7.37 repetitions for the 30s-STS test.DiscussionTheMediEvalmedical device proposes valid and reproducible measures of the 6MWT and the 30s-STS test. This device holds promise for monitoring the physical activity of large epidemiologic cohorts while refining patient experience and improving the scalability of the healthcare system. Considering minimal detectable change values, it may be important to ask participants to perform several tests and average them to improve accuracy. Future studies in clinical context are needed to evaluate the responsiveness and the smallest detectable changes of the device for specific populations with chronic diseases.Data, code & supplemental materialshttps://osf.io/4hzke/
Neural Correlates of Behavioral Recovery Following Ultrasonic Thalamic Stimulation in Chronic Disorders of Consciousness
Authors: Cain Josh A. , Spivak Norman M. , Coetzee John P. , Crone Julia S. , Johnson Micah A. , Lutkenhoff Evan S. , Real Courtney , Buitrago-Blanco Manuel , Vespa Paul M. , Schnakers Caroline , Monti Martin M. 
Published: July 2023

AbstractBackgroundTreatments aimed at hastening recovery from disorders of consciousness (DOC; e.g., coma, the vegetative state) have lagged behind a rapidly advancing science of these conditions. In part, this is due to the difficulty in selectively targeting the many deep regions of the brain known to be key for recovery from DOC. The (re)emergence of low intensity focused ultrasound (LIFU) neuromodulation addresses this gap by providing a non-invasive, safe, and relatively low-cost means to exert neuromodulatory effects, anywhere in the brain, with relatively high spatial precision.MethodsAs part of this first-in-man clinical trial, a cohort of 10 patients with chronic DOC underwent two sessions of MR-guided thalamic LIFU, with concomitant functional neuroimaging, one week apart. Behavioral responsiveness, measured with the Coma Recovery Scale Revised (CRS-R), was assessed at multiple time-points both before and after each LIFU session. Changes in clinical score before and after each session were compared within subjects.ResultsThis convenience sample of sample of chronic DOC patients included, at entry, 4 Minimally Conscious State plus (MCS+), 4 Minimally Conscious State minus (MCS-) and 2 Vegetative State (VS) patients (6 male; mean age = 39.1, mean time since injury = 56.75 months; 4 anoxic and 6 traumatic injuries). We find a significant linear increase over time in CRS-R total score with thalamic LIFU exposure. Functional imaging reveals changes in brain-wide activity and thalamo-cortical connectivity of the targeted thalamus (but not the contralateral, non-targeted, thalamus), during LIFU administration. Strikingly, these effects are associated with the degree of behavioral recovery observed following exposure.DiscussionCollectively, these results are the first to suggest the efficacy of thalamic LIFU for the treatment of chronic DOC and extend our previous investigations in acute DOC populations. Indeed, results from both cohorts support the safety, feasibility, and preliminary efficacy of LIFU, as evaluated by gold-standard clinical assessments. Moreover, imaging results in both datasets provide a convergent biological link uniting neuromodulatory thalamic LIFU and the observed behavioral recovery. These first-in-man findings provide a key foundation to motivate further exploration of this technique (e.g., LIFU parameterization, optimal number and timing of exposures) and invite a sham-control clinical trial, in a larger cohort, to assess, in a blinded fashion, the technique’s efficacy.Clinical Trial number, date of submission, date of first enrollment, registration link:NCT02522429August 13, 2015March 10, 2016https://clinicaltrials.gov/ct2/show/NCT02522429
Coding Inequity: Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in Healthcare
Authors: Zack Travis , Lehman Eric , Suzgun Mirac , Rodriguez Jorge A. , Celi Leo Anthony , Gichoya Judy , Jurafsky Dan , Szolovits Peter , Bates David W. , Abdulnour Raja-Elie E. , Butte Atul J. , Alsentzer Emily 
Published: July 2023

AbstractBackgroundLarge language models (LLMs) such as GPT-4 hold great promise as transformative tools in healthcare, ranging from automating administrative tasks to augmenting clinical decision- making. However, these models also pose a serious danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care.MethodsUsing the Azure OpenAI API, we tested whether GPT-4 encodes racial and gender biases and examined the impact of such biases on four potential applications of LLMs in the clinical domain—namely, medical education, diagnostic reasoning, plan generation, and patient assessment. We conducted experiments with prompts designed to resemble typical use of GPT-4 within clinical and medical education applications. We used clinical vignettes from NEJM Healer and from published research on implicit bias in healthcare. GPT-4 estimates of the demographic distribution of medical conditions were compared to true U.S. prevalence estimates. Differential diagnosis and treatment planning were evaluated across demographic groups using standard statistical tests for significance between groups.FindingsWe find that GPT-4 does not appropriately model the demographic diversity of medical conditions, consistently producing clinical vignettes that stereotype demographic presentations. The differential diagnoses created by GPT-4 for standardized clinical vignettes were more likely to include diagnoses that stereotype certain races, ethnicities, and gender identities. Assessment and plans created by the model showed significant association between demographic attributes and recommendations for more expensive procedures as well as differences in patient perception.InterpretationOur findings highlight the urgent need for comprehensive and transparent bias assessments of LLM tools like GPT-4 for every intended use case before they are integrated into clinical care. We discuss the potential sources of these biases and potential mitigation strategies prior to clinical implementation.
Validation of a 9-item Perceived Suicide Awareness Scale (PSAS-9) for adolescents
Authors: Baggio Stéphanie , Nsingi Neslie , Iglesias Katia , Sapin Marlène 
Published: July 2023

AbstractIntroductionRobust empirical data on suicide awareness are needed, to better plan and evaluate suicide prevention interventions. However, there is a lack of validated measures of suicide awareness. This is especially true for perceived suicide awareness, which focuses on perceived knowledge about suicide, willingness, and confidence to talk about suicide and get help. This study aimed to validate a measure of perceived suicide awareness.MethodsWe re-used data from a suicide prevention trial conducted in Swiss secondary schools (n=366). Baseline and one-month follow-up data were used to validate the scale. The main measure was an initial 14-item perceived suicide awareness scale (PSAS). Perceived knowledge of help-seeking resources, suicide-related knowledge, and support networks were used to assess convergent validity.ResultsA nine-item version, the PSAS-9, showed satisfactory psychometric properties, including high internal consistency (α=.78), acceptable test-retest (r=.68), and a one-factor structure explaining 95% of the variance. The convergent validity was acceptable (.19≤r≤.40). The PSAS-9 was not correlated with suicide-related knowledge (r=.02).ConclusionThis study was an important step towards validating a perceived suicide awareness scale, distinct from suicide-related knowledge, to be used in future studies focused on suicide prevention, and, more generally, studies interested in measuring suicide awareness.
Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma
Authors: Bond Kamila M. , Curtin Lee , Hawkins-Daarud Andrea , Urcuyo Javier C. , De Leon Gustavo , Singleton Kyle W. , Afshari Ariana E. , Paulson Lisa E. , Sereduk Christopher P. , Smith Kris A. , Nakaji Peter , Baxter Leslie C. , Patra Devi Prasad , Gustafson Michael P. , Dietz Allan B. , Zimmerman Richard S. , Bendok Bernard R. , Tran Nhan L. , Hu Leland S. , Parney Ian F. , Rubin Joshua B. , Swanson Kristin R. 
Published: July 2023

AbstractBACKGROUNDGlioblastoma is an extraordinarily heterogeneous tumor, yet the current treatment paradigm is a “one size fits all” approach. Hundreds of glioblastoma clinical trials have been deemed failures because they did not extend median survival, but these cohorts are comprised of patients with diverse tumors. Current methods of assessing treatment efficacy fail to fully account for this heterogeneity.METHODSUsing an image-based modeling approach, we predicted T-cell abundance from serial MRIs of patients enrolled in the dendritic cell (DC) vaccine clinical trial. T-cell predictions were quantified in both the contrast-enhancing and non-enhancing regions of the imageable tumor, and changes over time were assessed.RESULTSA subset of patients in a DC vaccine clinical trial, who had previously gone undetected, were identified as treatment responsive and benefited from prolonged survival. A mere two months after initial vaccine administration, responsive patients had a decrease in model-predicted T-cells within the contrast-enhancing region, with a simultaneous increase in the T2/FLAIR region.CONCLUSIONSIn a field that has yet to see breakthrough therapies, these results highlight the value of machine learning in enhancing clinical trial assessment, improving our ability to prospectively prognosticate patient outcomes, and advancing the pursuit towards individualized medicine.