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Abstract Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Motor vehicle crashes are the leading cause of the death of teenagers in the United States. Young drivers have shown a higher propensity to get involved in crashes due to using a cellphone while driving, breaking the speed limit, and reckless driving. This study analyzed motor vehicle crashes involving young drivers using New Jersey crash data. Specifically, four years of crash data (2016–2019) were gathered and analyzed. Different machine learning (ML) methods, such as Random Forest, Light GBM, Catboost, and XGBoost, were used to predict the injury severity. The performance of the models was evaluated using accuracy, precision, and recall scores. In addition, interpretable ML techniques like sensitivity analysis and Shapley values were conducted to assess the most influential factors' impacts on young driver-related crashes. The results revealed that XGBoost performed better than Random Forest, CatBoost, and LightGBM models in crash severity prediction. Results from the sensitivity analysis showed that multi-vehicle crashes, angular crashes, crashes at intersections, and dark-not-lit conditions had increased crash severity. A partial dependence plot of SHAP values revealed that speeding in clear weather had a higher likelihood of injury crashes, and multi-vehicle crashes at the intersection had more injury crashes. We expect that the results obtained from this study will help policymakers and practitioners take appropriate countermeasures to improve the safety of young drivers in New Jersey.

Monkeypox (Mpox), an uncommon zoonotic Orthopoxvirus, is commonly manifested by blisters on the skin and has a mortality rate of approximately 0-10%. Approximately two decades after the cessation of global smallpox vaccination, the number of confirmed cases of Mpox has been growing, making it the most common Orthopoxvirus infection. Therefore, in this narrative review, we aimed to shed light on recent advancements in the pathophysiology, transmission routes, epidemiology, manifestations, diagnosis, prevention, and treatment of Mpox, as well as the application of artificial intelligence (AI) methods for predicting this disease. The clinical manifestations of Mpox, including the onset of symptoms and dermatologic characteristics, are similar to those of the infamous smallpox, but Mpox is clinically milder. Notably, a key difference between smallpox and Mpox is the high prevalence of lymphadenopathy. Human-to-human, animal-to-human, and animal-to-animal transmission are the three main pathways of Mpox spread that must be considered for effective prevention, particularly during outbreaks. PCR testing, as the preferred method for diagnosing Mpox infection, can enhance early detection of new cases and thereby improve infection control measures. JYNNEOS and ACAM2000 are among the vaccines most commonly recommended for the prevention of Mpox. Brincidofovir, Cidofovir, and Tecovirimat are the primary treatments for Mpox cases. Similar to other viral infections, the best approach to managing Mpox is prevention. This can, in part, be achieved through measures such as reducing contact with individuals displaying symptoms, maintaining personal safety, and adhering to practices commonly used to prevent sexually transmitted infections.

Wrong-way driving (WWD) has been a long-lasting issue for transportation agencies and law enforcement, since it causes pivotal threats to road users. Notwithstanding being rare, crashes occurring due to WWD are more severe than other types of crashes. In order to analyze WWD crashes, there is a need to obtain WWD incidents or crash data. However, it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash databases. It often involves large man-hours to review hardcopy of crash narratives in the police reports. Otherwise, it may cause an overestimation or underestimation of WWD crash frequencies. To fill this gap, the present study, as the first-of-its-kind, aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning methods. Recently, Bidirectional Encoder Representations from Transformers (BERT) models have shown promising results in natural language processing. In this study, we implemented the BERT model as well as five conventional classification algorithms, including Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Single Layer Perceptron (SLP) to classify crash report narratives as actual WWD and non-WWD crashes. Cross-validation and different performance metrics were used to evaluate the performance of each classification algorithm. Results indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%. The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.

Introduction: Small bowel obstruction (SBO) is known as a common cause of acute abdominal complaints in the emergency department (ED). The modality of choice for the diagnosis of SBO has not yet been established. This systematic review and meta-analysis aimed to investigate the accuracy of ultrasonography for the diagnosis of SBO. Methods: Systematic search was performed on five electronic databases including Medline, Scopus, Web of Sciences, Embase, and Cochrane Library, and the retrieval period was from the inception of each database to November 2023. The quality of the included studies were investigated using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The pooled values of diagnostic characteristics for ultrasonography were estimated using meta-Disc and Stata statistical software. Results: Twenty-one studies with a total of 1977 patients were included in the meta-analysis. The pooled estimate for sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary ROC curve of ultrasonography for diagnosing SBO were 0.93 (95% CI: 0.91–0.95), 0.8 (95% CI: 0.77–0.83), 5.69 (95% CI: 3.64–8.89), 0.1 (95% CI: 0.07–0.16), 83.51 (95% CI: 18.12–182.91) and 0.96, respectively. Conclusion: The findings of this meta-analysis showed that the utilization of ultrasonography holds promise as a diagnostic imaging for SBO with high accuracy. However, additional worldwide studies are essential to get more evidence on the value of ultrasonography for the diagnosis of SBO.

Roadway departure (RwD) crashes are significant safety concerns, especially at horizontal curves. The design of these curves plays a crucial role in mitigating RwD crashes. Thus, a thorough understanding of the interaction between driver behavior, vehicle automation, and geometric design is vital. Substantive safety, which emphasizes the inherent safety in a road's design and function, serves as the foundation of our approach. Building on this, the study employs a safe system approach to investigate the performance of horizontal curves under both non-automated and partially automated conditions, using a reliability-based analysis focusing on Stopping Sight Distance as the primary driver demand. Factors including Perception-Brake Time and Take-Over Time for automated vehicles are examined. The analysis covers horizontal curves, characterized by their geometric design and crash data. Our findings highlight a shift in the performance of horizontal curves under automation, emphasizing the need to consider automation in roadway design within the safe system approach. This study demonstrates how a reliability-based analysis can guide designers in making informed decisions regarding the geometric design of horizontal curves to reduce RwD crashes. To enhance transportation safety in the era of increasing automation, ongoing exploration of the relationships between driver behavior, automation, and road design is indispensable.

The present work proposes a simulation-based Bayesian method for parameter estimation and fragility model selection for mutually exclusive and collectively exhaustive (MECE) damage states. This method uses an adaptive Markov chain Monte Carlo simulation (MCMC) based on likelihood estimation using point-wise intensity values. It identifies the simplest model that fits the data best, among the set of viable fragility models considered. The proposed methodology is demonstrated for empirical fragility assessments for two different tsunami events and different classes of buildings with varying numbers of observed damage and flow depth data pairs. As case studies, observed pairs of data for flow depth and the corresponding damage level from the South Pacific tsunami on 29 September 2009 and the Sulawesi–Palu tsunami on 28 September 2018 are used. Damage data related to a total of five different building classes are analysed. It is shown that the proposed methodology is stable and efficient for data sets with a very low number of damage versus intensity data pairs and cases in which observed data are missing for some of the damage levels.

Background: Up-regulation of ribosome biogenesis encodes the factors related to carcinogenesis. It has been shown that the nucleus diameter and number of nucleoli increase from normal mucosa to oral squamous cell carcinoma (OSCC). The relationship between nuclear parameters and lymph node involvement in OSCC has not been established, yet. The aim of this study was to evaluate the nuclear parameters comprising nucleoli count and nucleus: nucleoli ratio in relation to regional lymph node involvement in OSCC. Methods: Thirty-four formalin-fixed, paraffin embedded sections from different histopathologic grades of OSCC were stained with methyl green-pyronin. Mean number of nucleoli and nucleus: nucleoli ratio were calculated in 100 tumor cells from 10 random selected fields and compared based on lymph node involvement. Results: Nucleoli count in cases with metastasis to regional lymph nodes was not significantly different from that in cases without regional lymph nodes involvement (P = 0.29). The difference of nucleus: nucleoli ratio in cases with and without lymph nodes involvement were not significant (P=0.52). No significant correlation was found between the nucleoli count and lymph node involvement (r=0.08, P=0.78). The correlation between nucleus: nucleoli ratio and lymph node involvement was significant (r=0.58, P=0.02). Conclusion: The nucleus: nucleoli ratio of tumoral cells in OSCC was correlated to lymph node involvement. Based on the results, nucleus: nucleoli ratio can potentially be a useful tool to determine the lymph node involvement in OSCC.

Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought and evaluating its spatiotemporal dynamics are essential to manage regional drought conditions and protecting the natural environment. In this study, various single remote sensing-based drought indices including soil moisture condition index (SMCI), precipitation condition index (PCI), temperature condition index (TCI), and vegetation condition index (VCI) and combined RS-based drought Indices including optimized meteorological drought index (OMDI) and synthesized drought index (SDI) have been used to investigate the spatiotemporal variations of meteorological and agricultural droughts between 2000 and 2021 in Iran. The in situ drought indices, including the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) series of 1, 3, 6, 12, and 24 months were utilized to verify remote sensing-based drought indices and evaluate their applicability for analyzing drought conditions. The results indicated that the correlation coefficients of the in situ drought indices with the combined drought indices are higher than the RS-based single drought indexes. Generally, single-factor drought indexes, including VCI, TCI, PCI, and SMCI, have specific characteristics. The PCI and SMCI have an acceptable correlation with the short-term SPI and SPEI and are more applicable to monitoring short-term drought conditions. Further, the TCI has better performance in monitoring long-term drought conditions in Iran. This research concluded that the central, eastern, and southeastern parts of Iran mainly were experiencing exceptional and extreme drought conditions as the worst agricultural and meteorological drought conditions observed in the years 2008 and 2021 in the region during the last 20 years. The results also showed that, in 2019 and 2020, most areas of Iran had higher OMDI and SDI values and the severity of the drought has decreased in these years. Particularly, this research provides an essential reference for reasonably choosing RS-based drought indices for monitoring meteorological and agricultural droughts from a local to global scale.

Abstract The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017–2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively.

Condition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns.

Crashes at highway rail grade crossings (HRGCs) are often involved with fatalities due to the momentum of a train. This study collected nine years (2010–2018) of fatal HRGC crashes from the Fatality Analysis Reporting System (FARS) to perform the analysis. The Taxicab Correspondence Analysis (TCA) was applied to this dataset. This method identified several patterns that trigger HRGC-related fatal crashes. The findings indicate that fatal crashes involving multiple fatalities are often highly associated with alcohol-influenced drivers, poor lighting conditions, and inclement weather. The fatal crash that occurs during the daylight with the uninfluenced driver is less likely to involve more than one fatality. The results also recognized the combinations of vehicle type and speed are associated with fatal crashes at rail grade crossings. The relatively low-speed limit crossings and larger utility vehicles are more likely to be associated with fatal crashes because large vehicles require a longer time to cross railroads at a low speed. The relatively high-speed limit crossing and smaller or lighter vehicles, especially the motorcycle, are highly associated with fatal crashes.

Quantifying the impact of modelling uncertainty on seismic performance assessment of existing buildings is non-trivial when considering the partial information available on material properties, construction details, and the uncertainty in the capacity models. This task is further complicated when uncertainty related to ground motion representation is considered. To address this issue, record-to-record variability, uncertainties in structural model parameters, and fragility model parameters due to limited sample size are propagated herein by employing a nonlinear dynamic analysis procedure based on recorded ground motions. A one-to-one sampling approach is adopted in which each recorded ground motion is paired up with a different structural model realization. Uncertainty propagation is explored by measuring the impact of different sampling techniques, such as Monte Carlo simulation with standard random sampling and Latin Hypercube sampling (with Simulated Annealing) in the presence of three alternative nonlinear dynamic analysis procedures: Incremental Dynamic Analysis (IDA), Modified Cloud Analysis (MCA), and Cloud to IDA (a highly efficient IDA-like procedure). This is all illustrated through application to an existing reinforced-concrete school building in southern Italy. It is shown that with a small subset of records, both MCA and Cloud to IDA can provide reliable structural fragility (and risk) estimates for three considered limit states, comparable to the results of more resource-intensive schemes. [ABSTRACT FROM AUTHOR]