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Transportation sector and its impacts on climate change have received much attention over the last decade. Energy-optimal vehicle control algorithms such as adaptive cruise control can potentially reduce fuel consumption and the consequential environmental impacts. Adaptive cruise control algorithms optimize vehicles’ speed to lower energy consumption considering several constraints, including safety, stability, and comfortability. A wide range of algorithms with different objectives and optimization mechanisms have been reported in the literature. Due to the high diversity of constraints and objectives, a comprehensive study of these algorithms is required. In recent literature reviews, there is no comprehensive summary of the adaptive cruise control algorithms’ features, classes, and objectives. This paper presents a holistic literature review of energy-optimal adaptive cruise control algorithms. We gathered relevant publications from well-eminent journals. Information on diversity and the number of publications will be presented to provide data on used references. For each paper, the objectives, features, and relevant classes of algorithms were extracted. Then a classification based on objectives is suggested, and mathematical formulas of representative studies are summarized to provide the required knowledge regarding mathematical modeling and optimization in this field. The study provides a useful insight into the development of the cruise control systems research field, revealing those scientific actors (i.e., authors, developers, and institutions) that have made the biggest research contribution to its development.

Niloufar Shashaani,1 Reza Shiari,2 Abdullah Karimi,3 Shima Salehi,4 Roxana Ghanaei,3 Mehrnoush Hassas Yeganeh,2 Sara Shiari,1 Khosro Rahmani,2 Vadood Javadi Parvaneh2 1Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; 2Department of Pediatric Rheumatology, Mofid Children’s Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran; 3Department of Infectious Diseases, Mofid Children’s Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran; 4Faculty of Medicine, Ali Asghar Children’s Hospital, Iran University of Medical Sciences, Tehran, IranCorrespondence: Reza Shiari Tel +98-21-22227033Email shiareza@yahoo.comIntroduction: Kawasaki disease (KD) is a systemic vasculitis that occurs mostly in children under five years old. Kawasaki affects the middle-size arteries, especially the coronary arteries. Therefore, without adequate treatment, it may cause coronary artery aneurysm in 25% of patients. The purpose of this study was to investigate the relationship between Kobayashi, Sano, and Egami criterions with coronary artery aneurysm in KD patients during the last ten years and to identify risk factors in patients with intravenous immunoglobulin (IVIG)-resistant and coronary artery aneurysms.Methodology: Medical records of 363 Kawasaki patients referred during 2008– 2017 were reviewed. Patients’ demographic data and Kobayashi, Sano, and Egami scores of each patient were calculated. Based on echocardiographic findings, cases of coronary artery aneurysm were determined. Sensitivity, specificity, positive and negative predictive value, and the accuracy of each criterion were determined to predicting IVIG resistance and detect coronary artery aneurysm.Results: There was a slight relationship between IVIG-resistance in Kawasaki children and its prediction based on the Kobayashi risk score, but no relationship was found between the Egami and Sano criteria. Sixty-three patients (17.4%) had coronary artery lesions (CALs) on time of diagnosis. There were no statistically significant differences between gender and mean age of children with and without CALs. Also, there was no significant relationship between coronary artery aneurysm in Kawasaki children and its prediction based on the above three risk factors. The area under the ROC-curve of all three risk measures of Kobayashi, Egami, and Sano indicated that all three criteria were not useful in predicting CALs.Conclusion: Despite the low accuracy of the three above criteria to predictive of patients with IVIG resistance, it seems that the variables of age, duration of fever, and C-reactive protein (CRP) are more useful than other variables and may be utilized to evaluate patients by establishing a more appropriate cut-off point.Keywords: Kawasaki disease, coronary artery aneurysm, IVIG-resistant, Kobayashi, Sano, Egami

Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector’s concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements.

Aims: Given the increasing number of individuals developing metabolic dysfunction‐associated steatotic liver disease (MASLD) and the low rate of those with progressive liver disease, there is a pressing need to conceive affordable biomarkers to assess MASLD in general population settings. Herein, we aimed to investigate the performance of the ultrasound‐derived fat fraction (UDFF) for hepatic steatosis in high‐risk individuals. Methods: A total of 302 Europeans with obesity, type 2 diabetes, or a clinical history of hepatic steatosis were included in the analyses. Clinical, laboratory, and imaging data were collected using standardized procedures during a single screening visit in Rome, Italy. Hepatic steatosis was defined by controlled attenuation parameter (CAP) or ultrasound‐based Hamaguchi's score. UDFF performance for hepatic steatosis was estimated by the area under the receiver operating characteristic curve (AUC). Results: Overall, median (IQR) UDFF was 12% (7–20). UDFF was positively correlated with CAP (ρ = 0.73, p < 0.0001) and Hamaguchi's score (ρ = 0.79, p < 0.0001). Independent predictors of UDFF were circulating triglycerides, alanine aminotransferase (ALT), and ultrasound‐measured visceral adipose tissue (VAT). UDFF AUC was 0.89 (0.85–0.93) and 0.92 (0.88–0.95) for CAP‐ and ultrasound‐diagnosed hepatic steatosis, respectively. UDFF AUC for hepatic steatosis was higher than those of fatty liver index (FLI), hepatic steatosis index (HSI), CAP‐score (CAPS), and ALT (p < 0.0001). Lower age, ALT, and VAT were associated with discordance between UDFF and ultrasound. Conclusions: UDFF may be a simple and accurate imaging biomarker to assess hepatic steatosis and monitor changes in hepatic fat content over time or in response to therapeutic interventions beyond clinical trials. [ABSTRACT FROM AUTHOR]

This study was conducted to compare the effect of two anesthetizers Topped lavender essential oil (Lavandula angustifolia) and clove oil on some hematological and serum biochemical parameters in common carp (Cyprinus carpio) juveniles. Common carp juveniles exposed to three treatments including optimum concentration of Topped lavender essential oil (170 mg l-1), clove oil (35 mg l-1) and control and hematological, serum biochemical and stress parameters were determined at different times including 0, 3 and 24 h after anesthetizing. The results showed the effect of treatment (anesthetizing agent) on all hematological parameters, the effect of time on WBC, Hb, MCH and MCHC, the interaction effect of treatment and time on Hct and RBC and the effect of treatment, time and the interaction effect of them on all serum biochemical parameters were significant (p0.05), but in clove oil at the start of anesthetizing time (0) was significantly higher than other sampling times (p0.05). In all times, the lowest amount of glucose was observed in Topped lavender treatment; in Topped lavender and clove oil treatment, after increase in 3 h after anesthetizing, showed decrease in 24 h (p0.05). In all the studied times, higher level of AST in the control, ALT in the control and clove oil, ALP in clove oil and the lower LDH in Topped lavender groups were observed (p

Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector's concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements. [ABSTRACT FROM AUTHOR]