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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.

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.

Reduction of the transportation sector's air emissions has been an essential goal for decreasing the emissions' externalities and slowing climate change. This study evaluated the emission impacts of low-level automated vehicles (LAVs) in 86 metropolitan areas in the United States, based on county-specific emission costs of morbidity, mortality, and environmental damages. Twelve LAV technologies were investigated to estimate their impacts on CO2, CO, NOx, PM2.5, SOx, NH3, and VOC. The study analyzed the LAV technologies' direct and indirect emission impacts, including accident-related and non-accident-related congestion reductions, aerodynamic force reduction, the operational load of equipment, and traffic rebound. The results show that the LAV technologies could reduce the annual social costs of emissions by between 9 and 20 billion USD2019 in these metropolitan areas. This is equal to approximately 20% of the vehicles' emissions. A sensitivity analysis was performed on the technologies' market penetration rates (MPRs), demonstrating that the rate strongly influences the emission savings. This study revealed that LAVs' savings per vehicle-miles traveled (VMT) vary between areas, with the technologies performing differently in different areas due to both geographical factors (e.g., population) and technological factors (e.g., internal combustion engine performance). The study provides extensive resources for policymakers to develop targeted incentives and maximize the social benefits of the LAV technologies.

Background: Plant-origin foods are among the most important sources of food allergic reactions. An increase in the incidence of sesame seed allergy among children and adults has been reported in recent years. The aim of this preliminary study was to investigate the prevalence, importance and clinical manifestations of sesame allergy among Iranian patients.Methods: In a cross-sectional survey, 250 patients with suspected IgE-mediated food allergies completed a questionnaire and underwent skin prick tests with sesame extract as well as cross-reacting foods (walnut, soya and peanut). Total IgE and sesame-specific IgE levels were measured. Patients with positive skin test reactions and/or IgE specific for sesame without clinical symptoms were considered sensitive to sesame. The patients who also had clinical symptoms with sesame consumption were diagnosed as allergic to sesame.Results: Of the 250 patients enrolled in this study, 129 were male and 121 female, with a mean age of 11.7 years. The most common food allergens were cow's milk, egg, curry, tomato and sesame. Sesame sensitivity was found in 35 patients (14.1%). Only five patients (2%) had sesame allergy. Sesame-sensitive patients had a significantly higher frequency of positive prick test to cross-reacting foods when compared to non-sensitized patients (p=0.00). The type of symptom was independent of gender and age of the patients, but urticaria and dermatitis-eczema were significantly more frequent in sensitized patients (p=0.008).Conclusions: This is the first study addressing the prevalence of sesame seed allergy in Iranian population. We found sesame to be a common and important cause of food allergy. The panel of foods recommended for use in diagnostic allergy tests should be adjusted.

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]