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• The impact of recognizability of CAV platoons on drivers' behavior and safety was investigated. • Standard deviation of speed and acceleration decreased significantly during merging in the presence of recognizable CAV platoon compared to the presence of non-recognizable CAV platoon. • Time-to-collision (TTC) during merging was higher in the presence of recognizable CAV platoon. • Male drivers showed safer driving behavior during diverging in the presence of recognizable CAV platoon. Platoons of connected and automated Vehicles (CAVs) comprising both passenger cars and trucks are expected to appear on highways in the coming years. Though prior studies explored driver behaviors in the presence of CAV platoons, it remains unknown whether it is necessary for a traditional vehicle's driver to recognize CAV platoons in a mixed traffic environment for safe maneuvering. This study aims to test the hypothesis that drivers behave differently when interacting with recognizable CAV platoons compared to interacting with non-recognizable CAV platoons. A driving simulator experiment was conducted featuring four merging and diverging scenarios. Participants were informed beforehand that blue-colored vehicles driving with short gaps would be CAV platoons. However, they also encountered CAV platoons consisting of several vehicles with different colors (not blue). The blue-colored platoon was considered as recognizable CAV platoons and the latter as non-recognizable CAV platoons. Performance indicators including speed, maximum acceleration and deceleration, standard deviation (SD) of speed and acceleration, and time-to-collision (TTC) were considered to examine drivers' behavior. The generalized estimating equation (GEE) approach was applied to evaluate the effect of recognizability and demographic characteristics on driver performance. The GEE results revealed significant improvement in drivers' behavior in the presence of recognizable CAV platoons. The merging SD of speed and acceleration were around 32% and 29% lower, respectively in the presence of recognizable CAV platoons. The merging TTC was around 20% higher in the presence of recognizable CAV platoons. Although no significant main effect of recognizability was found for diverging maneuvers, a noteworthy result was that male drivers had around 37% higher diverging TTC than female drivers in the presence of recognizable CAV platoons. The findings also suggested significant effects of gender, age, driving experience, and education on driver performance while merging, as well as effect of gender and prior crash involvement on driver performance while diverging. These findings provide valuable insights to transportation planning authorities in determining the potential need for making CAV platoons recognizable and developing associated guidelines for safer traffic movements in mixed traffic environments. [ABSTRACT FROM AUTHOR]

• This paper examines seniors' perceptions and attitudes to a wide range of AV options from the perspective of pedestrians and users. • AV options included fully-automated, semi-automated, and shared autonomous vehicles. • The data were collected from 1000 older Americans during the COVID-19 pandemic. • Three clusters of seniors were identified with different perceptions and attitudes toward different AVs options as well as demographic characteristics. Introduction: Autonomous vehicles (AVs) are considered a promising solution to improve seniors' safety and mobility. However, to transition to fully automated transportation, especially among seniors, it is vital to assess their perception and attitude toward AVs. This paper investigates seniors' perceptions and attitudes to a wide range of AV options from the perspective of pedestrians and users in general, as well as during and after the COVID-19 pandemic. Underlying this objective is to examine older pedestrians' safety perceptions and behaviors at crosswalks in the presence of AVs. Method: A national survey collected data from a sample of 1,000 senior Americans. Using Principal Component Analysis (PCA) and Cluster Analysis, three clusters of seniors were identified with different demographic characteristics, perceptions, and attitudes toward AVs. Results: PCA findings revealed that "risky pedestrian crossing behavior," "cautious pedestrian crossing behavior in the presence of AVs," "positive perception and attitude toward shared AVs," and "demographic characteristics" were the main components explaining most of the variation within the data, respectively. The PCA factor scores were used in the cluster analysis, which resulted in the identification of three distinctive groups of seniors. Cluster one included individuals with lower demographic scores and a negative perception and attitude toward AVs from the perspective of users and pedestrians. Clusters two and three included individuals with higher demographic scores. Cluster two included individuals with a positive perception toward shared AVs from the user perspective, but a negative attitude toward pedestrian-AV interaction. Cluster three included those with a negative perception toward shared AVs but a somewhat positive attitude toward pedestrian-AV interaction. The findings of this study provide valuable insights to transportation authorities, AV manufacturers, and researchers regarding older American's perception and attitude toward AVs as well as their willingness to pay and use Advanced Vehicle Technologies. [ABSTRACT FROM AUTHOR]

• This study investigates the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. • The analysis was conducted using Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling technique. • The findings reveal that "Driving maneuver & crash location", "Road features and traffic control devices" were the main risk factors. • Four latent factors as well as "Pandemic" variable contributed equally in predicting crash size and severity. • Traffic rule violation, crash time, and traffic control devices were found to be new risk factors among senior drivers. Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors' crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including "Driving maneuver & crash location", "Road features and traffic control devices", "Driver condition & behavior", "Road geometric characteristics", "Crash time and lighting", and "Road class" latent factors. The binary variable of "Pandemic" was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also "Pandemic" was a risk factor to seniors as much as "Driver condition & behavior", "Road geometric characteristics", "Crash time & lighting", and "Road class" factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19. [ABSTRACT FROM AUTHOR]

Speeding is a key contributing factor in roadway crashes in the United Arab Emirates (UAE) and elsewhere. Understanding how drivers behave towards speed management devices (i.e., speed cameras, radars, speed limits and speed warning signs) as well as factors affecting drivers’ involvement in speed-related crashes might help in improving traffic safety. This study aims to identify and quantify the factors that affect drivers’ compliance with speed enforcement and management devices as well as drivers’ involvement in at-fault speed-related crashes in the Emirate of Abu Dhabi (AD), UAE. Two different datasets were collected from the same drivers’ population in AD to provide different valuable information regarding the speeding problem. The first dataset was obtained from crashes’ reports while, the second dataset was obtained from a self-reported questionnaire survey that was carried out among a total of 442 drivers in AD. Three logistic regression models were developed to identify the significant variables that affect (1) the occurrence of speed related crash (using crashes reports data), (2) drivers’ compliance with speed limits (using questionnaire data), and (3) involvement in at-fault speed related crashes (using questionnaire data). The findings revealed that drivers’ factors (gender, age, and nationality), vehicle factor (vehicle type), roads and environment factors (weather, road type and speed limit) were the significant factors that affect the occurrence of speed-related crashes in AD. The questionnaire findings revealed that running late, low values of posted speed limits and no sufficient police enforcement were the three main reasons that make motorists drive over the speed limits. In addition, the results indicated that drivers’ characteristics (i.e., gender, education and income), drivers’ responses to speed enforcement and management devices, and drivers’ awareness about the importance of such devices in improving traffic safety were the main factors that affecting both drivers’ compliance with speed enforcement devices and drivers’ involvement in at-fault speed-related crashes. A comparison between the analysis results of traffic crashes and questionnaire datasets as well as a comparison between the findings of this study and existing literature are also provided. [ABSTRACT FROM AUTHOR]

• The study investigates the impact of different types of in-vehicle warning messages (audio and text) on drivers' behaviors when approaching horizontal curves, compared to a base scenario with standard warning signs. • Vehicle-to-Infrastructure (V2I) warning messages demonstrated promising potential in effectively influencing drivers' behavior at horizontal curves. • Audio warning messages demonstrated greater effectiveness than text-based warning messages at horizontal curves. • The audio warning message resulted in higher speed limit compliance rates across age and gender groups. Introduction: According to the Federal Highway Administration, a quarter of fatal collisions has occurred at horizontal curves. The average collision rate at horizontal curves was found to be three times higher than other types of highway segments. The lack of compliance with the speed limit and driver-related factors are among the main contributing factors to those collisions. Vehicle to Infrastructure (V2I) communications can address these limitations by providing drivers with valuable in-vehicle warning messages based on operational and safety data. There is limited effort investigating the impact of different types of V2I warning messages at horizontal curves and among different profiles of drivers. This study aims to thoroughly examine drivers' behavior and compliance with different V2I warning messages when approaching horizontal curves. Methods: A driving simulator experiment and self-reported survey were conducted. Three main hypotheses were analyzed in this study. First, whether supplying drivers with in-vehicle V2I warning messages will positively affect drivers' behavior at horizontal curves compared to the standard road signs. Second, whether there will be a significant difference in drivers' behavior when receiving text and audio V2I warning messages. Third, whether seniors and female drivers will comply more with speed limit advisory provided through V2I message than younger and male drivers. Results: The Analysis of Covariance confirmed the first two hypotheses. Two main measures of drivers' behavior found to be lower in the V2I communication scenarios compared to the base one. The audio warning message was found to be more promising in increasing drivers' compliance with speed limit advisory when approaching the curves. Analyzing the third hypothesis revealed that younger and male drivers had higher curve initiation speed compared to females and seniors. Practical Applications: The findings of this study can be used by transportation researchers, authorities, and car manufacturers to improve the effectiveness of in-vehicle V2I warning messages among different profiles of drivers. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Recently, growing concern has been shifting toward the necessity of improving traffic safety in the Kingdom of Saudi Arabia (KSA). KSA has a unique traffic safety problem in that: (a) it can be classified as a developed country in terms of the magnitude and quality of the roadway networks available and its compatibility with international standards; however, (b) it can also be considered a developing country as the rate of increase in the number of road crashes is substantial compared with relevant figures of other developing countries and other countries of the Gulf region. Hence, more research efforts are still needed. Objectives: This paper examines the nature and causes of fatal and serious traffic crashes in KSA so that solutions and/or future studies can be suggested. Method: Data from 11,545 reported fatal and injury traffic crashes that occurred in Riyadh (the capital of KSA) during the period 2004–2011 were analyzed by alternative and complementary methods. A logistic regression model was estimated and the results revealed that crash reason (speeding), damages in public property, day of the week, crash location (non-intersection location), and point of collision (head-on) were the significant variables affecting the binary target variable (fatal and non-fatal crashes). Additionally, the structural equation modeling approach was developed to identify and quantify the impacts of significant variables influencing crash size (e.g., no. of injuries, no. of vehicles involved in the crash). Crash size is one of the important indices that measure the level of safety of transportation facilities. Results: The results showed that road factor was the most significant factor affecting the size of the crash followed by the driver and environment factors. Impact on Industry: Considering the results of this study, practical suggestions on how to improve traffic safety in KSA are also presented and discussed. [Copyright &y& Elsevier]

Abstract: Objectives: The main objective of this paper is to investigate whether real-time traffic flow data, collected from loop detectors and radar sensors on freeways, can be used to predict crashes occurring at reduced visibility conditions. In addition, it examines the difference between significant factors associated with reduced visibility related crashes to those factors correlated with crashes occurring at clear visibility conditions. Method: Random Forests and matched case-control logistic regression models were estimated. Results: The findings indicated that real-time traffic variables can be used to predict visibility related crashes on freeways. The results showed that about 69% of reduced visibility related crashes were correctly identified. The results also indicated that traffic flow variables leading to visibility related crashes are slightly different from those variables leading to clear visibility crashes. Impact on Industry: Using time slices 5–15 minutes before crashes might provide an opportunity for the appropriate traffic management centers for a proactive intervention to reduce crash risk in real-time. [Copyright &y& Elsevier]

Abstract: The present study aims at identifying and quantifying significant factors (i.e., demographic, aberrant driving behavior) associated with young drivers’ involvement in at-fault crashes or traffic citations at the ages of 16–17 (while having the Operational License) and 18–24 years old (while having the Full License). A second objective was to investigate the main reason(s) for involvement in risky driving behavior by young drivers. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 680 young drivers in Central Florida. To achieve these goals, the structural equation modeling approach was adopted. The results revealed that aggressive violations, in-vehicle distractions and demographic characteristics were the significant factors affecting young drivers’ involvement in at-fault crashes or traffic violations at the age of 16–17. However, in-vehicle distractions, attitudes toward speeding and demographic characteristics were the significant factors affecting young drivers’ crash risk at 18–24. Additionally, the majority of participants reported that “running late” is the main reason for taking risk while driving (i.e., speeding, accept short gaps, or drive so close to the car in front) followed by “racing other cars”. Additionally, “exceed speed limits” was the main reason for receiving traffic citations at 16–17 and 18–24 age groups. Practical suggestions on how to reduce crash risk and promote safe driving among young drivers are also discussed. [Copyright &y& Elsevier]

Abstract: More researchers started using real-time traffic surveillance data, collected from loop/radar detectors (LDs), for proactive crash risk assessment. However, there is a lack of prior studies that investigated the link between real-time traffic data and crash risk of reduced visibility related (VR) crashes. Two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data are advantageous for predicting VR crashes; LDs or AVIs. Thus, this study attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data are better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression models using the historical crashes, LDs and AVI data. Regarding the model estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5–10min prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5–10min prior to the crash time, affected the likelihood of VR crash occurrence. The results showed that both LDs and AVI systems can be used for safety application (i.e., predicting VR crashes). It was found that up to 73% of VR crashes could be identified correctly. Argument concerning which traffic data (LDs or AVI) are better for predicting VR crashes is also provided and discussed. [Copyright &y& Elsevier]

Abstract: One way to improve safety under reduced visibility conditions (i.e., reduce the chances of visibility related crashes) is to improve drivers’ behavior under such adverse weather conditions. The aim of this paper is to thoroughly examine drivers’ responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers’ compliance and drivers’ satisfaction with variable speed limit (VSL) and changeable message signs (CMS) instructions in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers’ satisfaction with VSL/CMS was the most significant factor that positively affected drivers’ compliance with advice or warning messages displayed on VSL/CMS under different fog conditions followed by human factors. Moreover, it was found that roadway type affected drivers’ compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers’ familiarity with VSL and human factors were the significant factors affecting drivers’ satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the present study, several recommendations are suggested as guidelines to improve drivers’ behavior in such reduced visibility conditions by enhancing drivers’ compliance with VSL/CMS instructions. [Copyright &y& Elsevier]

Abstract: Mammary gland secretions derived from secretory cows infected with coagulase +ve Staphylococcus spp. was examined for the expression of IL-6, production of lysozyme and NO x . The examined cows reflected 25 cases of subclinical mastitis and 15 cases of clinically mastitic animals. The IL-6 concentration in the subclinical animals was significantly higher (30.8ng/ml) than the clinically manifested animals (18.0ng/ml) and the normal cows (5.2ng/ml). On the other hand the level of lysozyme although significantly higher than the normal cows (6.9μg/ml) yet its level in the subclinical animals (11.2μg/ml) was lower than that estimated in the clinical animals (15.6μg/ml). Similarly, the level of NO x in the normal animals was found to be 5.6μM/ml to increase to 6.2μM/ml in the subclinical mastitic animals and to significantly increase further to 11.5μM/ml in the clinically affected cows. These results suggest the promising use of whey IL-6, lysozyme or/and NO concentration variabilities as prognostic parameters on the degree of the commencement of mastitis in cows. [Copyright &y& Elsevier]

Advanced Vehicle Technologies (AVTs) have been purported to improve the behind-the-wheel experience of older adults (65+ years) by making driving safer, easier, and more enjoyable. The autonomous vehicle, in particular, is viewed as a promising innovation for those in older age groups. However, little is known about how older adults perceive AVTs as well as their attitudes, concerns, and willingness to use different levels of vehicular automation. Using the results of an online survey of 1000 Canadians aged 65 and older (who live in the Greater Golden Horseshoe Region, Southern Ontario, Canada), this paper aims to identify and quantify key factors that can affect older adults' willingness to use vehicles with different levels of automation (e.g., semi vs. fully autonomous vehicles; SAV/FAV). Underlying this objective is a need to examine older adults' preferences and concerns for using emerging automotive technologies. Using factor analysis and structural equation modeling, several factors were also identified that can influence older adults' openness to use SAV/FAV including the perception that the AVTs would actually assist with performing driving-related tasks, participant concerns about using SAV/FAV (mainly concerns about inadequate testing on SAV/FAV and being legally responsible if SAV/FAV technology failure causes an accident), using automobile as a passenger, gender, level of education, and the need for assistance with driving. Moreover, almost two thirds of participants reported having concerns about liability if an AVT failed resulting in a crash. Findings of this study provide decision makers and car manufacturing companies with valuable insights regarding older adults' willingness and needs to use SAV/FAV. [ABSTRACT FROM AUTHOR]

• This paper examines significant factors causing or leading older adults (65+) to be involved in pedestrian incidents. • The data were collected from 1001 older adults living in the Greater Golden Horseshoe, South Ontario, Canada. • A wide range of health and behavioral factors of older pedestrians were explored. • Walking difficulties and fear of falling were the most significant factors followed by crossing evaluation capability. Older pedestrians (65 years and above) are among the most vulnerable road users. As the number and proportion of older adults grows in many countries, and awareness of the benefits of active travel becomes increasingly evident, it behooves researchers to further investigate the safety of older pedestrians. This study contributes to the literature by examining the factors that influence older adults' susceptibility to pedestrian incidents (falls incidents and pedestrian-vehicle collisions). To this end, a sample of older adults living in the Greater Golden Horseshoe, South Ontario, Canada was surveyed. The data were analyzed using descriptive statistics, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), to identify several latent factors that are significantly associated with older adults' involvement in pedestrian incidents. The results revealed that the factors that affect older adults' susceptibility to pedestrian incidents are level of difficulty while walking, fear of falling, and crossing evaluation capability. In contrast, level of pedestrian confidence and level of risk-taking crossing behavior are not among the significant factors. It was also found that older pedestrians aged 65–75 years, males, and those who are still working had a greater probability to be involved in pedestrian incidents. These findings shed light on some key constraints of older pedestrians, and the extent to which each factor could improve their safety. This information, in turn, can be used by transportation authorities in prioritizing their plans, policies, and programs towards improving the safety and mobility of older pedestrians. [ABSTRACT FROM AUTHOR]

• A traffic crash severity prediction framework using deep learning was proposed. • A generalized image transformation technique was employed to convert crash data to images. • The deep learning network was trained using a customized f1-loss function. • An inference setting was proposed for practical application. • The result showed an improved performance for fatal and injury crashes. Highway work zones are most vulnerable roadway segments for congestion and traffic collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions at work zones is vital to reduce the response time for emergency units (e.g., medical aid), accordingly improve traffic safety and reduce congestion. In predicting the severity of traffic collisions, previous studies used different statistical and machine learning models with accuracy as the main evaluating factor. However, the performance of these models was generally not good, especially on fatal and injury crashes. Also, looking into the prediction accuracy only is misleading. This paper aims to propose a novel deep learning-based approach with a customized f1-loss function to predict the severity of traffic crashes. Underlying this objective is to compare the results of deep learning models with machine learning model considering two performance indicators, namely precision, and recall. The data used in the analysis include a sample of traffic crashes that occurred at work zones in Louisiana from 2014 to 2018. This dataset includes valuable information (features) related to road, vehicle, and human factors affecting the occurrence and severity of those crashes. The proposed methodology is based on transforming these features/variables into images. Image transformation is conducted using a nonlinear dimensionality reduction technique t-SNE and convex hull algorithm. A CNN based deep learning algorithm with a customized loss function was used to directly optimize the model for precision and recall. The results showed improved performance in predicting the crash severity of fatal and injury crashes using the deep learning approach, which can help to improve traffic safety as well as traffic congestion at work zones and possibly other roadways segments. [ABSTRACT FROM AUTHOR]

The seismicity rate variations prior to the 1993 and 1995 Gulf of Aqaba mainshocks have been investigated using statistical analysis, focusing on the precursory phenomena of the seismic activity progression. A statistical estimation of the seismic quiescence was applied to detect a significant reduction in the rate of seismic activity, using the normal standard deviate Z-test. The catalogue data both from the Egyptian National Seismological Network (ENSN) and the International Seismological Center (ISC) which cover the interval from 1983 to 2003 was used in this analysis. For our computations, only events with completeness magnitude ≥3.5 in the source area of the two Gulf of Aqaba mainshocks were considered. We investigated seismicity rate variations as a function of time and space in terms of the Z value preceding the 1993 and 1995 Gulf of Aqaba mainshocks respectively, using a declustered catalogue. Mapping the spatial changes of the seismicity over the entire period of catalogue manifests clear seismic quiescence anomalies related to the epicenters of the 1993 and 1995 Gulf of Aqaba mainshocks. The long-term average and the corresponding cumulative number of events versus time plot for anomalous areas have been constructed as a way of determining the starting time of the seismic quiescence and its duration for both mainshocks. The plot of the 1993 mainshock shows a decrease in the seismicity rate with a maximum Z value level (Z max) equal to 5.4 with an observed seismic quiescence starting two years before its occurrence while the 1995 mainshock plot gives a Z max = 4.5 with 1.3 seismic quiescence duration before it. The estimated periods of quiescence are consistent with the duration of the second type seismic gap derived from the detailed investigations of the spatio-temporal variations of the seismicity pattern which precedes the 1993 and 1995 Gulf of Aqaba main earthquakes. Earthquakes of magnitudes smaller than those which have shown the second type of seismic gap reflect pattern similarities of the activity before both 1993 and 1995 earthquakes. Both mainshocks are preceded by anomalous patterns of activity, which include both swarm type activity and short-term seismic gap, respectively. • The seismic activity in Gulf of Aqaba is characterized by precursory phenomena. • The Gulf of Aqaba manifested seismic quiescence before the main shocks. • The Gulf of Aqaba recorded the second type seismic gap. • Seismicity rate change detected the quiescence period. [ABSTRACT FROM AUTHOR]

Throughout its history, the northern coast of Alexandria has experienced devastating earthquakes and tsunamis impacts from tsunamigenic sources in the Eastern Mediterranean. The most hazardous tsunami events for Northern coast of Egypt were related to the earthquakes of 365 in Crete with M w 8.5, 1222 in Cyprus with M w 7–7.5, and 1303 in Rhodes Island with M w 8.0.The 365 and 1303 earthquakes were accompanied by tsunamis that resulted in widespread destruction and subsequent fatalities along the coast of Alexandria as evidenced by the available historical reports for the Mediterranean region, geomorphologic and paleo-tsunami investigations accomplished recently. In this work, for each tsunamigenic source, i.e., West Hellenic Arc, East Hellenic Arc, and Cyprian Arc, the maximum credible earthquake (MCE) is defined then modeled with NAMI-DANCE. The comparison of the tsunami inundation maps of MCE scenarios computed for each tsunami source shows that the East Hellenic arc (EHA) is the most hazardous source for the Alexandria coast, with a tsunami wave height of 5.5–6.0 m, while the Cyprian arc (CA) is the least hazardous source. The aggregated tsunami inundation map for Alexandria defines the furthest boundary between inundated and non-inundated lands and is associated with hazard levels based on water heights. This map shows that the coasts of Al Amereya district and Borg Al Arab city are expected to be more affected than the others districts. It also indicates that the west side of Alexandria, called Al Sahel Al Shamally, is expected to have less impact than the eastern side, because it is naturally protected by a barrier of carbonate material parallel to the shoreline. The exposure analysis shows that more than 85,000 people are located in the tsunami hazard zone (representing 15.8% of Alexandria governorate's population). Regarding buildings and infrastructure, the most exposed district is Al Amereya with 65.8% of total exposed objects. Among the buildings, the residential ones are the most exposed and about 21% can be classified as highly vulnerable. In terms of risk assessment, the Al Amereya district is identified as the area with the highest tsunami risk. • We provided a detailed tsunami hazard assessment for Alexandria using numerical modelling. • Tsunami flow depth for Alexandria was computed using topography and tsunami wave height. • Vulnerability of residential buildings in the areas under risk has been evaluated. • Tsunami risk map for Alexandria indicate that the district of Al Amereya is the highest risk. • There is a need of adopting structural and non-structural prevention measures for Alexandria. [ABSTRACT FROM AUTHOR]

Abstract: Induced responses to insect herbivory are a common phenomenon in the plant kingdom. So far, induced responses have mostly investigated in aerial plant parts. Recently it was found that root herbivore may also elicit both local and systemic responses affecting aboveground herbivores and their natural enemies. Using broccoli (Brassica oleracea subsp. italica L.) and turnip (Brassica rapa subsp. rapa L.), two cultivated brassicaceaous plants differing in their chemistry and morphology, we analysed the local and systemic induced responses triggered by Delia radicum L. damage, JA and SA application. We also assessed whether the root induction treatments affected D. radicum larval performance. Both D. radicum damage and JA induced changes in glucosinolate and sugar content as well as affected D. radicum performance, while SA application did not. Despite the uniform chemical responses, the effect on larval performance on broccoli and turnip plants was very different. On broccoli, JA root treatment reduced herbivore performance, whereas in turnips the same treatment enhanced it. JA- and D. radicum-induced responses followed similar patterns, which suggests that the JA signalling pathway is involved in root-induced responses to larval feeding. Glucosinolate induction cannot fully explain the differences found in the performance of D. radicum on the different species. Changes in other resistance factors might significantly contribute to the induced resistance in these brassicaceaeous species as well. [Copyright &y& Elsevier]