Is there a Relationship between the Level of Plant Metabolites in Cucumber and Globe Cucumber and the Degree of Insect Infestation?

Cucumber plant was infested by three major insect pests, i.e., cotton aphid, white fly nymph and green bugs while globe cucumber, the wild cucumber species was kept healthy without any remarkable infestation. In addition, globe cucumber had higher ratios of T.S.S over T.N, T.S.S over T.C.A.A., T.S.S over T.F.A.A., higher content of cucurbitacins and phenolic compounds than cucumber in the two successive seasons. Lower values of these markers rendered cucumber susceptible to pests under study, not at all stages of growth but whenever these values were much lower through the growth stages. On the other hand, the major cucurbitacins were isolated and purified from each plant and identified by spectroscopic techniques. The development of cotton aphid treated with various globe cucumber extracts as well as the isolated cucurbitacins was also evaluated. Ethanol extract of globe cucumber caused higher mortality and affected on the biological aspects of cotton aphids when compared with all extracts. The importance of acetyl group and the double bond in the position 1 and 2 in different cucurbitacins on aphid’s mortality was studied. A field study showed a remarkable reduction (82.0 %) of cotton aphid induced by spraying the cucumber with 4 % ethanol extract of globe cucumber

A Simultaneous System Model to Estimate Temperature of Pavement Layers: Development and Validation

Many roads in hot regions of the world are exposed to very high temperatures especially in desert areas during the summer season, where the temperature could reach more than sixty Celsius degrees. Temperature variations cause a lot of distress in flexible and rigid pavement such as rutting, vertical transverse (i.e., thermal) cracking, thermal fatigue damage, blowup/buckling, in addition to generating warping stresses. Accurate detection of the surface temperature of flexible pavement is very important for pavement design analysis. This research presents a new model to estimate the temperature of flexible pavement layers under extremely high temperatures as a set of simultaneous equations. The proposed model included Clear-Sky as sub-model to estimate the solar energy of beam and diffuse radiation of the pavement surface. Ambient temperature was calculated by solving the model depending on the time of the day. The proposed model was validated via experimental data collected from one-year measurements at instrumented pavement section in New Sohag, Egypt. The asphalt surface temperatures were measured for summer, winter solstices and vernal and autumnal equinoxes. It was found that the model provides reasonable estimates compared to the experimental data. The proposed model is deemed appropriate for estimating pavement layers temperature in hot regions

Exploiting crowdsourced geographic information and GIS for assessment of air pollution exposure during active travel

Improvement on assessment of air pollution exposure will enhance assessment of health risk-benefit when active travel (cycling and walking). Earlier studies assessed air pollution exposure according to travel time and city-level air pollution. The lack of spatially fine-grained travel data is a barrier to an accurate assessment of air pollution exposure. Due to a high-level spatial granularity, Strava Metro provides an opportunity to assessing air pollution exposure in combination with spatially varying air pollution concentrations. Strava Metro anonymized and aggregated a large volume of users’ traces to streets for each city. In this study, to explore the potential of crowdsourced geographic information in research of active travel and health, we used Strava Metro data and GIS technologies to assess air pollution exposure in Glasgow, UK. Particularly, we incorporated time of the trip to assess average inhaled dose of pollutant during a single cycling or pedestrian trip. Empirical results demonstrate that Strava Metro data provides an opportunity to an assessment of average air pollution exposure during active travel. Additionally, to demonstrate the potential of Strava Metro data in policy-making, we explored the spatial association of air pollution concentration and active travel. As a result, we identified areas that require investment priority, and finally offered implications for policies

The use of crowdsourcing data for analyzing pedestrian safety in urban areas

Pedestrians are the most affected vulnerable road users by traffic collisions. Due to incomplete and inconsistent collision statistics, assessing pedestrian safety remains a complex issue in developing countries. This study investigates the potential of using crowdsourced data to identify hotspot locations by observing pedestrian-vehicle interactions. Safety analysis was carried out using traffic incident data in Eastern Cairo, Egypt. Incident data included collisions, near misses, and infrastructure issues. Spatial autocorrelation analysis was undergone to determine whether incidents are clustered, dispersed, or randomly distributed. The results showed that incidents in the study area are generally dispersed. Nevertheless, local spatial autocorrelation showed that some locations on four major corridors were identified as hotspots with a 99% confidence level. The approach proposed in this study shall help transportation authorities in developing countries to identify and prioritize sites that require more safety attention

Development of Dynamic Transit Signal Priority Strategy

Transit signal priority (TSP) is a popular strategy used to enhance the performance of transit systems by modifying the signal control logic to give transit vehicles priority at signalized intersections. Conventional TSP strategies used in most cities have been shown to offer significant benefits by reducing delay of transit vehicles. However, concerns about shortcomings of conventional TSP strategies have limited their application. The main concern is a potential negative impact on cross street traffic. Another concern is the static nature of conventional TSP strategies and the lack of responsiveness to real-time traffic and transit conditions. A dynamic TSP control system has been developed that can provide signal priority in response to real-time traffic and transit conditions. The dynamic TSP system consists of three main components: a virtual detection system, a dynamic arrival prediction model, and a dynamic TSP algorithm. Two case studies are presented to test and compare the dynamic and the conventional TSP systems. A hypothetical intersection is simulated in the first case study, and a proposed light rail transit line is simulated in the second. For both case studies, a virtual detection system was developed in VISSIM, along with a linear travel time arrival prediction model. Finally, a dynamic TSP algorithm was developed to determine what TSP strategy to use and when to apply it. The results show that the dynamic TSP system reduced the total delay of transit vehicles and outperformed the conventional TSP system for reducing transit trip travel time