The accumulation of large wood debris around bridge piers obstructs the flow, producing increased upstream water levels, large horizontal structural loadings, and flow field modifications that can ...considerably exacerbate scour. These effects have frequently been held responsible for the failure of a large number of bridges around the world, as well as for increased risk of flooding of adjacent areas. Yet little is currently known about the time evolution and processes responsible for the formation and growth of these debris piles. This paper is aimed at deciphering the whole life of debris accumulations through an exhaustive set of 570 experiments in which debris elements were individually introduced into a flume and accumulated at a pier model downstream. Our findings show that in all experiments, the growth of accumulations is halted at a critical stage, after which the jam is removed from the pier by the flow. This condition typically coincides with the time when the dimensions of the accumulations are maxima. The values of the accumulation maximum size display a clear dependence on flow characteristics and debris length distribution. On the other hand, other variables have shown much weaker effects on the geometry of the accumulations. For a given debris length, accumulations are wide, shallow, and long at low flow velocities but become narrower, deeper, and shorter with increasing velocities. A comparison of results of accumulations formed with debris of uniform and nonuniform size distributions has revealed that the former can be up to 2.5 times wider than the latter.
Key Points
Experimental results show that the growth of debris jams at piers is halted at a critical stage
New relations are proposed between the size of debris jams and flow and debris variables
Accumulations formed with uniform are considerably larger than with nonuniform debris
The present study aimed to determine the effects of sugar type on the formation of α-dicarbonyl compounds (α-DCs) in jams under in vitro digestive system. Glyoxal (GO) and methylglyoxal (MGO) levels ...were analyzed with High Performance Liquid Chromatography in jams before and after in vitro digestion. Initial GO and MGO values in jam samples were ranged between 184.38 and 615.94 µg/100 g, and 63.71 and 2978.04 µg/100 g, respectively. After in vitro digestion, the GO and MGO values were increased up to 1616.59 µg/100 g and 3170.39 µg/100 g, respectively. At initial, diabetic jams and homemade jams had relatively low α-DCs content than other jam samples. In vitro digestion had strongly increased the GO and MGO formation in jams, especially in diabetic jams and homemade jams. Sample containing sugar alcohols may have more potential to form AGEs precursors after in vitro digestion, which could be a problem for diabetic people.
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•Jams contain high levels of α-dicarbonyl compounds (α-DC).•Glucose syrup or fructose syrup might contribute to the formation of α-DC in jams.•The contents of α-DCs increase under in vitro digestive system conditions.•The highest α-DCs formation was found in diabetic jams and homemade jams.•Sugar alcohols and fructose-containing jams may have more potential to form α-DC.
In this paper, we studied the effect of driver’s anticipation with passing in a new lattice model. The effect of driver’s anticipation is examined through linear stability analysis and shown that the ...anticipation term can significantly enlarge the stability region on the phase diagram. Using nonlinear stability analysis, we obtained the range of passing constant for which kink soliton solution of mKdV equation exist. For smaller values of passing constant, uniform flow and kink jam phase are present on the phase diagram and jamming transition occurs between them. When passing constant is greater than the critical value depending on the anticipation coefficient, jamming transitions occur from uniform traffic flow to kink-bando traffic wave through chaotic phase with decreasing sensitivity. The theoretical findings are verified using numerical simulation which confirm that traffic jam can be suppressed efficiently by considering the anticipation effect in the new lattice model.
Traffic Jam is a well-known thing that occurs in Indonesia, which caused by some factors, such as vehicle volume, narrow road, road user, activity on the road, and many more. Dijkstra's Algorithm is ...an algorithm that is commonly used in pathfinding to find a shortest path (Shortest Path Problem). Main focus on this research is to find an effective route to avoid the traffic jam points. This algorithm works to find the route to the final destination, finding the shortest route, and then to eliminate the route which qualified to the traffic jam. The result is an effective route, which is a route that has a possible shortest route and has no traffic jam points.
•We propose SNSJam, which is a system to detect and predict road traffic jams by leveraging multiple data sources, specifically Twitter and Instagram.•SNSJam supports multiple languages, specifically ...Arabic and English. It also supports Standard Arabic and UAE local Dialect.•We developed a location recognizer that identifies locations from the text of posts and/or GPS locations. SNSJam supports user-defined locations, which are common names among people but different from the official names. SNSJam is the first such system to define and support user-defined locations.•We developed a context-aware classifier to detect traffic jams. The classifier is able to identify the cause of traffic jams. The detected traffic jams can be visualized through a dynamic map.•SNSJam employs a linear regression model to predict future traffic jams by leveraging current and historical posts.
The increased popularity of micro-blogging applications together with the widespread of location-aware devices have resulted in the creation of large streams of geo-tagged data. Such data provides a great opportunity for researchers to explore event detection and prediction. In particular, road traffic detection and prediction are of great importance to various applications, i.e. Intelligent Transportation Systems. Current works proposed traffic jam detection from a single data source with a single language. However, for countries where the residents are speaking two, or more, languages and are interacting with more than one online social platform, single-language and single-source systems are insufficient to capture the necessary online information. Therefore, in this paper, we introduce SNSJam, an effective system to detect and predict road traffic jams using cross-lingual (English and Arabic) data collected from multiple dynamic sources, such as Twitter and Instagram. SNSJam classifier not only detect traffic events, but also identifies the causes of traffic jams. To identify the location of a traffic event, a Location Recognizer is developed that extracts locations from text and GPS of the post. Additionally, the Location Recognizer supports user-defined locations, which are common names among people. Our experiments show that by combining Arabic and English data streams, the accuracies of traffic events detection and prediction are significantly improved as compared with that of the individual languages. Additionally, combining data streams from multiple sources (Twitter and Instagram) further improved the accuracies of event detection and prediction over any individual source. A visualization interface was developed to show the detected spatio-temporal traffic events on a dynamic map. The detection and prediction results are validated against ground truth data obtained from the concerned authorities in the UAE.
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