Investigation of Durability and Performance of High Friction Surface Treatment

The Indiana Department of Transportation (INDOT) completed a total of 25 high friction surface treatment (HFST) projects across the state in 2018. This research study attempted to investigate the durability and performance of HFST in terms of its HFST-pavement system integrity and surface friction performance. Laboratory tests were conducted to determine the physical and mechanical properties of epoxy-bauxite mortar. Field inspections were carried out to identify site conditions and common early HFST distresses. Cyclic loading test and finite element method (FEM) analysis were performed to evaluate the bonding strength between HFST and existing pavement, in particular chip seal with different pretreatments such as vacuum sweeping, shotblasting, and scarification milling. Both surface friction and texture tests were undertaken periodically (generally once every 6 months) to evaluate the surface friction performance of HFST. Crash records over a 5-year period, i.e., 3 years before installation and 2 years after installation, were examined to determine the safety performance of HFST, crash modification factor (CMF) in particular. It was found that HFST epoxy-bauxite mortar has a coefficient of thermal expansion (CTE) significantly higher than those of hot mix asphalt (HMA) mixtures and Portland cement concrete (PCC), and good cracking resistance. The most common early HFST distresses in Indiana are reflective cracking, surface wrinkling, aggregate loss, and delamination. Vacuum sweeping is the optimal method for pretreating existing pavements, chip seal in particular. Chip seal in good condition is structurally capable of providing a sound base for HFST. On two-lane highway curves, HFST is capable of reducing the total vehicle crash by 30%, injury crash by 50%, and wet weather crash by 44%, and providing a CMF of 0.584 in Indiana. Great variability may arise in the results of friction tests on horizontal curves by the use of locked wheel skid tester (LWST) due both to the nature of vehicle dynamics and to the operation of test vehicle. Texture testing, however, is capable of providing continuous texture measurements that can be used to calculate a texture height parameter, i.e., mean profile depth (MPD), not only for evaluating friction performance but also implementing quality control (QC) and quality assurance (QA) plans for HFST

Effects of Bt Cotton on Thrips tabaci (Thysanoptera: Thripidae) and Its Predator, Orius insidiosus (Hemiptera: Anthocoridae)

Laboratory studies were conducted to investigate tritrophic transfer of insecticidal Cry proteins from transgenic cotton to an herbivore and its predator, and to examine effects of these proteins on the predator's development, survival, and reproduction. Cry1Ac and Cry2Ab proteins from the bacterium Bacillus thuringiensis (Bt) produced in Bollgard-II (BG-II, Event 15985) cotton plants were acquired by Thrips tabaci Lindeman (Thysanoptera: Thripidae), an important sucking pest of cotton, and its generalist predator, Orius insidiosus (Say) (Hemiptera: Anthocoridae). The average protein titers in BG-II cotton leaves were 1,256 and 43,637 ng Cry1Ac and Cry2Ab per gram fresh leaf tissue, respectively. At the second trophic level, larvae of T. tabaci reared on BG-II cotton for 48-96 h had 22.1 and 2.1% of the Cry1Ac and Cry2Ab levels expressed in leaves, respectively. At the third trophic level, O. insidiosus that fed on T. tabaci larvae had 4.4 and 0.3% of the Cry1Ac and Cry2Ab protein levels, respectively, expressed in BG-II plants. O. insidiosus survivorship, time of nymphal development, adult weight, preoviposition and postoviposition periods, fecundity, and adult longevity were not adversely affected owing to consumption of T. tabaci larvae that had fed on BG-II cotton compared with non-Bt cotton. Our results indicate that O. insidiosus, a common predator of T. tabaci, is not harmed by BG-II cotton when exposed to Bt proteins through its prey. Thus, O. insidiosus can continue to provide important biological control services in the cotton ecosystem when BG-II cotton is used to control primary lepidopteran pest

Abnormal Event Detection Based on Deep Autoencoder Fusing Optical Flow

International audienceAs an important research topic in computer vision, abnormal detection has gained more and more attention. In order to detect abnormal events effectively, we propose a novel method using optical flow and deep autoencoder. In our model, optical flow of the original video sequence is calculated and visualized as optical flow image, which is then fed into a deep autoencoder. Then the deep autoencoder extract features from the training samples which are compressed to low dimension vectors. Finally, the normal and abnormal samples gather separately in the coordinate axis. In the evaluation, we show that our approach outperforms the existing methods in different scenes, in terms of accuracy

Using field-evolved resistance to Cry1F maize in a lepidopteran pest to demonstrate no adverse effects of Cry1F on one of its major predators

Spodoptera frugiperda (JE Smith) represents the first documented case of field-evolved resistance to a genetically engineered crop expressing an insecticidal protein from Bacillus thuringiensis (Bt). In this case it was Cry1F-expressing maize (Mycogen 2A517). The ladybird beetle, Coleomegilla maculata, is a common and abundant predator that suppresses pest populations in maize and many other cropping systems. Its larvae and adults are polyphagous, feeding on aphids, thrips, lepidopteran eggs and larvae, as well as plant tissues. Thus, C. maculata may be exposed to Bt proteins expressed in genetically engineered crops by several pathways. Using Cry1F-resistant S. frugiperda larvae as prey, we evaluated the potential impact of Cry1F-expressing maize on several fitness parameters of C. maculata over two generations. Using Cry1F resistant prey removed any potential prey-mediated effects. Duration of larval and pupal stages, adult weight and female fecundity of C. maculata were not different when they were fed resistant S. frugiperda larvae reared on either Bt or control maize leaves during both generations. ELISA and insect-sensitive bioassays showed C. maculata were exposed to bioactive Cry1F protein. The insecticidal protein had no effect on C. maculata larvae, even though larvae contained 20-32ng of Cry1F/g by fresh weight. Over all, our results demonstrated that the Cry1F protein did not affect important fitness parameters of one of S. frugiperda's major predators and that Cry1F protein did not accumulate but was strongly diluted when transferred during trophic interaction

Tri-Trophic Studies Using Cry1Ac-Resistant Plutella xylostella Demonstrate No Adverse Effects of Cry1Ac on the Entomopathogenic Nematode, Heterorhabditis bacteriophora

The potential impacts on natural enemies of crops that produce insecticidal Cry proteins from Bacillus thuringiensis (Bt) are an important part of an environmental risk assessment. Entomopathogenic nematodes are important natural enemies of lepidopteran pests, and the effects of Bt crops on these nontarget organisms should be investigated to avoid disruption of their biological control function. The objective of this study was to investigate the effects of Cry1Ac-expressing transgenic Bt broccoli on the entomopathogenic nematode, Heterorhabditis bacteriophora Poinar (Rhabditida: Heterorhabditidae), under tri-trophic conditions. Using Cry1Ac-resistant Plutella xylostella L. (Lepidoptera: Plutellidae) larvae as hosts, we evaluated the potential impact of Cry1Ac-expressing Bt broccoli on several fitness parameters of H. bacteriophora. Virulence, reproductive potential, time of emergence, and preference of H. bacteriophora for the host (P. xylostella) were not significantly affected when Cry1Ac-resistant P. xylostella larvae were reared on leaves of Cry1Ac or non-Bt broccoli. Also the aforementioned parameters of the subsequent generation of H. bacteriophora did not differ between nematodes obtained from P. xylostella reared on Cry1Ac broccoli compared with those obtained from P. xylostella reared on non-Bt broccoli. To the best of our knowledge, the current study provides the first clear evidence that Cry1Ac does not affect important fitness parameters of H. bacteriophor

Generative Neural Networks for Anomaly Detection in Crowded Scenes

International audienc

Using field-evolved resistance to Cry1F maize in a lepidopteran pest to demonstrate no adverse effects of Cry1F on one of its major predators

Spodoptera frugiperda (JE Smith) represents the first documented case of field-evolved resistance to a genetically engineered crop expressing an insecticidal protein from Bacillus thuringiensis (Bt). In this case it was Cry1F-expressing maize (Mycogen 2A517). The ladybird beetle, Coleomegilla maculata, is a common and abundant predator that suppresses pest populations in maize and many other cropping systems. Its larvae and adults are polyphagous, feeding on aphids, thrips, lepidopteran eggs and larvae, as well as plant tissues. Thus, C. maculata may be exposed to Bt proteins expressed in genetically engineered crops by several pathways. Using Cry1F-resistant S. frugiperda larvae as prey, we evaluated the potential impact of Cry1F-expressing maize on several fitness parameters of C. maculata over two generations. Using Cry1F resistant prey removed any potential prey-mediated effects. Duration of larval and pupal stages, adult weight and female fecundity of C. maculata were not different when they were fed resistant S. frugiperda larvae reared on either Bt or control maize leaves during both generations. ELISA and insect-sensitive bioassays showed C. maculata were exposed to bioactive Cry1F protein. The insecticidal protein had no effect on C. maculata larvae, even though larvae contained 20–32 ng of Cry1F/g by fresh weight. Over all, our results demonstrated that the Cry1F protein did not affect important fitness parameters of one of S. frugiperda’s major predators and that Cry1F protein did not accumulate but was strongly diluted when transferred during trophic interactions

Sensing Social Media Signals for Cryptocurrency News

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.Comment: full version of the paper, that is accepted at ACM WWW '19 Conference, MSM'19 Worksho