Abstract

Extended use of plant ingredients in Atlantic salmon farming has increased the need for knowledge on the effects of new nutrients and contaminants in plant based feeds on fish health and nutrient-contaminant interactions. Primary Atlantic salmon hepatocytes were exposed to a mixture of PAHs and pesticides alone or in combination with the nutrients ARA, EPA, α-tocopherol, and γ-tocopherol according to a factorial design. Cells were screened for effects using xCELLigence cytotoxicity screening, NMR spectroscopy metabolomics, mass spectrometry lipidomics and RT-qPCR transcriptomics. The cytotoxicity results suggest that adverse effects of the contaminants can be counteracted by the nutrients. The lipidomics suggested effects on cell membrane stability and vitamin D metabolism after contaminant and fatty acid exposure. Co-exposure of the contaminants with EPA or α-tocopherol contributed to an antagonistic effect in exposed cells, with reduced effects on the VTG and FABP4 transcripts. ARA and γ-tocopherol strengthened the contaminant-induced response, ARA by contributing to an additive and synergistic induction of CYP1A, CYP3A and CPT2, and γ-tocopherol by synergistically increasing ACOX1. Individually EPA and α-tocopherol seemed more beneficial than ARA and γ-tocopherol in preventing the adverse effects induced by the contaminant mixture, though a combination of all nutrients showed the greatest ameliorating effect.

Abstract

Partially and fully automated vehicles (AVs) are being developed and tested in different countries. These vehicles are being designed to reduce and ultimately eliminate the role of human drivers in the future. Most fatal accidents of vulnerable road users (VRUs), pedestrians, cyclists and mopeds, involve a motorized vehicle. In addition, most of the accidents involving VRUs and motorized vehicles happen at road crossings. By replacing human-driven vehicles with automated vehicles, the human role will be altered and reduced which could lead to an increase in traffic safety. However, drivers are not the only ones who will have to adapt to automated vehicles, other road users, such as pedestrians and cyclists, will have to interact with vehicles with various levels of automation, too. Pedestrians and cyclists will still be humans and might behave in an unpredictable manner which could lead to unsafe behaviors. The main goal of this paper is to propose a theoretical framework which describes the interactions between automated vehicles and road user behavior under different road design conditions. This is a prerequisite to understand how to design safe urban environments where VRUs and automated vehicles can interact safely. A synthesis of the existing literature about the interactions between automated vehicles and VRUs, and the main factors that could influence VRUs’ behavior is presented. The results of the synthesis and the identified knowledge gaps are discussed. Based on this, a theoretical framework for the interactions between VRUs and automated vehicles is developed and discussed.

Abstract

We seek to quantify smartwatch use, and establish differences and similarities to smartphone use. Our analysis considers use traces from 307 users that include over 2.8 million notifications and 800,000 screen usage events, and we compare our findings to previous work that quantifies smartphone use. The results show that smartwatches are used more briefly and more frequently throughout the day, with half the sessions lasting less than 5 seconds. Interaction with notifications is similar across both types of devices, both in terms of response times and preferred application types. We also analyse the differences between our smartwatch dataset and a dataset aggregated from four previously conducted smartphone studies. The similarities and differences between smartwatch and smartphone use suggest effect on usage that go beyond differences in form factor.

Abstract

n insufficient functional relationship between adjustment factors and saturation flow rate (SFR) in the U.S. Highway Capacity Manual (HCM) method increases an additional prediction bias. The error of SFR predictions can reach 8%&ndash 10%. To solve this problem, this paper proposes a comprehensive adjusted method that considers the effects of interactions between factors. Based on the data from 35 through lanes in Beijing and 25 shared through and left-turn lanes in Washington, DC, the interactions between lane width and percentage of heavy vehicles and proportion of left-turning vehicles were analyzed. Two comprehensive adjustment factor models were established and tested. The mean absolute percentage error (MAPE) of model 1 (considering the interaction between lane width and percentage of heavy vehicles) was 4.89% smaller than the MAPE of Chinese National Standard method (Standard Number is GB50647) at 13.64%. The MAPE of model 2 (considering the interaction between lane width and proportion of left-turning vehicles was 33.16% smaller than the MAPE of HCM method at 14.56%. This method could improve the accuracy of SFR prediction, provide support for traffic operation measures, alleviate the traffic congestion, and improve sustainable development of cities. Document type: Article