Measuring vehicle volumes and monitoring and modeling of PM\u3csub\u3e2.5\u3c/sub\u3e concentrations in a travel center associated with a major urban interstate and interchange

The Watt Road interchange that crosses over I-40 interstate in West Knox County, TN is associated with three travel centers which have overnight parking spaces for approximately 700 vehicles, primarily occupied by heavy heavy-duty diesel vehicles (HHDDV). Field studies were conducted to characterize the traffic patterns by vehicle type within the I-40 and Watt Road corridors and travel centers, and to monitor and model PM2.5 concentrations resulting from the various vehicle activities within the corridors and at the PETRO travel center. Traffic volumes and the associated vehicle mixes were measured for each hour of day. Monitoring PM2.5 concentrations was conducted using a forward light scattering aerosol monitoring system (DataRAM, MIE, Inc.). Modeling of the concentrations was also conducted using the CAL3QHC highway air pollutant prediction model to predict concentrations resulting from the vehicle activities. Total vehicle volumes measured on I-40 were about 3 times heavier during the daytime than during the night, while HHDDV volumes were less deviate and rather consistent throughout the day. On Watt Road, total vehicle and HHDDV volumes were about 3 times heavier during the daytime than during the night. In the travel centers, 46.2 % of HHDDV were idling during the night while 40.7 % of them were idling during the daytime. The ambient contributions of real-time PM2.5 concentrations attributed to vehicle traffic measured within the corridors were 0.3, 2.6, 6.8 and 8.9 µg/m3 from a local highway (US-381), I-40/I-75, Watt Road and travel centers, respectively. Ten minute average PM2.5 concentrations were monitored at the PETRO travel center under four different conditions: stable and unstable atmospheric conditions during dry meteorological periods, and stable and unstable atmospheric conditions immediately after a heavy rain. PM2.5 concentrations contributed from the travel center were from 18 to 27 % of the downwind concentrations. A comparison of the predicted and monitored concentrations suggests that PM2.5 concentrations monitored in the micro-scale of the travel center (downwind 10 to 20 meters from the emission source) may be more affected by an induced mechanical turbulence around HHDDV than by macro-scale atmospheric stabilities. During dry meteorological periods, monitored PM2.5 concentrations may also have been influenced by re-entrainment of gray-black road dust associated with the HHDDV activities

Application of Jacket Pack Anchor (JP Anchor)

Jacket Pack Anchor(JP Anchor) is applied the irregular layer that general ground anchor is difficult to be applied such as soft layer(SPT-N values of less than 20) or gravel layer that grout lost. It makes sure of the pullout resistance required in these layers by the certain grout bulb formation and expansion effect. Thereby, Jacket Pack Anchor that is a new concept makes possible the construction improving and the cost saving in the excavation site. From the field test results, it was observed that the pullout resistance of Jacket Pack Anchor was about 84% greater than that of general ground anchor, and plastic deformation of Jacket Pack Anchor compared to that of general ground anchor was about 35% at the same load cycle. Especially, it was showed that the increase of resistance over 200% and plastic deformation was about 17% in gravel layer. This method has been applied mainly in the soft reclaimed soil and marine deposit areas of Inchon and Pusan etc. or in the loose layer of urban waste landfill. From the result of these cases, its usefulness has been proved because of ground displacement and building damage with little during the excavation work. Therefore, we propose strongly to try Jacket Pack Anchor in the past difficult layer from this paper. Also, we are hoping to take the full advantage of ground anchor that is secure enough workspace to minimize disturbance of excavation or underground structure can improve work efficiency, using Jacket Pack Anchor in the excavation site

Audit Opinion and Earnings Quality: Evidence from Korea

This study examines whether the decrease in qualified opinions is due to improved accruals quality. The proportion of qualified opinions has been declining in Korea for about 10 years. However, it has not been reported that earnings quality has improved. We analyze this contradictory relationship using two models. We find that Korean firms’ accruals quality has no association with unqualified opinions. This means that the increasing trend in unqualified opinions is occurring regardless of earnings quality, although audit opinion chiefly depends on it. Thus, our results suggest that more researches are required to determine why qualified opinions are declining. Keywords: audit opinion; earnings quality; accruals DOI: 10.7176/EJBM/12-30-02 Publication date:October 31st 202

Efficient Video Representation Learning via Masked Video Modeling with Motion-centric Token Selection

Self-supervised Video Representation Learning (VRL) aims to learn transferrable representations from uncurated, unlabeled video streams that could be utilized for diverse downstream tasks. With recent advances in Masked Image Modeling (MIM), in which the model learns to predict randomly masked regions in the images given only the visible patches, MIM-based VRL methods have emerged and demonstrated their potential by significantly outperforming previous VRL methods. However, they require an excessive amount of computations due to the added temporal dimension. This is because existing MIM-based VRL methods overlook spatial and temporal inequality of information density among the patches in arriving videos by resorting to random masking strategies, thereby wasting computations on predicting uninformative tokens/frames. To tackle these limitations of Masked Video Modeling, we propose a new token selection method that masks our more important tokens according to the object's motions in an online manner, which we refer to as Motion-centric Token Selection. Further, we present a dynamic frame selection strategy that allows the model to focus on informative and causal frames with minimal redundancy. We validate our method over multiple benchmark and Ego4D datasets, showing that the pre-trained model using our proposed method significantly outperforms state-of-the-art VRL methods on downstream tasks, such as action recognition and object state change classification while largely reducing memory requirements during pre-training and fine-tuning.Comment: 15 page