Impact of Connected Vehicles on Mitigating Secondary Crash Risk

Reducing the risk of secondary crashes is a key goal for effective traffic incident management. However, only few countermeasures have been established in practices to achieve the goal. This is mainly due to the stochastic nature of both primary and secondary crashes. Given the emerging connected vehicle (CV) technologies, it is highly likely that CVs will soon be able to communicate with each other through the ad-hoc wireless vehicular network. Information sharing among vehicles is deemed to change traffic operations and allow motorists for more proactive actions. Motorists who receive safety messages can be motivated to approach queues and incident sites with more caution. As a result of the improved situational awareness, the risk of secondary crashes is expected to be reduced. To examine whether this expectation is achievable or not, this study aims to assess the impact of connectivity on the risk of secondary crashes. A simulation-based modeling framework that enables vehicle-to-vehicle communication module was developed. Since crashes cannot be directly simulated in micro-simulation, the use of surrogate safety measures was proposed to capture vehicular conflicts as a proxy for secondary crash risk upstream of a primary crash site. An experimental study was conducted based on the developed simulation modeling framework. The results show that the use of connected vehicles can be a viable way to reduce the risk of secondary crashes. Their impact is expected to change with an increasing market penetration of connected vehicles. © 2017 Tongji University and Tongji University Press

The Nitrogen Budget of Coastal Eastern Guangdong in the Last 15 Years

Nitrogen pollution has caused severe ecological and environmental crisis, especially in densely populated coastal regions. Using a mathematical model based on statistical data series from industry, agriculture, environmental protection, and population in 2000, 2005, 2010, and 2015, this paper aims to estimate the nitrogen income and expenditure of coastal Eastern Guangdong, to reveal the temporal variation of the nitrogen budget in the coastal region with high agriculture intensity, and to suggest a management strategy for the local nitrogen control. The results show that: coastal Eastern Guangdong is a nitrogen surplus region, with nitrogen load and nitrogen flux varying in the range 276.01–299.60 kg N ha−1 yr−1 and 221.26–239.06 kg N ha−1 yr−1, respectively, during the period 2000–2015; from 2000 to 2015, the overall nitrogen surplus and the nitrogen surplus unit area showed an obvious upward trend, indicating that nitrogen pollution in the area was deteriorating; agricultural used fertilizer serves as the main contributor to nitrogen input, while water nitrogen accounts for the highest portion of nitrogen output; despite the fluctuation of nitrogen input and output, water nitrogen output steadily increased, suggesting a stronger water environment management requirement. This research provides reference for researchers and decision-makers in the ecological and environmental domain

Research and Practice on the Training Mode of Master of Finance Under the Background of Fin Tech

In the trend of Fin Tech, the training mode of Master of Finance(MF) is in urgent need of reform and innovation. In this paper, the author proposes to build a three in one postgraduate training model of "science and technology, application and internationalization" to strengthen the training quality of MF, through the establishment of a curriculum system that integrates Fin Tech and finance; organic embedding of cross courses such as artificial intelligence; building a multi faculty, implementing the dual tutor system; building an international training platform, cultivating the international vision of teachers and students; exploring the training mechanism of professional qualification embedding, encouraging students to obtain high-end professional qualification certificates such as CFA;developing practice bases for in-depth cooperation

Microscopic changing-mass model of PVA hydrogel under unidirectional compression

PVA (Polyvinyl Alcohol) hydrogel is a kind of soft materials, with nontoxic and good biocompatibility. This kind of hydrogel shows strong nonlinearity in the static compression tests because of the high water content (~60‑90%), and the water is squeezed out when the PVA hydrogel is compressed. It means that the PVA hydrogel losts the mass during compression. Aiming at the changing mass mechanism and constitution in the compression of PVA hydrogel, we established a simplified microscopic model which is a single layer frame composite structure consisting of PVA fibers, water, and virtual membrane, in which the membrane wraps outside surfaces of the cubic cells and has no mass, but its membrane force has same properties with the surface tension of water. In the model, the deformation of PVA fibers and the compressive water sustains the external stress when the PVA hydrogel is compressed. In addition, by considering the limitation of the maximum membrane force induced by compressive water, the squeezed water will be calculated in each compressive step and the mechanism of the changing mass is determined quantitatively; in the meantime, the constitution of the PVA hydrogel may be deduced

Fashion Matrix: Editing Photos by Just Talking

The utilization of Large Language Models (LLMs) for the construction of AI systems has garnered significant attention across diverse fields. The extension of LLMs to the domain of fashion holds substantial commercial potential but also inherent challenges due to the intricate semantic interactions in fashion-related generation. To address this issue, we developed a hierarchical AI system called Fashion Matrix dedicated to editing photos by just talking. This system facilitates diverse prompt-driven tasks, encompassing garment or accessory replacement, recoloring, addition, and removal. Specifically, Fashion Matrix employs LLM as its foundational support and engages in iterative interactions with users. It employs a range of Semantic Segmentation Models (e.g., Grounded-SAM, MattingAnything, etc.) to delineate the specific editing masks based on user instructions. Subsequently, Visual Foundation Models (e.g., Stable Diffusion, ControlNet, etc.) are leveraged to generate edited images from text prompts and masks, thereby facilitating the automation of fashion editing processes. Experiments demonstrate the outstanding ability of Fashion Matrix to explores the collaborative potential of functionally diverse pre-trained models in the domain of fashion editing.Comment: 13 pages, 5 figures, 2 table

Deeply Virtual Compton Scattering at Future Electron-Ion Colliders

The study of hadronic structure has been carried out for many years. Generalized parton distribution functions (GPDs) give broad information on the internal structure of hadrons. Combining GPDs and high-energy scattering experiments, we expect yielding three-dimensional physical quantities from experiments. Deeply Virtual Compton Scattering (DVCS) process is a powerful tool to study GPDs. It is one of the important experiments of Electron Ion Collider (EIC) and Electron ion collider at China (EicC) in the future. In the initial stage, the proposed EicC will have $3 \sim 5$ GeV polarized electrons on $12 \sim 25$ GeV polarized protons, with luminosity up to $1 \sim 2 \times 10^{33}$cm$^{-2}$s$^{-1}$. EIC will be constructed in coming years, which will cover the variable c.m. energies from 30 to 50 GeV, with the luminosity about $10^{33} \sim 10^{34}$cm$^{-2}$s$^{-1}$. In this work we present a detailed simulation of DVCS to study the feasibility of experiments at EicC and EIC. Referring the method used by HERMES Collaboration, and comparing the model calculations with pseudo data of asymmetries attributed to the DVCS, we obtained a model-dependent constraint on the total angular momentum of up and down quarks in the proton.Comment: 12 pages, 18 figures, 3 Table