A Dynamic Competition Control Strategy for Freeway Merging Region Balancing Individual Behaviour and Traffic Efficiency

An integrated control strategy is considered in this paper with the aim of solving congestion in freeway merging regions during peak hours. Merging regions discussed in this paper include the mainline and on-ramp. Traditional research mainly focuses on the efficiency of traffic, ignoring the experience of on-ramp drivers and passengers. Accordingly, a dynamic competition control strategy is proposed to balance individual behaviour and traffic efficiency. First, the concept of the congestion index is introduced, which is expressed by the queue length and the speed parameter of the merging region. The congestion index is used to balance the priorities of the vehicles from the mainline and on-ramp into the merging region in order to avoid poor individual behaviour of on-ramp drivers due to the long-time waiting. Additionally, a nonlinear optimal control approach integrating variable speed limits control and ramp metering is proposed to minimize the total time spent and the maximum traffic flow. The integrated control approach proposed in this paper is tested by simulation which is calibrated using field data. The results indicate that the integrated control approach can effectively shorten the total delay and enhance the traffic service level.</p

An Examination of the US Residential Heating Market

This paper outlines the US residential space heating market and highlights thirteen disruptive companies whose products decarbonize some link in the space heating supply chain. The goal of the paper is to provide Energy Impact Partners (EIP) with a strong understanding of market trends, regional switching costs, customer behaviors, and policy incentives. Additionally, we present an investment landscape of disruptive companies from which EIP may choose to pursue specific investment objectives. The US residential space heating market may be thought of as a mix of space heating fuel sources, such as natural gas and electricity, and a mix of space heating technologies, such as Furnaces and Heat Pumps. Four major trends stick out. First, Furnaces dominate the technology landscape as the most popular heating technology. Second, natural gas and electricity are the two main fuel types used for space heating, with 51% of households using natural gas and 37% of households using electricity. Third, the mixes of fuel and equipment have changed since 2001 largely due to higher population growth in southern regions where electricity and Heat Pumps provide space heating for most homes. Fourth, according to utility executives interviewed the mix of fuel and technology will not change drastically over the next ten years. Payback periods calculated are often long, greater than 10 years, making the switch to less carbon intensive fuel sources or less energy intensive technologies less appealing to the average homeowner. Furthermore, customer behavior hinders the switch to decarbonizing technologies because most individuals do not view space heating equipment as aspirational purchases and will only replace equipment upon failure – which often happens during the winter – forcing them to seek out the quickest fix rather than shop around for an alternative option, even if that option can save money through lower operating costs. Several federal and state incentives exist to motivate homeowners to decarbonize their space heating system. More details are provided in Chapter 7. Ultimately, the paper concludes with four insights for EIP with regards to investing in space heating startups. These insights revolve around the projected energy and technology mix, where innovation occurs in the space heating supply chain, customer behavior in purchasing decisions, and the importance of government policy for a startup’s success.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/146738/1/An Examination of the US Residential Heating Market_338.pd

Computing the ground state and dynamics of the nonlinear Schrödinger equation with nonlocal interactions via the nonuniform FFT

International audienceWe present efficient and accurate numerical methods for computing the ground state and dynamics of the nonlinear Schrödinger equation (NLSE) with nonlocal interactions based on a fast and accurate evaluation of the long-range interactions via the nonuniform fast Fourier transform (NUFFT). We begin with a review of the fast and accurate NUFFT based method in [29] for nonlocal interactions where the singularity of the Fourier symbol of the interaction kernel at the origin can be canceled by switching to spherical or polar coordinates. We then extend the method to compute other nonlocal interactions whose Fourier symbols have stronger singularity at the origin that cannot be canceled by the coordinate transform. Many of these interactions do not decay at infinity in the physical space, which adds another layer of complexity since it is more difficult to impose the correct artificial boundary conditions for the truncated bounded computational domain. The performance of our method against other existing methods is illustrated numerically, with particular attention on the effect of the size of the computational domain in the physical space. Finally, to study the ground state and dynamics of the NLSE, we propose efficient and accurate numerical methods by combining the NUFFT method for potential evaluation with the normalized gradient flow using backward Euler Fourier pseudospectral discretization and time-splitting Fourier pseudospectral method, respectively. Extensive numerical comparisons are carried out between these methods and other existing methods for computing the ground state and dynamics of the NLSE with various nonlocal interactions. Numerical results show that our scheme performs much better than those existing methods in terms of both accuracy and efficiency

Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI