Blue-Green Infrastructure for Sustainable Urban Stormwater Management—Lessons from Six Municipality-Led Pilot Projects in Beijing and Copenhagen

Managing stormwater on urban surfaces with blue-green infrastructure (BGI) is being increasingly adopted as an alternative to conventional pipe-based stormwater management in cities. BGI combats water problems and provides multiple benefits for cities, including improved livability and enhanced biodiversity. The paper examines six municipality-led pilot projects from Beijing and Copenhagen, through a review of documents, site observations and interviews with project managers. Beijing’s projects attempt to divert from a pipe-based approach but are dominated by less BGI-based solutions; they could benefit from more integration of multiple benefits with stormwater management. Copenhagen’s projects combine stormwater management with amenity improvement, but lack focus on stormwater utilization. Reviewed municipality-led pilot projects are shown to play an important role in both testing new solutions and upscaling them in the process of developing more sustainable cities. Key lessons are extracted and a simple guideline synthesized. This guideline suggests necessary considerations for a holistic solution that combines stormwater management and urban space improvements. Key lessons for sustainable solutions include defining a clear water technique priority, targeting both small and big rain events, strengthening ‘vertical design’ and providing multiple benefits. An integrated stormwater management and landscape design process is a prerequisite to the meaningful implementation of these solutions. Research and documentation integrated with pilot projects will help upscale the practice at city scale

3D Chocolate Printer Dropper

The Mechanical Engineering Department at Washington University in St. Louis is working to stimulate interest in the fields of fluid dynamics and thermal sciences, as students are not typically exposed to these topics within the first two years of school. Dr. Okamoto, Jeff Krampf, and Dr. Weisensee of the Mechanical Engineering Department would like to remedy this situation by developing a laboratory experiment for first year students that utilizes a 3D chocolate printer to teach thermal-fluid concepts in a fun and engaging manner. The goal of this project is to build a chocolate droplet dispensing system, which is a part of the 3D chocolate printing machine. The device must be able to melt chocolate and generate droplets in consistent and adjustable time intervals. The dispensing height of the nozzle should be manually changeable so that the students can understand how height and frequency influence the droplet impact. While the primary function of this device is to help students learn thermal-fluids in a fun yet educational environment, it is also imperative that the device is safe for students to use

Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach

Mobile energy storage systems (MESSs) provide mobility and flexibility to enhance distribution system resilience. The paper proposes a Markov decision process (MDP) formulation for an integrated service restoration strategy that coordinates the scheduling of MESSs and resource dispatching of microgrids. The uncertainties in load consumption are taken into account. The deep reinforcement learning (DRL) algorithm is utilized to solve the MDP for optimal scheduling. Specifically, the twin delayed deep deterministic policy gradient (TD3) is applied to train the deep Q-network and policy network, then the well trained policy can be deployed in on-line manner to perform multiple actions simultaneously. The proposed model is demonstrated on an integrated test system with three microgrids connected by Sioux Falls transportation network. The simulation results indicate that mobile and stationary energy resources can be well coordinated to improve system resilience.Comment: Submitted to 2020 IEEE Power and Energy Society General Meetin

I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset

A simile is a figure of speech that compares two different things (called the tenor and the vehicle) via shared properties. The tenor and the vehicle are usually connected with comparator words such as "like" or "as". The simile phenomena are unique and complex in a real-life dialogue scene where the tenor and the vehicle can be verbal phrases or sentences, mentioned by different speakers, exist in different sentences, or occur in reversed order. However, the current simile research usually focuses on similes in a triplet tuple (tenor, property, vehicle) or a single sentence where the tenor and vehicle are usually entities or noun phrases, which could not reflect complex simile phenomena in real scenarios. In this paper, we propose a novel and high-quality multilingual simile dialogue (MSD) dataset to facilitate the study of complex simile phenomena. The MSD is the largest manually annotated simile data ($\sim$20K) and it contains both English and Chinese data. Meanwhile, the MSD data can also be used on dialogue tasks to test the ability of dialogue systems when using similes. We design 3 simile tasks (recognition, interpretation, and generation) and 2 dialogue tasks (retrieval and generation) with MSD. For each task, we provide experimental results from strong pre-trained or state-of-the-art models. The experiments demonstrate the challenge of MSD and we have released the data/code on GitHub.Comment: 13 Pages, 1 Figure, 12 Tables, ACL 2023 finding

An Efficient Universal Noise Removal Algorithm Combining Spatial Gradient and Impulse Statistic

We propose a novel universal noise removal algorithm by combining spatial gradient and a new impulse statistic into the trilateral filter. By introducing a reference image, an impulse statistic is proposed, which is called directional absolute relative differences (DARD) statistic. Operation was carried out in two stages: getting reference image and image denoising. For denoising, we introduce the spatial gradient into the Gaussian filtering framework for Gaussian noise removal and integrate our DARD statistic for impulse noise removal, and finally we combine them together to create a new trilateral filter for mixed noise removal. Simulation results show that our noise detector has a high classification rate, especially for salt-and-pepper noise. And the proposed approach achieves great results both in terms of quantitative measures of signal restoration and qualitative judgments of image quality. In addition, the computational complexity of the proposed method is less than that of many other mixed noise filters