Optimization of thermochemical heat storage systems by controlling operating parameters and using two reactors

Direct CO2 emissions from space heating and hot water production in buildings has been on a rising trend in recent decades. It is increasingly urgent to develop efficient and low-carbon heating technologies that can reduce energy consumption and shift the load to off-peak times. This work concerns thermochemical heat storage (TCHS), which has the potential to offer flexibility to bridge the energy supply and demand mismatches, and help with load shifting. One of the technical barriers for the use of TCHS is the variation of the outlet conditions for discharge process, which limits the implementation and competitiveness of the technology. Here we propose a new method to overcome the barrier. By using packed-bed based thermochemical reactors packed with silica gel, as an example, we use a Computational Fluid Dynamic (CFD) tool to understand the effectiveness of controlling and optimising the outlet conditions of the TCHS reactor. We demonstrated that, by optimizing the packed bed, a stable outlet temperature could be achieved. Furthermore, the whole TCHS performance could be enhanced, doubling the discharging power and prolonged discharge time by 4 times while keeping the same outlet temperature

Public Restroom Access and Mental Health Among Gender-Minoritized Individuals in China

open access articleThis cross-sectional study assesses the adequacy of gender-neutral public restrooms and examines the association of public restroom–related stress with mental health among gender-diverse individuals in China

Polycomb group proteins EZH2 and EED directly regulate androgen receptor in advanced prostate cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/1/ijc32118.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/2/ijc32118_am.pd

DeepDyve: Dynamic Verification for Deep Neural Networks

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as adversarial example attacks and fault injection attacks. While there are many defense methods proposed against maliciously crafted inputs, solutions against faults presented in the DNN system itself (e.g., parameters and calculations) are far less explored. In this paper, we develop a novel lightweight fault-tolerant solution for DNN-based systems, namely DeepDyve, which employs pre-trained neural networks that are far simpler and smaller than the original DNN for dynamic verification. The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much. We develop efficient and effective architecture and task exploration techniques to achieve optimized risk/overhead trade-off in DeepDyve. Experimental results show that DeepDyve can reduce 90% of the risks at around 10% overhead