607 research outputs found
Nitric acid activation of graphite granules to increase the performance of the non-catalyzed oxygen reduction reaction (ORR) for MFC applications
Nitric acid and thermal activation of graphite granules were explored to increase the electrocatalytic performance of dissolved oxygen reduction at neutral pH for microbial fuel cell (MFC) applications. Electrochemical experiments showed an improvement of +400 mV in open circuit potential for graphite granules when they were activated. The improvement of ORR performance observed with activated granules was correlated to the increase of Brunauer–Emmett–Teller (BET) surface of the activated material and the emergence of nitrogen superficial groups revealed by X-ray photoelectron spectroscopy (XPS) analysis on its surface. The use of activated graphite granules in the cathodic compartment of a dual-chamber MFC led to a high open circuit voltage of 1050 mV, which is among one of the highest reported so far. The stable performance of this cathode material (current density of 96 A m−3 at +200 mV/Ag–AgCl) over a period of 10 days demonstrated its applicability as a cathode material without any costly noble metal
The Reconstruction of China's Regional Economic Pattern under the Background of "The Belt and Road" Strategy
The excessive development structure between China regions is generated in the process of industrialization and urbanization, and it is concentrated in an unequal spatial structure. The strategic concept of "The Belt and Road" provides an opportunity for the coordinated development of China's regional economy. On the one hand, it helps to promote economic transformation and upgrading in eastern China. On the other hand, it is helpful to realize the rising of central China and the opening of the western border zone, and optimize the regional development pattern. In this study, we propose that the regional coordination should be established by using "The Belt and Road" strategy. First of all, building a multi-center urban network to stimulate the economic development in the central and western regions; secondly, balancing the interests of different regions with a multi-level regional coordination mechanism and finally, forming a new pattern of coordinated regional development with institutionalized regional ecosystems. Keywords: The Belt and Road, regional imbalance, multi-center, regional coordination mechanism, institutio
Analysis of Spatial Travel Association Rules for Rail Transit Based on AFC and POI Data
In order to explore the spatial distribution rules and causes of urban rail transit passenger travel, this paper mines the spatial 1-frequent itemset and 2-frequent itemsets of weekdays and weekends metro passenger travel based on Apriori algorithm using the continuous week of Automatic Fare Collection System (AFC) swipe card. At the same time, the K-Means algorithm is used to cluster the subway stations and explore the causes of association rules by combining the Point of Interest (POI) data of the same period within the radiation range of the subway stations. The study shows that the spatial distribution pattern of inbound and outbound passenger flow of Shanghai rail transit is consistent between weekdays and weekends, and the outbound passenger flow is more concentrated than the inbound passenger flow, and the significance of weekends is higher; the spatial distribution of metro stations is "circled"; the analysis of the high-lift association rules show that a large passenger flow group centered on the type 3 station is formed in the spatial location, and the passenger flow within the group is mainly commuter flow with separation of employment and residence. The association rule mining of metro passenger travel data is beneficial to understanding the spatial distribution pattern and causes of metro ridership, which can provide reference for rail network planning and operation management
Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction
Proximal gradient-based optimization is one of the most common strategies for
solving image inverse problems as well as easy to implement. However, these
techniques often generate heavy artifacts in image reconstruction. One of the
most popular refinement methods is to fine-tune the regularization parameter to
alleviate such artifacts, but it may not always be sufficient or applicable due
to increased computational costs. In this work, we propose a deep geometric
incremental learning framework based on second Nesterov proximal gradient
optimization. The proposed end-to-end network not only has the powerful
learning ability for high/low frequency image features,but also can
theoretically guarantee that geometric texture details will be reconstructed
from preliminary linear reconstruction.Furthermore, it can avoid the risk of
intermediate reconstruction results falling outside the geometric decomposition
domains and achieve fast convergence. Our reconstruction framework is
decomposed into four modules including general linear reconstruction, cascade
geometric incremental restoration, Nesterov acceleration and post-processing.
In the image restoration step,a cascade geometric incremental learning module
is designed to compensate for the missing texture information from different
geometric spectral decomposition domains. Inspired by overlap-tile strategy, we
also develop a post-processing module to remove the block-effect in
patch-wise-based natural image reconstruction. All parameters in the proposed
model are learnable,an adaptive initialization technique of physical-parameters
is also employed to make model flexibility and ensure converging smoothly. We
compare the reconstruction performance of the proposed method with existing
state-of-the-art methods to demonstrate its superiority. Our source codes are
available at https://github.com/fanxiaohong/Nest-DGIL.Comment: 15 page
AeDet: Azimuth-invariant Multi-view 3D Object Detection
Recent LSS-based multi-view 3D object detection has made tremendous progress,
by processing the features in Brid-Eye-View (BEV) via the convolutional
detector. However, the typical convolution ignores the radial symmetry of the
BEV features and increases the difficulty of the detector optimization. To
preserve the inherent property of the BEV features and ease the optimization,
we propose an azimuth-equivariant convolution (AeConv) and an
azimuth-equivariant anchor. The sampling grid of AeConv is always in the radial
direction, thus it can learn azimuth-invariant BEV features. The proposed
anchor enables the detection head to learn predicting azimuth-irrelevant
targets. In addition, we introduce a camera-decoupled virtual depth to unify
the depth prediction for the images with different camera intrinsic parameters.
The resultant detector is dubbed Azimuth-equivariant Detector (AeDet).
Extensive experiments are conducted on nuScenes, and AeDet achieves a 62.0%
NDS, surpassing the recent multi-view 3D object detectors such as PETRv2 (58.2%
NDS) and BEVDepth (60.0% NDS) by a large margin. Project page:
https://fcjian.github.io/aedet.Comment: Tech repor
A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing
Remote sensing images are essential for many earth science applications, but
their quality can be degraded due to limitations in sensor technology and
complex imaging environments. To address this, various remote sensing image
deblurring methods have been developed to restore sharp, high-quality images
from degraded observational data. However, most traditional model-based
deblurring methods usually require predefined hand-craft prior assumptions,
which are difficult to handle in complex applications, and most deep
learning-based deblurring methods are designed as a black box, lacking
transparency and interpretability. In this work, we propose a novel blind
deblurring learning framework based on alternating iterations of shrinkage
thresholds, alternately updating blurring kernels and images, with the
theoretical foundation of network design. Additionally, we propose a learnable
blur kernel proximal mapping module to improve the blur kernel evaluation in
the kernel domain. Then, we proposed a deep proximal mapping module in the
image domain, which combines a generalized shrinkage threshold operator and a
multi-scale prior feature extraction block. This module also introduces an
attention mechanism to adaptively adjust the prior importance, thus avoiding
the drawbacks of hand-crafted image prior terms. Thus, a novel multi-scale
generalized shrinkage threshold network (MGSTNet) is designed to specifically
focus on learning deep geometric prior features to enhance image restoration.
Experiments demonstrate the superiority of our MGSTNet framework on remote
sensing image datasets compared to existing deblurring methods.Comment: 12 pages
Towards LLM-driven Dialogue State Tracking
Dialogue State Tracking (DST) is of paramount importance in ensuring accurate
tracking of user goals and system actions within task-oriented dialogue
systems. The emergence of large language models (LLMs) such as GPT3 and ChatGPT
has sparked considerable interest in assessing their efficacy across diverse
applications. In this study, we conduct an initial examination of ChatGPT's
capabilities in DST. Our evaluation uncovers the exceptional performance of
ChatGPT in this task, offering valuable insights to researchers regarding its
capabilities and providing useful directions for designing and enhancing
dialogue systems. Despite its impressive performance, ChatGPT has significant
limitations including its closed-source nature, request restrictions, raising
data privacy concerns, and lacking local deployment capabilities. To address
these concerns, we present LDST, an LLM-driven DST framework based on smaller,
open-source foundation models. By utilizing a novel domain-slot instruction
tuning method, LDST achieves performance on par with ChatGPT. Comprehensive
evaluations across three distinct experimental settings, we find that LDST
exhibits remarkable performance improvements in both zero-shot and few-shot
setting compared to previous SOTA methods. The source code is provided for
reproducibility.Comment: Accepted at EMNLP 202
Incomplete Wood-Ljungdahl pathway facilitates one-carbon metabolism in organohalide-respiring Dehalococcoides mccartyi.
The acetyl-CoA "Wood-Ljungdahl" pathway couples the folate-mediated one-carbon (C1) metabolism to either CO2 reduction or acetate oxidation via acetyl-CoA. This pathway is distributed in diverse anaerobes and is used for both energy conservation and assimilation of C1 compounds. Genome annotations for all sequenced strains of Dehalococcoides mccartyi, an important bacterium involved in the bioremediation of chlorinated solvents, reveal homologous genes encoding an incomplete Wood-Ljungdahl pathway. Because this pathway lacks key enzymes for both C1 metabolism and CO2 reduction, its cellular functions remain elusive. Here we used D. mccartyi strain 195 as a model organism to investigate the metabolic function of this pathway and its impacts on the growth of strain 195. Surprisingly, this pathway cleaves acetyl-CoA to donate a methyl group for production of methyl-tetrahydrofolate (CH3-THF) for methionine biosynthesis, representing an unconventional strategy for generating CH3-THF in organisms without methylene-tetrahydrofolate reductase. Carbon monoxide (CO) was found to accumulate as an obligate by-product from the acetyl-CoA cleavage because of the lack of a CO dehydrogenase in strain 195. CO accumulation inhibits the sustainable growth and dechlorination of strain 195 maintained in pure cultures, but can be prevented by CO-metabolizing anaerobes that coexist with D. mccartyi, resulting in an unusual syntrophic association. We also found that this pathway incorporates exogenous formate to support serine biosynthesis. This study of the incomplete Wood-Ljungdahl pathway in D. mccartyi indicates a unique bacterial C1 metabolism that is critical for D. mccartyi growth and interactions in dechlorinating communities and may play a role in other anaerobic communities
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