445 research outputs found
Intermittent Prediction Method Based On Marcov Method And Grey Prediction Method
This paper concentrates on the intermittent demand for electric power supply and studies the method of demand prediction. This chapter first divides the demand for electric power supply into two statistical sequences: (1) sequence of demand occurrence, among which “1”stands for the occurrence of demand,“0”means that the demand fails to occur; (2) sequence of demand quantity. Next the author predicts the moment of time and the number of times n that demand occurs within a specific time interval in the future based on 0-1 sequence using Markov arrival process (MAP). Then the paper forecasts the demand quantity in subsequent n intervals using Grey prediction model GM (1, 1) based on the sequence of demand quantity. Finally, the author places the demand quantity in the n intervals in order at the moments where demand occurs to get the predicted result of demand for electric material with intermittent demand. According to instance analysis, the integrated approach mentioned in this paper surpasses existing methods in providing accurate prediction on data of product with intermittent demand
Review of Energy Transition Policies in Singapore, London, and California
The paper contains the online supplementary materials for "Data-Driven
Prediction and Evaluation on Future Impact of Energy Transition Policies in
Smart Regions". We review the renewable energy development and policies in the
three metropolitan cities/regions over recent decades. Depending on the
geographic variations in the types and quantities of renewable energy resources
and the levels of policymakers' commitment to carbon neutrality, we classify
Singapore, London, and California as case studies at the primary, intermediate,
and advanced stages of the renewable energy transition, respectively
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
Cell segmentation for multi-modal microscopy images remains a challenge due
to the complex textures, patterns, and cell shapes in these images. To tackle
the problem, we first develop an automatic cell classification pipeline to
label the microscopy images based on their low-level image characteristics, and
then train a classification model based on the category labels. Afterward, we
train a separate segmentation model for each category using the images in the
corresponding category. Besides, we further deploy two types of segmentation
models to segment cells with roundish and irregular shapes respectively.
Moreover, an efficient and powerful backbone model is utilized to enhance the
efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS
2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and
the running time for all cases is within the time tolerance.Comment: The second place in NeurIPS 2022 cell segmentation challenge
(https://neurips22-cellseg.grand-challenge.org/), released code:
https://github.com/lhaof/CellSe
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Nuclei segmentation is a fundamental but challenging task in the quantitative
analysis of histopathology images. Although fully-supervised deep
learning-based methods have made significant progress, a large number of
labeled images are required to achieve great segmentation performance.
Considering that manually labeling all nuclei instances for a dataset is
inefficient, obtaining a large-scale human-annotated dataset is time-consuming
and labor-intensive. Therefore, augmenting a dataset with only a few labeled
images to improve the segmentation performance is of significant research and
application value. In this paper, we introduce the first diffusion-based
augmentation method for nuclei segmentation. The idea is to synthesize a large
number of labeled images to facilitate training the segmentation model. To
achieve this, we propose a two-step strategy. In the first step, we train an
unconditional diffusion model to synthesize the Nuclei Structure that is
defined as the representation of pixel-level semantic and distance transform.
Each synthetic nuclei structure will serve as a constraint on histopathology
image synthesis and is further post-processed to be an instance map. In the
second step, we train a conditioned diffusion model to synthesize
histopathology images based on nuclei structures. The synthetic histopathology
images paired with synthetic instance maps will be added to the real dataset
for training the segmentation model. The experimental results show that by
augmenting 10% labeled real dataset with synthetic samples, one can achieve
comparable segmentation results with the fully-supervised baseline.Comment: MICCAI 2023, released code: https://github.com/lhaof/Nudif
Effect of heat input on nanomechanical properties of wire-arc additive manufactured Al 4047 alloys
Heat input is one of the most important process parameters during additive manufacturing (AM). It is of great significance to understand the effect of heat input on the microstructure and nanomechanical properties, as well as the underlying mechanisms. Wire-arc additive manufactured (WAAM-ed) Al 4047 alloys under different heat inputs were produced and studied in this work. The as-manufactured Al alloys showed hypoeutectic microstructure that consisted of primary Al (α-Al) dendrite and ultrafine Al–Si eutectic. The effect of heat input on hardness and strain rate sensitivity (SRS) were investigated through nanoindentation. The nanohardness decreased with the increasing heat input, in accordance with the trend of yield strength and microhardness in the previous studies, in which the mechanism was usually explained by the grain growth model and Hall-Petch relationship. This work suggests a distinct mechanism regarding the effect of heat input on nanohardness, which is the enhanced solid solution strengthening produced by lower heat input. In addition, the heat input had little effect on the SRS and activation volume. It is hoped that this study leads to new insights into the understanding of the relation between heat input and nanomechanical properties, and further benefits to improve the targeted mechanical properties and engineering applications of the AM-ed materials.publishedVersio
The influence of dietary intake of omega‑3 polyunsaturated fatty acids on the association between short‑term exposure to ambient nitrogen dioxide and respiratory and cardiovascular outcomes among healthy adults
Omega-3 fatty acids attenuate cardiovascular effects of short-term exposure to ambient air pollution
Exposure to air pollution is associated with elevated cardiovascular risk. Evidence shows that omega-3 polyunsaturated fatty acids (omega-3 PUFA) may attenuate the adverse cardiovascular effects of exposure to fine particulate matter (PM2.5). However, it is unclear whether habitual dietary intake of omega-3 PUFA protects against the cardiovascular effects of short-term exposure to low-level ambient air pollution in healthy participants. In the present study, sixty-two adults with low or high dietary omega-3 PUFA intake were enrolled. Blood lipids, markers of vascular inflammation, coagulation and fibrinolysis, and heart rate variability (HRV) and repolarization were repeatedly assessed in 5 sessions separated by at least 7 days. This study was carried out in the Research Triangle area of North Carolina, USA between October 2016 and September 2019. Daily PM2.5 and maximum 8-h ozone (O3) concentrations were obtained from nearby air quality monitoring stations. Linear mixed-effects models were used to assess the associations between air pollutant concentrations and cardiovascular responses stratified by the omega-3 intake levels
Magnon-mediated interlayer coupling in an all-antiferromagnetic junction
The interlayer coupling mediated by fermions in ferromagnets brings about
parallel and anti-parallel magnetization orientations of two magnetic layers,
resulting in the giant magnetoresistance, which forms the foundation in
spintronics and accelerates the development of information technology. However,
the interlayer coupling mediated by another kind of quasi-particle, boson, is
still lacking. Here we demonstrate such a static interlayer coupling at room
temperature in an antiferromagnetic junction Fe2O3/Cr2O3/Fe2O3, where the two
antiferromagnetic Fe2O3 layers are functional materials and the
antiferromagnetic Cr2O3 layer serves as a spacer. The N\'eel vectors in the top
and bottom Fe2O3 are strongly orthogonally coupled, which is bridged by a
typical bosonic excitation (magnon) in the Cr2O3 spacer. Such an orthogonally
coupling exceeds the category of traditional collinear interlayer coupling via
fermions in ground state, reflecting the fluctuating nature of the magnons, as
supported by our magnon quantum well model. Besides the fundamental
significance on the quasi-particle-mediated interaction, the strong coupling in
an antiferromagnetic magnon junction makes it a realistic candidate for
practical antiferromagnetic spintronics and magnonics with ultrahigh-density
integration.Comment: 19 pages, 4 figure
MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV
Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe
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