722 research outputs found
Biomass-derived three-dimensional porous N-doped carbonaceous aerogel for efficient supercapacitor electrodes
Functionalized carbonaceous materials with hierarchical structure and developed porosity are highly desired in energy storage and conversion fields. In this work, a facile and scalable hydrothermal methodology was established to synthesise three-dimensional (3D) N-doped carbonaceous aerogels using biomass-based starting materials and polypyrrole as N-source. The effect of different calcination temperatures on the structural properties, type and content of N-species and electrochemical performance of the 3D N-doped carbonaceous aerogels were uncovered. Thanks to the combinatorial effect of the appropriate N content and porous structure, the obtained samples exhibited excellent electrochemical performance, in particular, an outstanding specific capacitance of 281.0 F g-1 achieved on the sample calcined at 600 °C. This methodology offers a new fabrication strategy to prepare nanoscale carbonaceous materials with desirable morphology and hierarchical architecture of great potentials for the applications in energy fields
PreciseBugCollector: Extensible, Executable and Precise Bug-fix Collection
Bug datasets are vital for enabling deep learning techniques to address
software maintenance tasks related to bugs. However, existing bug datasets
suffer from precise and scale limitations: they are either small-scale but
precise with manual validation or large-scale but imprecise with simple commit
message processing. In this paper, we introduce PreciseBugCollector, a precise,
multi-language bug collection approach that overcomes these two limitations.
PreciseBugCollector is based on two novel components: a) A bug tracker to map
the codebase repositories with external bug repositories to trace bug type
information, and b) A bug injector to generate project-specific bugs by
injecting noise into the correct codebases and then executing them against
their test suites to obtain test failure messages.
We implement PreciseBugCollector against three sources: 1) A bug tracker that
links to the national vulnerability data set (NVD) to collect general-wise
vulnerabilities, 2) A bug tracker that links to OSS-Fuzz to collect
general-wise bugs, and 3) A bug injector based on 16 injection rules to
generate project-wise bugs. To date, PreciseBugCollector comprises 1057818 bugs
extracted from 2968 open-source projects. Of these, 12602 bugs are sourced from
bug repositories (NVD and OSS-Fuzz), while the remaining 1045216
project-specific bugs are generated by the bug injector. Considering the
challenge objectives, we argue that a bug injection approach is highly valuable
for the industrial setting, since project-specific bugs align with domain
knowledge, share the same codebase, and adhere to the coding style employed in
industrial projects.Comment: Accepted at the industry challenge track of ASE 202
Learning the Relation between Code Features and Code Transforms with Structured Prediction
We present in this paper the first approach for structurally predicting code
transforms at the level of AST nodes using conditional random fields. Our
approach first learns offline a probabilistic model that captures how certain
code transforms are applied to certain AST nodes, and then uses the learned
model to predict transforms for new, unseen code snippets. We implement our
approach in the context of repair transform prediction for Java programs. Our
implementation contains a set of carefully designed code features, deals with
the training data imbalance issue, and comprises transform constraints that are
specific to code. We conduct a large-scale experimental evaluation based on a
dataset of 4,590,679 bug fixing commits from real-world Java projects. The
experimental results show that our approach predicts the code transforms with a
success rate varying from 37.1% to 61.1% depending on the transforms
Experiments on bright field and dark field high energy electron imaging with thick target material
Using a high energy electron beam for the imaging of high density matter with
both high spatial-temporal and areal density resolution under extreme states of
temperature and pressure is one of the critical challenges in high energy
density physics . When a charged particle beam passes through an opaque target,
the beam will be scattered with a distribution that depends on the thickness of
the material. By collecting the scattered beam either near or off axis,
so-called bright field or dark field images can be obtained. Here we report on
an electron radiography experiment using 45 MeV electrons from an S-band
photo-injector, where scattered electrons, after interacting with a sample, are
collected and imaged by a quadrupole imaging system. We achieved a few
micrometers (about 4 micrometers) spatial resolution and about 10 micrometers
thickness resolution for a silicon target of 300-600 micron thickness. With
addition of dark field images that are captured by selecting electrons with
large scattering angle, we show that more useful information in determining
external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure
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