339 research outputs found
Advanced Robot control Algorithms based on model-based nonlinear velocity-observers and adaptive friction compensation
Ph.DDOCTOR OF PHILOSOPH
Three dimensional photonic Dirac points in metamaterials
Topological semimetals, representing a new topological phase that lacks a
full bandgap in bulk states and exhibiting nontrivial topological orders,
recently have been extended to photonic systems, predominantly in photonic
crystals and to a lesser extent, metamaterials. Photonic crystal realizations
of Dirac degeneracies are protected by various space symmetries, where Bloch
modes span the spin and orbital subspaces. Here, we theoretically show that
Dirac points can also be realized in effective media through the intrinsic
degrees of freedom in electromagnetism under electromagnetic duality. A pair of
spin polarized Fermi arc like surface states is observed at the interface
between air and the Dirac metamaterials. These surface states show linear
k-space dispersion relation, resulting in nearly diffraction-less propagation.
Furthermore, eigen reflection fields show the decomposition from a Dirac point
to two Weyl points. We also find the topological correlation between a Dirac
point and vortex/vector beams in classic photonics. The theoretical proposal of
photonic Dirac point lays foundation for unveiling the connection between
intrinsic physics and global topology in electromagnetism.Comment: 15 pages, 5 figure
On the Way to SBOMs: Investigating Design Issues and Solutions in Practice
Software Bill of Materials (SBOM), offers improved transparency and supply
chain security by providing a machine-readable inventory of software components
used. With the rise in software supply chain attacks, the SBOM has attracted
attention from both academia and industry. This paper presents a study on the
practice of SBOM, based on the analysis of 4,786 GitHub discussions from 510
SBOM-related projects. Our study identifies key topics, challenges, and
solutions associated with effective SBOM usage. We also highlight commonly used
tools and frameworks for generating SBOMs, along with their respective
strengths and limitations. Our research underscores the importance of SBOMs in
software development and the need for their widespread adoption to enhance
supply chain security. Additionally, the insights gained from our study can
inform future research and development in this field
Polymorphisms of the IGF1R gene and their genetic effects on chicken early growth and carcass traits
<p>Abstract</p> <p>Background</p> <p>The insulin-like growth factor I receptor (IGF1R) has an important effect on growth, carcass, and meat quality traits in many species. However, few studies on associations of the <it>IGF1R </it>gene with growth and carcass traits have been reported in chickens. The objectives of the present study were to study the associations of the <it>IGF1R </it>gene with chicken early growth and carcass traits using a neutral test, variation scan of the gene, genetic diversity, linkage disequilibrium and association analyses.</p> <p>Results</p> <p>The tree generated from the amino acid sequences of 15 species showed that the <it>IGF1R </it>gene was conservative in the whole evolution among the mammalian animals and chickens. In a total of 10,818 bp of sequence, 70 single nucleotide polymorphisms were identified in the chicken <it>IGF1R </it>gene. The allelic and genotypic frequency distribution, genetic diversity and linkage disequilibrium of 18 single nucleotide polymorphisms (SNPs) in the Xinghua and White Recessive Rock chickens showed that six of them were possibly associated with growth traits. Association analyses showed that the A17299834G SNP was significantly associated with chicken carcass body weight, eviscerated weight with giblets, eviscerated weight, body weights at 28, 35, and 56 d of age, leg length at 56 d of age, and daily weight gain at 0–4 weeks. The haplotypes of the A17307750G and A17307494G were associated with early growth traits. The haplotypes of the A17299834G and C17293932T were significantly associated with most of the early growth traits and carcass traits.</p> <p>Conclusion</p> <p>There were rich polymorphisms in the chicken <it>IGF1R </it>gene. Several SNPs associated with chicken early growth traits and carcass traits were identified in the <it>IGF1R </it>gene by genetic diversity, linkage disequilibrium, and association analyses in the present study.</p
Trust in Software Supply Chains: Blockchain-Enabled SBOM and the AIBOM Future
Software Bill of Materials (SBOM) serves as a critical pillar in ensuring
software supply chain security by providing a detailed inventory of the
components and dependencies integral to software development. However,
challenges abound in the sharing of SBOMs, including potential data tampering,
hesitation among software vendors to disclose comprehensive information, and
bespoke requirements from software procurers or users. These obstacles have
stifled widespread adoption and utilization of SBOMs, underscoring the need for
a more secure and flexible mechanism for SBOM sharing. This study proposes a
novel solution to these challenges by introducing a blockchain-empowered
approach for SBOM sharing, leveraging verifiable credentials to allow for
selective disclosure. This strategy not only heightens security but also offers
flexibility. Furthermore, this paper broadens the remit of SBOM to encompass AI
systems, thereby coining the term AI Bill of Materials (AIBOM). This extension
is motivated by the rapid progression in AI technology and the escalating
necessity to track the lineage and composition of AI software and systems.
Particularly in the era of foundational models like large language models
(LLMs), understanding their composition and dependencies becomes crucial. These
models often serve as a base for further development, creating complex
dependencies and paving the way for innovative AI applications. The evaluation
of our solution indicates the feasibility and flexibility of the proposed SBOM
sharing mechanism, positing a new solution for securing (AI) software supply
chains
Multiobjective Image Color Quantization Algorithm Based on Self-Adaptive Hybrid Differential Evolution
In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm
Iron and nickel doped CoSe2 as efficient non precious metal catalysts for oxygen reduction
Iron and nickel doped CoSe2 were prepared by solvothermal method, and they were proved to be ternary chalcogenides by series of physical characterization. The effects of the iron and nickel contents on the oxygen reduction reaction were investigated by electrochemical measurements, and the highest activities were obtained on Co0.7Fe0.3Se2 and Co0.7Ni0.3Se2, respectively. Both Co0.7Fe0.3Se2 and Co0.7Ni0.3Se2 presented four-electron pathway. Furthermore, Co0.7Fe0.3Se2 exhibited more positive cathodic peak potential (0.564 V) and onset potential (0.759 V) than these of Co0.7Ni0.3Se2 (0.558 V and 0.741 V). And Co0.7Fe0.3Se2 displayed even superior stability and better tolerance to methanol, ethanol and ethylene glycol crossover effects than the commercial Pt/C (20 wt% Pt)
GBG++: A Fast and Stable Granular Ball Generation Method for Classification
Granular ball computing (GBC), as an efficient, robust, and scalable learning
method, has become a popular research topic of granular computing. GBC includes
two stages: granular ball generation (GBG) and multi-granularity learning based
on the granular ball (GB). However, the stability and efficiency of existing
GBG methods need to be further improved due to their strong dependence on
-means or -division. In addition, GB-based classifiers only unilaterally
consider the GB's geometric characteristics to construct classification rules,
but the GB's quality is ignored. Therefore, in this paper, based on the
attention mechanism, a fast and stable GBG (GBG++) method is proposed first.
Specifically, the proposed GBG++ method only needs to calculate the distances
from the data-driven center to the undivided samples when splitting each GB
instead of randomly selecting the center and calculating the distances between
it and all samples. Moreover, an outlier detection method is introduced to
identify local outliers. Consequently, the GBG++ method can significantly
improve effectiveness, robustness, and efficiency while being absolutely
stable. Second, considering the influence of the sample size within the GB on
the GB's quality, based on the GBG++ method, an improved GB-based -nearest
neighbors algorithm (GBNN++) is presented, which can reduce
misclassification at the class boundary. Finally, the experimental results
indicate that the proposed method outperforms several existing GB-based
classifiers and classical machine learning classifiers on public benchmark
datasets
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