5,519 research outputs found
Modeling Temporal Pattern and Event Detection using Hidden Markov Model with Application to a Sludge Bulking Data
This paper discusses a method of modeling temporal pattern and event detection based on Hidden Markov Model (HMM) for a continuous time series data. We also provide methods for checking model adequacy and predicting future events. These methods are applied to a real example of sludge bulking data for detecting sludge bulking for a water plant in Chicago
Revisiting energy efficiency and energy related CO2 emissions: Evidence from RCEP economies
Since the last four decades, energy demand has been reached to
the utmost level, which also leads to emissions and causes environmental
degradation, global warming and climate change all
over the world. In this sense, policy makers have suggested various
measures including renewable adoption and energy efficiency.
Current study aims to investigate the influence of
economic growth, energy consumption, renewable electricity output,
and energy efficiency on the energy related emissions. A
panel of 12 RCEP economies are examined covering the period
1990-2020. Since the data follows irregular path, therefore a novel
method of moment panel quantile regression is employed along
with the Granger causality test. The empirical results indicate that
economic growth and energy consumption significantly enhances
energy related emissions, where the magnitude and significance
level is found strengthening from lower to upper quantiles (Q0.25,
Q0.50, Q0.75 and Q0.90). Conversely, renewable electricity and energy
efficiency are the significant tools for lowering energy related
emissions in the region. Additionally, a unidirectional causality is
found from energy consumption and renewable electricity output
to energy related emissions. However, a feedback effect is validated
between economic growth, energy efficiency, and energy
related emissions. Based on the empirical findings, this study suggests
enhancement of renewable electricity output and adoption
of energy efficient technologies to reduce environmental degradation
and emission level
Neyman-pearson classiffication under high-dimensional settings
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error and a constrained type I error under a user specified level. This article is the first attempt to construct classifiers with guaranteed theoretical performance under the NP paradigm in high-dimensional settings. Based on the fundamental Neyman-Pearson Lemma, we used a plug-in approach to construct NP-Type classifiers for Naive Bayes models. The proposed classifiers satisfy the NP oracle inequalities, which are natural NP paradigm counterparts of the oracle inequalities in classical binary classification. Besides their desirable theoretical properties, we also demonstrated their numerical advantages in prioritized error control via both simulation and real data studies
Topological Dirac states beyond orbitals for silicene on SiC(0001) surface
The discovery of intriguing properties related to the Dirac states in
graphene has spurred huge interest in exploring its two-dimensional group-IV
counterparts, such as silicene, germanene, and stanene. However, these
materials have to be obtained via synthesizing on substrates with strong
interfacial interactions, which usually destroy their intrinsic
()-orbital Dirac states. Here we report a theoretical study on the
existence of Dirac states arising from the orbitals instead of
orbitals in silicene on 4H-SiC(0001), which survive in spite of the strong
interfacial interactions. We also show that the exchange field together with
the spin-orbital coupling give rise to a detectable band gap of 1.3 meV. Berry
curvature calculations demonstrate the nontrivial topological nature of such
Dirac states with a Chern number , presenting the potential of realizing
quantum anomalous Hall effect for silicene on SiC(0001). Finally, we construct
a minimal effective model to capture the low-energy physics of this system.
This finding is expected to be also applicable to germanene and stanene, and
imply great application potentials in nanoelectronics.Comment: 6 Figures , Accepted by Nano Letter
Tris(5,6-dimethyl-1H-benzimidazole-κN 3)(pyridine-2,6-dicarboxylato-κ3 O 2,N,O 6)nickel(II)
The title mononuclear complex, [Ni(C7H3NO4)(C9H10N2)3], shows a central NiII atom which is coordinated by two carboxylate O atoms and the N atom from a pyridine-2,6-dicarboxylate ligand and by three N atoms from different 5,6-dimethyl-1H-benzimidazole ligands in a distorted octahedral geometry. The crystal structure shows intermolecular N—H⋯O hydrogen bonds
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