North China Electric Power University

About: North China Electric Power University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Electric power system & Wind power. The organization has 31534 authors who have published 27765 publications receiving 305444 citations. The organization is also known as: Huáběi diànlì dàxué & NCEPU.

Observation of Electron-Antineutrino Disappearance at Daya Bay

Abstract: The Daya Bay Reactor Neutrino Experiment has measured a nonzero value for the neutrino mixing angle θ13 with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GW_(th) reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a 43 000 ton–GW_(th)–day live-time exposure in 55 days, 10 416 (80 376) electron-antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is R=0.940± 0.011(stat.)±0.004(syst.). A rate-only analysis finds sin^22θ_(13)=0.092±0.016(stat.)±0.005(syst.) in a three-neutrino framework.

Review on multi-criteria decision analysis aid in sustainable energy decision-making

Abstract: Multi-criteria decision analysis (MCDA) methods have become increasingly popular in decision-making for sustainable energy because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic and biophysical systems. This article reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation. The criteria of energy supply systems are summarized from technical, economic, environmental and social aspects. The weighting methods of criteria are classified into three categories: subjective weighting, objective weighting and combination weighting methods. Several methods based on weighted sum, priority setting, outranking, fuzzy set methodology and their combinations are employed for energy decision-making. It is observed that the investment cost locates the first place in all evaluation criteria and CO2 emission follows closely because of more focuses on environment protection, equal criteria weights are still the most popular weighting method, analytical hierarchy process is the most popular comprehensive MCDA method, and the aggregation methods are helpful to get the rational result in sustainable energy decision-making.

Metal–organic framework-based materials: superior adsorbents for the capture of toxic and radioactive metal ions

Abstract: Highly efficient removal of metal ion pollutants, such as toxic and nuclear waste-related metal ions, remains a serious task from the biological and environmental standpoint because of their harmful effects on human health and the environment. Recently, highly porous metal–organic frameworks (MOFs), with excellent chemical stability and abundant functional groups, have represented a new addition to the area of capturing various types of hazardous metal ion pollutants. This review focuses on recent progress in reported MOFs and MOF-based composites as superior adsorbents for the efficient removal of toxic and nuclear waste-related metal ions. Aspects related to the interaction mechanisms between metal ions and MOF-based materials are systematically summarized, including macroscopic batch experiments, microscopic spectroscopy analysis, and theoretical calculations. The adsorption properties of various MOF-based materials are assessed and compared with those of other widely used adsorbents. Finally, we propose our personal insights into future research opportunities and challenges in the hope of stimulating more researchers to engage in this new field of MOF-based materials for environmental pollution management.

Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

Abstract: We present a method that learns word embedding for Twitter sentiment classification in this paper. Most existing algorithms for learning continuous word representations typically only model the syntactic context of words but ignore the sentiment of text. This is problematic for sentiment analysis as they usually map words with similar syntactic context but opposite sentiment polarity, such as good and bad, to neighboring word vectors. We address this issue by learning sentimentspecific word embedding (SSWE), which encodes sentiment information in the continuous representation of words. Specifically, we develop three neural networks to effectively incorporate the supervision from sentiment polarity of text (e.g. sentences or tweets) in their loss functions. To obtain large scale training corpora, we learn the sentiment-specific word embedding from massive distant-supervised tweets collected by positive and negative emoticons. Experiments on applying SSWE to a benchmark Twitter sentiment classification dataset in SemEval 2013 show that (1) the SSWE feature performs comparably with hand-crafted features in the top-performed system; (2) the performance is further improved by concatenating SSWE with existing feature set.