Two‐dimensional (2D) nanostructures are highly attractive for fabricating nanodevices due to their high surface‐to‐volume ratio and good compatibility with device design. In recent years 2D ...nanostructures of various materials including metal oxides, graphene, metal dichalcogenides, phosphorene, BN and MXenes, have demonstrated significant potential for gas sensors. This review aims to provide the most recent advancements in utilization of various 2D nanomaterials for gas sensing. The common methods for the preparation of 2D nanostructures are briefly summarized first. The focus is then placed on the sensing performances provided by devices integrating 2D nanostructures. Strategies for optimizing the sensing features are also discussed. By combining both the experimental results and the theoretical studies available, structure‐properties correlations are discussed. The conclusion gives some perspectives on the open challenges and future prospects for engineering advanced 2D nanostructures for high‐performance gas sensors devices.
Two‐dimensional (2D) nanostructures are highly attractive for nanodevices due to their high surface‐to‐volume ratio and good compatibility with device design. The most recent advancements in the synthesis, surface engineering, and funcationlization of various 2D nanostructures, including metal oxides, graphene, metal dichalcogenides, phosphorene, BN, and Mxenes for high‐performance gas sensors, are reviewed.
•Modeling the topic model.•Modeling the geographical correlations.•Modeling the social correlations.•Modeling the categorical correlations.•We integrate the textual, geographical, social, categorical ...and popularity information into probabilistic matrix factorization model for POI recommendation.
The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through Point-of-Interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper, we propose a context-aware probabilistic matrix factorization method for POI recommendation. Our model is called TGSC-PMF, it exploits textual information, geographical information, social information, categorical information and popularity information, and incorporates these factors effectively. First, we exploit an aggregated Latent Dirichlet Allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and then generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, we model the categorical correlations which combine the category bias of users and the popularity of POIs into categorical relevance score. Further, we integrate the interest, geographical, social and categorical relevance scores into probabilistic matrix factorization model (PMF) for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGSC-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
Flow‐dependent errors in tropical analyses and short‐range forecasts are analysed using global observing‐system simulation experiments assimilating only temperature, only winds, and both data types ...using the ensemble Kalman filter (EnKF) Data Assimilation Research Testbed (DART) and a perfect model framework. The idealised, homogeneous observation network provides profiles of wind and temperature data from the nature run for January 2018 using the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) forced by the observed sea‐surface temperature. The results show that the assimilation of abundant wind observations in a perfect model makes the temperature data in the Tropics largely uninformative. Furthermore, the assimilation of wind data reduces the background errors in specific humidity twice as much as the assimilation of temperature observations. In all experiments, the largest analysis uncertainties and the largest short‐term forecast errors are found in regions of strong vertical and longitudinal gradients in the background wind, especially in the upper troposphere and lower stratosphere over the Indian Ocean and Maritime Continent. The horizontal error correlation scales are on average short throughout the troposphere, just several hundred km. The correlation scales of the wind variables in precipitating regions are half of those in nonprecipitating regions. In precipitating regions, the correlations are elongated vertically, especially for the wind variables. Strong positive cross‐correlations between temperature and specific humidity in the precipitating regions are explained using the Clausius–Clapeyron equation.
Tropical upper troposphere is a region with the largest uncertainties in the temperature and winds in analyses and short‐range forecasts, even in a perfect model. We show that the assimilation of wind observations using the ensemble Kalman filter (experiment “UV$$ UV $$”) leads to smaller uncertainties in both wind and temperature fields than the assimilation of temperature data (experiments “T$$ T $$” and “TUV$$ TUV $$”). Wind observations are also more effective than temperature data for the analysis of the specific moisture.
We explore the different local symmetries in colloidal glasses beyond the standard pair correlation analysis. Using our newly developed X-ray cross correlation analysis (XCCA) concept together with ...brilliant coherent X-ray sources, we have been able to access and classify the otherwise hidden local order within disorder. The emerging local symmetries are coupled to distinct momentum transfer (Q) values, which do not coincide with the maxima of the amorphous structure factor. Four-, 6-, 10- and, most prevalently, 5-fold symmetries are observed. The observation of dynamical evolution of these symmetries forms a connection to dynamical heterogeneities in glasses, which is far beyond conventional diffraction analysis. The XCCA concept opens up a fascinating view into the world of disorder and will definitely allow, with the advent of free electron X-ray lasers, an accurate and systematic experimental characterization of the structure of the liquid and glass states.
Individual differences researchers very commonly report Pearson correlations between their variables of interest. Cohen (1988) provided guidelines for the purposes of interpreting the magnitude of a ...correlation, as well as estimating power. Specifically, r=0.10, r=0.30, and r=0.50 were recommended to be considered small, medium, and large in magnitude, respectively. However, Cohen's effect size guidelines were based principally upon an essentially qualitative impression, rather than a systematic, quantitative analysis of data. Consequently, the purpose of this investigation was to develop a large sample of previously published meta-analytically derived correlations which would allow for an evaluation of Cohen's guidelines from an empirical perspective. Based on 708 meta-analytically derived correlations, the 25th, 50th, and 75th percentiles corresponded to correlations of 0.11, 0.19, and 0.29, respectively. Based on the results, it is suggested that Cohen's correlation guidelines are too exigent, as <3% of correlations in the literature were found to be as large as r=0.50. Consequently, in the absence of any other information, individual differences researchers are recommended to consider correlations of 0.10, 0.20, and 0.30 as relatively small, typical, and relatively large, in the context of a power analysis, as well as the interpretation of statistical results from a normative perspective.
•Cohen's correlation guidelines (0.10, 0.30, 0.50) were evaluated empirically.•A pool of 708 meta-analytically derived correlations were collated.•The 25th, 50th, and 75th percentiles corresponded to r=0.10, r=0.20, r=0.30.•Researchers are recommended to consider these normative guidelines.
Measurements are indispensable tools for extracting information about quantum systems. For a bipartite system, local measurements provide a way to probe its correlations. In this work, we study ...correlations of a bipartite state from the perspective of local measurements. We introduce measurement-extracted total correlations as the average reduction of quantum mutual information caused by this measurement, and show that it can be decomposed into classical part (called measurement-extracted classical correlations, quantified by the average reduction of the von Neumann entropy of the unmeasured subsystem) and quantum part (called measurement-extracted quantum correlations, quantified by the coherence difference between the bipartite state and the reduced state of the measured subsystem). We investigate their basic properties and reveal the connections between the measurement-extracted correlations and the conventional measurement-independent correlations by rank-1 positive-operator-valued measures. Finally, we evaluate these correlations quantifiers for two-qubit Werner states relative to several prototypical measurements.
•Measurement-extracted total, classical and quantum correlations are introduced.•Basic properties of measurement-extracted total, classical and quantum correlations are revealed.•The connections between measurement-extracted correlations and measurement-independent correlations are discussed.
Monotone Measures for Non-Local Correlations Beigi, Salman; Gohari, Amin
IEEE transactions on information theory,
2015-Sept., 2015-9-00, 20150901, Letnik:
61, Številka:
9
Journal Article
Recenzirano
Odprti dostop
Non-locality is the phenomenon of observing strong correlations among the outcomes of local measurements of a multipartite physical system. No-signaling boxes are the abstract objects for studying ...non-locality, and wirings are local operations on the space of no-signaling boxes. This means that, no matter how non-local the nature is, the set of physical non-local correlations must be closed under wirings. Then, one approach to identify the non-locality of nature is to characterize the closed sets of non-local correlations. Although non-trivial examples of wirings of no-signaling boxes are known, there is no systematic way to study wirings. In particular, given a set of no-signaling boxes, we do not know a general method to prove that it is closed under wirings. In this paper, we propose the first general method to construct such closed sets of non-local correlations. We show that a well-known measure of correlation, called maximal correlation, when appropriately defined for non-local correlations, is monotonically decreasing under wirings. This establishes a conjecture about the impossibility of simulating isotropic boxes from each other, implying the existence of a continuum of closed sets of non-local boxes under wirings. To prove our main result, we introduce some mathematical tools that may be of independent interest: we define a notion of maximal correlation ribbon as a generalization of maximal correlation, and provide a connection between it and a known object called hypercontractivity ribbon; we show that these two ribbons are monotone under wirings too.
In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the ...DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured by the fractional differencing parameter d). For a comparison, we also report the results for the standard Pearson correlation coefficient. The DCCA coefficient dominates the Pearson coefficient for non-stationary series.
•DCCA coefficient for non-stationary series is analyzed.•Different settings (non-stationarity level, scales, correlations, time series length) are examined.•DCCA coefficient strongly dominates the standard Pearson coefficient for non-stationary series.
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