We present an efficient, highly selective and binder free non-enzymatic glucose sensor based on polyaniline@copper-nickel (PANI@CuNi) nanocomposite. PANI@CuNi nanocomposites with different loading ...ratio of nanoparticles (1: 025, 1: 0.33, 1: 05 and 1: 1) were prepared by mixing solution of PANI, synthesized through inverse emulsion polymerization method, and CuNi nanoparticles, synthesized through polyol process. The as prepared PANI@CuNi nanocomposites were coated on glassy carbon substrate without binder for non-enzymatic glucose sensing. A considerable increase in the active surface area of the electrode occurred after coating of this material. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and chronoamperometry demonstrated that PANI@CuNi nanocomposite with 1: 0.5 ratio could be a good choice to be used as electrode material for non-enzymatic glucose sensing. The PANI@CuNi modified electrode exhibited high sensitivity (1030 μA mM−1 cm−2), good lower detection limit (0.2 μM) and a linear range of 5.6 mM (R2 = 0.992) with additional advantage of excellent selectivity, high stability and effective detection in real blood samples.
•PANI@CuNi nanocomposite is highly efficient and selective towards glucose oxidation.•The material is electrochemically very active and stable and can offer a good choice as electrode material for binder free non-enzymatic glucose sensor.•The response of the PANI@CuNi nanocomposite coated electrode towards glucose is very rapid.•The PANI@CuNi modified electrode also possesses good lower detection limit (0.2 μM) and a linear range of 5.6 mM (R2 = 0.992) and effective detection in real blood samples.
Stock market forecasting has attracted significant attention mainly due to the potential monetary benefits. Predicting these markets is a challenging task due to numerous interrelated factors, and ...needs a complete and efficient feature selection process to identify the most informative factors. As a time series problem, stock price movements are also dependent on movements on its previous trading days. Feature selection techniques have been widely applied in stock forecasting, but existing approaches usually use a single feature selection technique, which may overlook some important assumptions about the underlying regression function linking the input and output variables. In this study, we combine features selected by multiple feature selection techniques to generate an optimal feature subset and then use a deep generative model to predict future price movements. First, we compute an extended set of forty-four technical indicators from daily stock data of eighty-eight stocks and then compute their importance by independently training logistic regression model, support vector machine and random forests. Based on a prespecified threshold, the lowest ranked features are dropped and the rest are grouped into clusters. The variable importance measure is reused to select the most important feature from each cluster to generate the final subset. The input is then fed to a deep generative model comprising of a market signal extractor and an attention mechanism. The market signal extractor recurrently decodes market movement from the latent variables to deal with stochastic nature of the stock data and the attention mechanism discriminates between predictive dependencies of different temporal auxiliary outputs. The results demonstrate that combining features selected by multiple feature selection approaches and using them as input into a deep generative model outperforms state-of-the-art approaches.
•Forty-four financial indicators are computed from historical data of 88 stocks.•A correlation-based multi-filter feature selection selects a more optimal feature set.•A deep generative model is used for predicting future stock price trends.•The results demonstrate that feature selection positively effect performance of prediction model.
Knowledge graph (KG) embedding models map nodes and edges to fixed-length vectors and obtain the similarity of nodes as the output of a scoring function to predict missing links between nodes. KG ...embedding methods based on graph convolutional networks (GCNs) have recently gained significant attention due to their ability to add information of neighboring nodes into the nodes’ embeddings. However, existing GCNs are primarily based on real-valued embeddings, which have high distortion, particularly when modeling graphs with varying geometric structures. In this paper, we propose complex graph convolutional network (ComplexGCN), a novel extension of the standard GCNs in complex space to combine the expressiveness of complex geometry with GCNs for improving the representation quality of KG components. The proposed ComplexGCN comprises a set of complex graph convolutional layers and a complex scoring function based on PARATUCK2 decomposition: the former includes information of neighboring nodes into the nodes’ embeddings, while the latter leverages these embeddings to predict new links between nodes. The proposed model demonstrates enhanced performance compared to existing methods on the two recent standard link prediction datasets.
•Real embeddings usually suffer from high distortion.•An extension of graph convolution network is introduced with complex embeddings.•A novel complex scoring function based on PARATUCK2 decomposition is introduced.•The results show that complex embeddings lead to performance improvement.
Density functional theory studies (DFT) have been carried out to evaluate the ability of polyaniline emeraldine salt (PANI ES) from 2 to 8 phenyl rings as sensor for NH3, CO2, and CO. The sensitivity ...and selectivity of nPANI ES among NH3, CO2, and CO are studied at UB3LYP/6-31G(d) level of theory. Interaction of nPANI ES with CO is studied from both O (CO(1)) and C (CO(2)) sides of CO. Interaction energy, NBO, and Mulliken charge analysis were used to evaluate the sensing ability of PANI ES for different analytes. Interaction energies are calculated and corrected for BSSE. Large forces of attraction in nPANI ES-NH3 complexes are observed compared to nPANI ES–CO2, nPANI ES-CO(1), and nPANI ES-CO(2) complexes. The inertness of +CO– in nPANI ES-CO(1) and nPANI ES-CO(2) complexes are also discussed. Frontier molecular orbitals and energies indicate that NH3 changes the orbital energy of nPANI ES to a greater extent compared to CO2, CO(1), and CO(2). Peaks in UV–vis and UV–vis–near-IR spectra of nPANI ES are blue-shifted upon doping with NH3, CO2, CO(1), and CO(2) which illustrates dedoping of PANI ES to PANI emeraldine base (PANI EB). Finally, it is concluded that PANI ES has greater response selectivity toward NH3 compared to CO2 and CO and it is consistent with the experimental observations.
The smart grid initiative has encouraged utility companies worldwide to roll-out new and smarter versions of energy meters. Before an extensive roll-out, which is both labor-intensive and incurs high ...capital costs, consumers need to be incentivised to reap the long-term benefits of such smart meters. Off-the-shelf energy monitors (e-monitors) can provide consumers with an insight into such potential benefits. As e-monitors are owned by the consumer, the consumer has greater control over the data, which significantly reduces the privacy and data confidentiality concerns. Because only limited online technical information is available about e-monitors, we evaluate several existing e-monitors using an online technical survey directly from the vendors. Besides automated e-monitoring, the use of different off-the-shelf e-monitors can also help to demonstrate state-of-the-art techniques such as non-intrusive load monitoring (NILM), data analytics, and the predictive maintenance of appliances. Our survey indicates a trend towards the incorporation of such state-of-the-art capabilities, particularly the appliance-level e-monitoring and load disaggregation. We have also discussed some essential requirements to implement load disaggregation in the next generation e-monitors. In future, these intelligent e-monitoring techniques will encourage effective consumer participation in the demand-side management (DSM) programs.
In this work nickel modified polymer composites have been synthesized electrochemically for methanol electrooxidation on platinum and graphite electrodes. Ni (II) ions were incorporated on ...polyaniline and poly (o-aminophenol)(PANI/POAP) bilayer structure from 0.1M Nickel sulphate hexahydrate solution at open circuit potential (OCP).TheNi (II) deposited composites were characterized with Fourier Transform Infrared (FTIR) spectroscopy, Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS). Cyclic voltammetry characterization exhibits stable redox pair of Ni+3/Ni+2 for both electrodes. Fourier transform infrared spectroscopy was used for functional group analysis. Ni (II) peaks were observed in the region of 400–700cm−1 along with peaks at 1102cm−1 and 1400–1600cm−1 for polyaniline and phenoxazine units. The Ni-polymer composite on platinum and graphite electrode followed different phenomenon for methanol electroxidation in alkaline media. The cyclic voltammetry results showed significantly large methanol oxidation on platinum substrate with charge storage capacity of 196μ F/cm2 for 1.5M methanol in alkaline media as compared to 8.306μF/cm2 of graphite. The diffusion controlled linear response for increase rate of methanol concentration has been obtained for Ni (II)/PANI/POAP-Pt. The electrochemical impedance spectroscopy (EIS)results indicated increase in charge storage capacity from 10−4F/cm2 to 10−3F/cm2 with increasing potentials at lower frequency region.The phase shift was observed from 60–40degrees for increased potentials at low frequency range in EIS analysis. The increased conductive behavior from 10−5 to 10−2s/cm2 has been obtained fornickel modified polymer composite at higher frequency region in EIS analysis.
Knowledge graphs (KGs) have recently become increasingly popular due to the broad range of essential applications in various downstream tasks including intelligent search, personalized ...recommendations, intelligent financial data analytics, etc. During an automated construction of a KG, the knowledge facts from multiple knowledge sources are automatically extracted in the form of triples, and these observed triples are used to derive new unobserved triples for KG completion (also known as link prediction). State-of-the-art link prediction methods are known to be primarily KG embedding models, among which tensor factorization models have recently drawn much attention due to their scalability and expressive feature embeddings, and hence, perform well for link prediction. However, these embedding models consider each KG triple individually and fail to capture the useful information present in the neighborhood of a node. To this end, we propose a novel end-to-end KG embedding learning framework that consists of an encoder of a dual weighted graph convolutional network, and a decoder of a novel fully expressive tensor factorization model. The proposed encoder extends weighted graph convolutional network to generate two rich and high quality embedding vectors for each node by aggregating information from the neighboring nodes. The proposed decoder has a flexible and powerful tensor representation form of the Tensor Train decomposition that takes benefit of the two representations of each node in its embedding space to accurately model the KG triples. We also derive a bound on the size of the embeddings for full expressivity and show that our proposed tensor factorization model is fully expressive. Additionally, we show the relationship of our tensor factorization model to previous tensor factorization models. The experimental results show the effectiveness of the proposed framework that consistently marks performance gains over several previous models on recent standard link prediction datasets.
•Embedding models perform the link prediction task on the basis of individual triples.•Gathering neighborhood information leads to richer node embeddings.•A GCN based model is used to produce two embedding vectors of each node.•A novel tensor factorization model predicts missing entities to derive new triples.•The results prove that richer node embeddings lead to significant performance gain.
Summary
Double perovskite halides are potential materials for the production of renewable energy that could meet the global demands for resolving energy shortage issues. In this study, we ...systematically investigate the Rb2XGaBr6 (XNa, K) double perovskites using the full‐potential linearized augmented plane wave (FP‐LAPW+lo) method of density functional theory. The thermodynamic as well as the structural stabilities of the studied materials have been confirmed from the calculated formation energy and Goldsmith tolerance factor (0.89 and 0.92). On the other hand, the calculated Pugh's ratio shows the ductile mechanical nature of the studied materials. The calculated electronic bandgaps of 2.2 eV/1.90 eV for Rb2Na/KGaBr6 lies is in the visible region, which indicated the potential application of these materials in solar cells. The electronic properties of the two compounds are studied using the electronic density of states and the complex dielectric functions are used to evaluate optical properties. Our calculated results clearly indicate the optimum absorption of light in visible regions which depicts the potential of these materials for opto‐electronic devices. The thermoelectric properties of the two Rb2XGaBr6 (XNa, K) double perovskites are also studied in terms of thermal and electrical conductivity and the Seebeck coefficient.
Here we report, the thermodynamic and structural stabilities of Rb2NaGaBr6 and Rb2KGaBr6 determined by a tolerance factor, with negative formation energy and ductile behavior of Rb2Na/KGaBr6 by Pugh's (1.82/2.17) and Poisson ratio (0.27/0.30). The larger value of absorption‐coefficient for Rb2NaGaBr6 makes it a potential candidate for solar cell systems. A large value of thermal to electrical conductivity ratio (10−5), n‐type semiconducting nature, and large value of the figure of merit and exposed their potential for thermoelectric generators and refrigerators.
Knowledge graph (KG) embedding methods aim to learn low-dimensional representations of entities and relations to predict new valid triples for KG completion. Most of the existing KG embedding models ...learn embeddings in Euclidean space, which cannot accurately capture hierarchical structures and complex properties of relations found in KGs. To this effect, a recent model MuRP, as a first hyperbolic method, learns the KG embeddings in hyperbolic space and outperforms existing Euclidean embedding models. However, MuRP treats the KG triples individually, and hence fails to capture the complex structural information inherent in the local vicinity of a node, leading to low-quality node embeddings. On the other hand, the recent hyperbolic graph neural network (HGNN) provides a way of learning high-quality hyperbolic node embeddings by capturing information from each node’s neighborhood. However, HGNN ignores the relation features and treats all neighboring nodes with equal importance. To this end, we propose RHGNN, which extends HGNN by including the relation features and performing a hyperbolic attention-based neighborhood aggregation. We then combine RHGNN with MuRP into a novel encoder–decoder hyperbolic embedding learning framework (which we call HyperGEL) for KG completion. RHGNN gathers information from the neighboring nodes to generate rich hyperbolic node embeddings, and MuRP uses these embeddings to predict new triples. Experimental results show the proposed framework’s effectiveness that consistently marks performance gains over several previous models on recent standard KG completion datasets.
•Euclidean space cannot accurately preserve the hierarchies present in KGs.•Hyperbolic space requires few dimensions to embed the hierarchical structures.•An HGNN based encoder is used to enrich node embedding with neighborhood information.•A recent hyperbolic model is used to predict new valid triples for KG completion.•The results prove that richer hyperbolic embeddings lead to performance improvement.
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