Due to its wide applications, remote sensing (RS) image scene classification has attracted increasing research interest. When each category has a sufficient number of labeled samples, RS image scene ...classification can be well addressed by deep learning. However, in the RS big data era, it is extremely difficult or even impossible to annotate RS scene samples for all the categories in one time as the RS scene classification often needs to be extended along with the emergence of new applications that inevitably involve a new class of RS images. Hence, the RS big data era fairly requires a zero-shot RS scene classification (ZSRSSC) paradigm in which the classification model learned from training RS scene categories obeys the inference ability to recognize the RS image scenes from unseen categories, in common with the humans' evolutionary perception ability. Unfortunately, zero-shot classification is largely unexploited in the RS field. This article proposes a novel ZSRSSC method based on locality-preservation deep cross-modal embedding networks (LPDCMENs). The proposed LPDCMENs, which can fully assimilate the pairwise intramodal and intermodal supervision in an end-to-end manner, aim to alleviate the problem of class structure inconsistency between two hybrid spaces (i.e., the visual image space and the semantic space). To pursue a stable and generalization ability, which is highly desired for ZSRSSC, a set of explainable constraints is specially designed to optimize LPDCMENs. To fully verify the effectiveness of the proposed LPDCMENs, we collect a new large-scale RS scene data set, including the instance-level visual images and class-level semantic representations (RSSDIVCS), where the general and domain knowledge is exploited to construct the class-level semantic representations. Extensive experiments show that the proposed ZSRSSC method based on LPDCMENs can obviously outperform the state-of-the-art methods, and the domain knowledge further improves the performance of ZSRSSC compared with the general knowledge. The collected RSSDIVCS will be made publicly available along with this article.
Using the spillover index approach and its variants, we examine both static and dynamic volatility connectedness among eight typical cryptocurrencies. The results reveal that their connectedness ...fluctuates cyclically and has shown an obvious rise trend since the end of 2016. In the variance decomposition framework, we further construct a volatility connectedness network linking 52 cryptocurrencies using the LASSO-VAR for estimating high-dimensional VARs. We find that these 52 cryptocurrencies are tightly interconnected and “mega-cap” cryptocurrencies are more likely to propagate volatility shocks to others. However, some unnoticeable cryptocurrencies (e.g., Maidsafe Coin) are also significant net-transmitters of volatility connectedness and even have larger contribution of volatility spillovers to others.
•Total connectedness of 8 cryptocurrencies fluctuates periodically and increases from end-2016 onwards.•Volatility connectedness network of 52 cryptocurrencies is built and analyzed.•The level of incoming or outgoing connectedness of a cryptocurrency is partly linked to its market cap.•Bitcoin is proved to be an important net-emitter of connectedness but not the dominant one.•Some unnoticeable cryptocurrencies (e.g., Maidsafe Coin) also transmit strong volatility shocks to others.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
Background: A growing number of cohort studies revealed an inverse association between cheese intake and cardiovascular diseases, yet the causal relationship is unclear. Objective: To assess the ...causal relationship between cheese intake, and cardiovascular diseases and cardiovascular biomarkers. Methods: A two-sample Mendelian randomization (MR) analysis based on publicly available genome-wide association studies was employed to infer the causal relationship. The effect estimates were calculated using the random-effects inverse-variance-weighted method. Results: Cheese intake per standard deviation increase causally reduced the risks of type 2 diabetes (odds ratio (OR) = 0.46; 95% confidence interval (CI), 0.34–0.63; p = 1.02 × 10−6), heart failure (OR = 0.62; 95% CI, 0.49–0.79; p = 0.0001), coronary heart disease (OR = 0.65; 95% CI, 0.53–0.79; p = 2.01 × 10−5), hypertension (OR = 0.67; 95% CI, 0.53–0.84; p = 0.001), and ischemic stroke (OR = 0.76; 95% CI, 0.63–0.91; p = 0.003). Suggestive evidence of an inverse association between cheese intake and peripheral artery disease was also observed. No associations were observed for atrial fibrillation, cardiac death, pulmonary embolism, or transient ischemic attack. The better prognosis associated with cheese intake may be explained by lower body mass index (BMI; effect estimate = −0.58; 95% CI, from −0.88 to −0.27; p = 0.0002), waist circumference (effect estimate = −0.49; 95% CI, from −0.76 to −0.23; p = 0.0003), triglycerides (effect estimate = −0.33; 95% CI, from −0.50 to −0.17; p = 4.91 × 10−5), and fasting glucose (effect estimate = −0.20; 95% CI, from −0.33 to −0.07; p = 0.0003). There was suggestive evidence of a positive association between cheese intake and high-density lipoprotein. No influences were observed for blood pressure or inflammation biomarkers. Conclusions: This two-sample MR analysis found causally inverse associations between cheese intake and type 2 diabetes, heart failure, coronary heart disease, hypertension, and ischemic stroke.
Energy shortages and greenhouse effects are two unavoidable problems that need to be solved. Photocatalytically converting CO2 into a series of valuable chemicals is considered to be an effective ...means of solving the above dilemmas. Among these photocatalysts, the utilization of black phosphorus for CO2 photocatalytic reduction deserves a lightspot not only for its excellent catalytic activity through different reaction routes, but also on account of the great preponderance of this relatively cheap catalyst. Herein, this review offers a summary of the recent advances in synthesis, structure, properties, and application for CO2 photocatalytic reduction. In detail, the review starts from the basic principle of CO2 photocatalytic reduction. In the following section, the synthesis, structure, and properties, as well as CO2 photocatalytic reduction process of black phosphorus‐based photocatalyst are discussed. In addition, some possible influencing factors and reaction mechanism are also summarized. Finally, a summary and the possible future perspectives of black phosphorus‐based photocatalyst for CO2 reduction are established.
Black phosphorus has been a hotspot in the field of photocatalytic CO2 reduction due to its distinctive potential advantages. This review summarizes recent synthesis methods and photocatalytic CO2 reduction applications of black phosphorus, the possible appearing challenges, and some future perspectives of these materials are also discussed. The aim is to provide certain reference to design black phosphorus‐based catalysts.
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Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer ...intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
The oxygen reduction reaction (ORR) is one of the most important reactions in life processes and energy conversion systems. To alleviate global warming and the energy crisis, the development of ...high‐performance electrocatalysts for the ORR for application in energy conversion and storage devices such as metal–air batteries and fuel cells is highly desirable. Inspired by the biological oxygen activation/reduction process associated with heme‐ and multicopper‐containing metalloenzymes, iron and copper‐based transition‐metal complexes have been extensively explored as ORR electrocatalysts. Herein, an outline into recent progress on non‐precious‐metal electrocatalysts for the ORR is provided; these electrocatalysts do not require pyrolysis treatment, which is regarded as desirable from the viewpoint of bioinspired molecular catalyst design, focusing on iron/cobalt macrocycles (porphyrins, phthalocyanines, and corroles) and copper complexes in which the ORR activity is tuned by ligand variation/substitution, the method of catalyst immobilization, and the underlying supporting materials. Current challenges and exciting imminent developments in bioinspired ORR electrocatalysts are summarized and proposed.
An outline of recent progress on non‐precious‐metal electrocatalysts for the oxygen reduction reaction (ORR) is presented from the viewpoint of bioinspired molecular catalyst design, with a focus on iron/cobalt macrocycles and copper complexes in which the ORR activity is tuned by ligand modification, the method of catalyst immobilization, and the underlying supporting materials (see scheme).
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We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and ...evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.
In recent years, financial institutions (FIs) have tentatively utilized supply chain finance (SCF) as a means of solving the financing issues of small and medium-sized enterprises (SMEs). Thus, ...forecasting SMEs' credit risk in SCF has become one of the most critical issues in financing decision-making. Nevertheless, traditional credit risk forecasting models cannot meet the needs of such forecasting. Many researchers argue that machine learning (ML) approaches are good tools. Here we propose an enhanced hybrid ensemble ML approach called RS-MultiBoosting by incorporating two classic ensemble ML approaches, random subspace (RS) and MultiBoosting, to improve the accuracy of forecasting SMEs' credit risk. The experimental samples, originating from data on forty-six quoted SMEs and seven quoted core enterprises (CEs) in the Chinese securities market between 31 March 2014 and 31 December 2015, are collected to test the feasibility and effectiveness of the RS-MultiBoosting approach. The forecasting result shows that RS-MultiBoosting has good performance in dealing with a small sample size. From the SCF perspective, the results suggest that to enhance SMEs' financing ability, ‘traditional’ factors, such as the current and quick ratio of SMEs, remain critical. Other SCF-specific factors, for instance, the features of trade goods and the CE's profit margin, play a significant role.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
•A new graph-based semi-supervised learning method is proposed.•The graph structure and the class imbalance are taken into account in our method.•There are interesting connections between our method ...and different areas.•Experimental results demonstrate our method can achieve promising performance on several benchmark datasets.
Graph-based Semi-Supervised Learning (GSSL) methods aim to classify unlabeled data by learning the graph structure and labeled data jointly. In this work, we propose a simple GSSL approach, which can deal with various degrees of class imbalance in given datasets. The key idea is to estimate the class proportion of input data in order to enhance the discriminative power of learned smooth classification function on the graph. Moreover, it has interesting connections to the regularization framework, the Markov stability for graph partition and the group inverse of normalized Laplacain matrix. For classification problems, experimental results demonstrate our approach can achieve promising performance on several datasets with varying class imbalance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Two organo‐soluble polyimide (PI) resins were prepared from a tricyclic fluoro‐containing dianhydride 9,9‐bis(trifluoromethyl)xanthene‐2,3,6,7‐tetracarboxylic dianhydride (6FCDA) and aromatic diamine ...of 2,2‐bis4‐(4‐aminophenoxy)phenylhexafluoropropane (BDAF) for PI‐IIa and 1,3‐bis(3‐aminophenoxy)benzene (133APB) for PI‐IIb, respectively. For comparison, two analogous PIs were prepared from 4,4′‐(hexafluoroisopropylidene)diphthalic anhydride (6FDA) and the same diamines to afford PI‐Ia (6FDA‐BDAF) and PI‐Ib (6FDA‐133APB), respectively. Incorporation of the rigid and planar xanthene units in the 6FCDA‐PIs obviously decreased the solubility of the resins in organic solvents. Flexible and tough PI films were fabricated from the PI solutions in N,N‐dimethylacetamide (DMAc). The PI films derived from the rigid and planar 6FCDA dianhydride exhibited obviously enhance thermal stability, and high‐temperature dimensional stability. For example, PI‐IIa (6FCDA‐BDAF) showed the glass transition temperature (Tg) and coefficient of linear thermal expansion (CTE) values of 311.5°C and 50.5 × 10−6/K, respectively, which were all superior to those of the PI‐Ia counterpart (Tg = 264.6°C; CTE = 55.4 × 10−6/K). In addition, the 6FCDA‐PI films maintained the intrinsically excellent optical transparency of the fluoro‐containing PI films and showed the comparable optical properties to those of the 6FDA‐PI films. As for the properties highly related to the space applications, all the PI films exhibited low solar absorptivity (α) values of 0.18–0.20 and the good thermal emissivity (ε) values in the range of 0.68–0.70. PI‐Ia (6FDA‐BDAF) and PI‐IIa (6FCDA‐BDAF) films showed the similar atomic oxygen erosion behaviors with the standard Kapton® type of PI film derived from pyromellitic dianhydride (PMDA) and 4,4′‐oxydianiline (ODA).
Fluoro‐containing polyimide film with improved thermal stability and low solar absorptivity
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