This paper analyses the dynamic dependence between WTI crude oil and the exchange rates of the United States and China, taking structural changes of dependence into account by using six time-varying ...copula models. Upside and downside conditional values at risk (CoVaRs) are introduced specifically to measure the upward and downward risk dependences between oil prices and exchange rates. The findings indicate a structural break point of dependence exists between daily or weekly crude oil and the US dollar index. The dependence between crude oil and the RMB exchange rate is faintly positive with lower tail dependence, while the dependence between crude oil and the US dollar index is significantly negative with lower-upper and upper-lower tail dependence. Finally, the CoVaRs results show that there is significant risk spillover from crude oil to Chinese and the US exchange rate markets. Furthermore, the spillover effect is significantly asymmetry in Chinese exchange rate market in response to rising and falling oil returns, while the asymmetry of spillover effect for the US dollar index is not significant.
•Dynamic dependences between oil returns and exchange rates are analysed.•Risk spillover measured by CoVaR is estimated by six time-varying copula models.•A structural change of dependence for USD-WTI has been detected.•The dependence of CNY-WTI is weakly positive, while that of USD-WTI is negative.•There is significant risk spillover from WTI to exchange rates of China and the US.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Machine learning （ML） has been instrumental for the ad- vances of both data analysis and artificial intelligence （AI）. The recent success of deep learning brings it to a new height. ML algorithms ...have been successfully used in almost all ar- eas of applications in industry, science, and engineering.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
4.
Deep learning for sentiment analysis: A survey Zhang, Lei; Wang, Shuai; Liu, Bing
Wiley interdisciplinary reviews. Data mining and knowledge discovery,
July/August 2018, Volume:
8, Issue:
4
Journal Article
Peer reviewed
Open access
Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state‐of‐the‐art prediction results. Along with ...the success of deep learning in many application domains, deep learning is also used in sentiment analysis in recent years. This paper gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.
This article is categorized under:
Fundamental Concepts of Data and Knowledge > Data Concepts
Algorithmic Development > Text Mining
Sentiment analysis and opinion mining using deep learning.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
This paper studies clustering of multi-view data, known as multi-view clustering. Among existing multi-view clustering methods, one representative category of methods is the graph-based approach. ...Despite its elegant and simple formulation, the graph-based approach has not been studied in terms of (a) the generalization of the approach or (b) the impact of different graph metrics on the clustering results. This paper extends this important approach by first proposing a general Graph-Based System (GBS) for multi-view clustering, and then discussing and evaluating the impact of different graph metrics on the multi-view clustering performance within the proposed framework. GBS works by extracting data feature matrix of each view, constructing graph matrices of all views, and fusing the constructed graph matrices to generate a unified graph matrix, which gives the final clusters. A novel multi-view clustering method that works in the GBS framework is also proposed, which can (1) construct data graph matrices effectively, (2) weight each graph matrix automatically, and (3) produce clustering results directly. Experimental results on benchmark datasets show that the proposed method outperforms state-of-the-art baselines significantly.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In mmWave communication system we propose a simplified Genetic Algorithm (GA) in multiple-beam combination, which helps to overcome problems such as grating-lobe in common beam-forming algorithms, ...and it can get a better Signal Noise Ratio (SNR) performance at User Equipment (UE). The simplified GA can select optimal antenna cells from massive antenna array with a lower computational complexity and higher power-efficiency. Theoretical analysis and software simulation show that the method can support more UEs with a higher power efficiency, reduce the bit error rate and computational complexity.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Despite extensive literature on leadership and its impact employee innovative behavior, few studies have explored the relationship between inclusive leadership and employee innovative behavior. To ...address this gap, this study aimed to investigate how inclusive leadership influenced employee innovative behavior by examining perceived organizational support (POS) as a mediator. We used multi-wave and multi-source data collected at 15 companies in China to test our theoretical model. Results revealed that inclusive leadership had significantly positive effects on POS and employee innovative behavior. Furthermore, POS was positively related to employee innovative behavior and partially mediated the relationship between inclusive leadership and employee innovative behavior. We discussed implications and limitations of this study as well as avenues for future research.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Green finance is an essential instrument for improving the environment and addressing climate change. This study investigates the dynamic spillovers among green finance markets using time-varying ...parameter vector autoregression (TVP-VAR) spillover indices, and further investigates the impact of climate policy uncertainty and investor sentiment on spillovers based on the generalised autoregressive conditional heteroscedasticity mixed data sampling (GARCH-MIDAS) model. The results indicate that: (i) environmental, social and governance (ESG), clean energy and water markets are information transmitters in the green finance system, whereas green building, green transportation, green bond and carbon markets are mainly information receivers; (ii) green stock markets including clean energy, non-energy and ESG markets transmit and receive greater information in the green finance system, while green bond and carbon markets do less; (iii) the green bond market is more interconnected with other green finance markets after the COVID-19 outbreak; (iv) investor sentiment contributes more to the net total directional spillovers of green resource markets (water and clean energy), while climate policy uncertainty contributes more to total spillovers and the net total directional spillovers of other green finance markets. These findings offer invaluable guidance for both policymakers and environmental investors.
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•We quantify the dynamic spillovers among green finance markets.•We reveal the role of climate policy uncertainty and investor sentiment on spillovers.•We employed the GARCH-MIDAS model, which is used to analyze mixed-frequency data.•Green stock markets transmit and receive greater information in the system.•Investor sentiment contributes more to the spillovers of green resource markets.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study deals with
species found and described from two regions of China with large climate differences during the period of 2014-2022. The first region, located in the Wuling Mountains and Hunan ...province, has a subtropical climate and the second in Qinghai, a northwest province of China, has a highland continental climate which is characterized by a cold and long winter and warm, short summer. Previously there were nine new
species published from the first region. This study describes 14 additional new
taxa, nine of which were found in the first region and five of which were found in the second region. A key to the
species that have been described from China is provided. The main morphological characteristics for 63
taxa are summarized in Appendices which allow the division of these
taxa into three groups: the seven members of group one all possess both uniseriate striae and valve marginal spines, the 42 members of group two all possess uniseriate or mostly uniseriate striae but without the valve marginal spines, and the 14 members of group three all possess mostly biseriate striae and without valve marginal spines. To summarize the morphological characters of the published
taxa and 14 taxa described in this study several conclusions for the characterization of
are drawn: 1) each cell has two valve-appressed, long plate-like plastids; 2) living cells of many
species often lie in girdle view on a slide because they have deep mantles and some copulae associated with either the epivalve or the hypovalve so that the cell depth is often larger than the valve width; 3) the basic structures forming a valve include sternum, virgae, and vimines/viminules; 4) the valvocopula is a closed hoop which has a similar ultrastructure in all
taxa but differs from the other copulae in structure; 5) the configuration of girdle bands is a common condition; 6) the life history of
can be divided into the four series of successive stages: auxospore, initial cell, pre-normal vegetative cell, and normal vegetative cell, which is very similar to the life history of
(Lagerstedt) Genkal and Kharitonov; 7) the closed valvocopula is proposed as a definition character for the genus
because demonstrating all girdle bands closed is impracticable.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Overseas energy investment as an effective way of securing energy supply is being favored by the world’s leading energy consuming countries. However, energy investment has the potential high risk on ...multiple forms, including political and regulatory risk, currency, liquidity and refinancing risk as well as resource risk and so on. To effectively evaluate overseas energy investment risk, this study proposed a new indicator system from six dimensions. Furthermore, a fuzzy integrated evaluation model based on the entropy weight was constructed to rate the energy investment risk for 50 nations along China’s “Belt & Road initiative”. The findings indicate that resource potential and Chinese factors have become the main determinant of energy investment risk, while environmental constraints and political risk should also be considered for investing decisions. In conclusion, Saudi Arabia, United Arab Emirates, Pakistan, Kazakhstan, and Russia are the most ideal choices for China’s energy investment balancing resource potential and investment environment.
•Energy investment risk assessment along the “Belt & Road Initiative” is evaluated.•A new indicator system including 36 indicators from six dimensions is proposed.•A fuzzy integrated evaluation model based on the entropy weight is constructed.•The overall energy investment risk along the “Belt & Road Initiative” is relatively high.•Malaysia, Saudi Arabia, UAE, Kazakhstan, and Russia are the ideal investment choices.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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