We analyze whether oil price uncertainty and U.S. stock uncertainty can simultaneously provide additional information to volatility forecast of six major stock indexes. For model settings, we find ...not only the uncertainty information of previous day, but that of previous week and month will also provide incremental predictive power for the stock market volatility. Based on that, from in-sample and out-of-sample perspective, the empirical evidences imply separately incorporating oil price uncertainty into the model can significantly improve the stock market volatility forecasting performance, but the improvements vanish after controlling the effects of volatility spillover from U.S. stock market while the effect of U.S. stock uncertainty is nonnegligible and sustainable for stock volatility forecasting. We confirm this finding from average and dynamic perspective. We further proceed the process in longer-horizon volatility forecasting, the evidences cannot overturn our conclusion. This conclusion implies that we should be cautious about the stock volatility predictability based on the oil price uncertainty, which further provide some important implications for researchers, regulators and investors.
Abstract
Electronic engineering technology is a symbol of modern technology, and a large number of electronic devices have been produced. This paved the way for the development of the Internet. This ...article analyzes the characteristics of the application of computer electronic engineering technology, and it analyzes the measures to strengthen the application of electronic engineering technology and the measures to promote the development of electronic engineering technology. In addition, this article also studies the teaching system design of electronic engineering technology.
Chirality is essential in nature and often pivotal for biological information transfer, for example, via odor messenger molecules. While the human nose can distinguish the enantiomers of many chiral ...odors, the technical realization by an artificial sensor or an electronic nose, e‐nose, remains a challenge. Herein, we present an array of six sensors coated with nanoporous metal–organic framework (MOF) films of different homochiral and achiral structures, working as an enantioselective e‐nose. While the achiral‐MOF‐film sensors show identical responses for both isomers of one chiral odor molecule, the responses of the homochiral MOF films differ for different enantiomers. By machine learning algorithms, the combined array data allow the stereoselective identification of all compounds, here tested for five pairs of chiral odor molecules. We foresee the chiral‐MOF‐e‐nose, able to enantioselectively detect and discriminate chiral odors, to be a powerful approach towards advanced odor sensing.
An enantioselective electronic nose based on an array of six sensors coated with nanoporous metal–organic framework films of different homochiral and achiral structures is presented. Each chiral film shows a different response for each enantiomer of the chiral odor molecules, allowing their discrimination. All tested odor molecules can be enantioselectively identified with high precision by using the combined data of the sensor array.
The bioavailability of selenium (Se) in soil partly determines Se deficiency in human health. Soil organic matter (OM) is an important soil component that controls Se bioavailability. Better ...understanding of the interaction between selenium and soil organic matter will be scientifically and practically significant in terms of Se risk assessment in the environment and Se biofortification for human health. This paper gives an overview of current understanding on the interaction between soil OM and Se in soil–plant systems, highlighting that OM can immobilize Se by both biotic and abiotic mechanisms and reduce its bioavailability, but the release of OM-immobilized Se through mineralization should not be overlooked. In addition, soil organic amendments also have diverse effects on Se bioavailability. Future research directions and challenges on this topic have been addressed.
•Soil organic matter retains Se in soil by both biotic and abiotic mechanisms.•The immobilization by soil organic matter significantly reduces Se bioavailability.•Se associated with soil organic matter can be slowly released into soil solution.•Soil organic amendments show diverse effects on soil Se bioavailability.•The influential factors of Se-organic matter interaction need to be further studied.
Low impact development (LID) is generally regarded as a more sustainable solution for urban stormwater management than conventional urban drainage systems. However, its effects on urban flooding at a ...scale of urban drainage systems have not been fully understood particularly when different rainfall characteristics are considered. In this paper, using an urbanizing catchment in China as a case study, the effects of three LID techniques (swale, permeable pavement and green roof) on urban flooding are analyzed and compared with the conventional drainage system design. A range of storm events with different rainfall amounts, durations and locations of peak intensity are considered for holistic assessment of the LID techniques. The effects are measured by the total flood volume reduction during a storm event compared to the conventional drainage system design. The results obtained indicate that all three LID scenarios are more effective in flood reduction during heavier and shorter storm events. Their performance, however, varies significantly according to the location of peak intensity. That is, swales perform best during a storm event with an early peak, permeable pavements perform best with a middle peak, and green roofs perform best with a late peak, respectively. The trends of flood reduction can be explained using a newly proposed water balance method, i.e., by comparing the effective storage depth of the LID designs with the accumulative rainfall amounts at the beginning and end of flooding in the conventional drainage system. This paper provides an insight into the performance of LID designs under different rainfall characteristics, which is essential for effective urban flood management.
•Effects of swales, permeable pavements and green roofs on urban flooding are analyzed.•They are more effective in flood reduction during heavier and shorter storm events.•Their performance varies significantly according to the location of storm peak.•The trends of flood reduction can be explained by a proposed water balance method.
The unequivocal detection of CO
2
is important in many situations, like in the living environment, plant cultivation and the conservation of cultural relics and archives. Due to their large specific ...surface areas and highly ordered and tunable structures, metal-organic frameworks (MOFs) have the potential to improve CO
2
sensing, however, they often suffer from low CO
2
affinity and selectivity. Ionic liquids (ILs) have high CO
2
affinity, but their performance in sensors is hampered by their nonporous, liquid form. Here, we present a low-cost and portable CO
2
sensor system based on an array of gravimetric sensors made of MOF films with embedded ILs in the pores. The array is composed of MOF films of two different structures, which are HKUST-1 and UiO-66, filled with 3 different types of ILs and 2 different pore-filling levels, resulting in an array of up to 14 different sensors. We show that the different combinations of IL and MOF result in different affinities for CO
2
and other analytes. With the help of machine learning using a neural network, the sensor array was used to quantify the CO
2
concentration as well as to distinguish CO
2
from other gases and vapors, like nitrogen, ethanol, methanol and water, and to distinguish certain binary mixtures. While the MOF-sensor array without IL achieves only a small accuracy for determining the CO
2
concentration, the IL@MOF sensor array can accurately classify the gas types (98% accuracy) in mixed gas atmospheres of unknown composition and concentration as well as can determine the CO
2
gas concentration with an average error of only 2.7%. Using only MOFs with a pronounced chemical stability (like UiO-66) in the sensor array also allows the detection and identification of CO
2
in a humid atmosphere. Moreover, the presented sensor system has very high sensitivity with a CO
2
limit of detection below 100 ppm, which is four times smaller than the CO
2
concentration in air. This work shows the unprecedented performance of the sensor arrays of MOFs with embedded ILs, referred to as IL@MOF-electronic nose (IL@MOF-e-nose), for sensing the composition and concentration of CO
2
gas mixtures.
A sensor array (or e-nose) made of nanoporous metal-organic framework films filled with different ionic liquids shows high selectivity and sensitivity as well as a very low limit of detection for various common gases and vapors, especially for CO
2
.
Water extraction is an important prerequisite for the protection and rational use of water resources. The existing waterbody extraction methods are mostly used for the extraction of large- and ...medium-sized waterbodies, whereas less attention has been paid to small waterbodies. In this letter, we adapt the U-Net convolutional neural network to extract small waterbodies from Zhuhai-1 satellite hyperspectral remote sensing image. To the best of our knowledge, this is the first time that U-Net framework has been used for small waterbody extraction from satellite hyperspectral image. Specifically, we increase the depth of the network, and because there are far more negative samples (non-waterbodies) in remote sensing data than positive samples (waterbodies), Intersection over Union (IoU) is used as an evaluation indicator during model training. The results show that this method can accurately extract small waterbodies in the complex scenes. Compared with the traditional methods of support vector machine and the normalized waterbody index, the accuracy of this method is significantly higher, and both the Recall and the Precision are close to 90%.
This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE ...crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.
Virtual commerce applies immersive technology such as augmented reality and virtual reality into e-commerce to shift consumer perception from 2D product catalogs to 3D immersive virtual spaces. In ...virtual commerce, the alignment of application design paradigms and the factors influencing consumer behavior is paramount to promote purchase of products and services. The question of their relation needs to be answered, together with the possible improvement of application design. This paper used a systematic literature review approach to synthesize research on virtual commerce from both application design and consumer behavior research, considering the promotion of purchase in virtual commerce settings. Throughout the review, influential factors to purchase and preeminent design artifacts were identified. Then, the research gaps were discovered by mapping the design artifacts to the influential factors, which can inspire future research opportunities on the synergy of these two research directions. Moreover, the evolution of virtual commerce research along with multiple directions were discussed, including the suggestion of meta-commerce as a future trend.
Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force ...on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.
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