Hypervalent iodine compounds are privileged reagents in organic synthesis because of their exceptional reactivity. Among these compounds, cyclic derivatives stand apart because of their enhanced ...stability. They have been widely used as oxidants, but their potential for functional‐group transfer has only begun to be investigated recently. The use of benziodoxol(on)es for trifluoromethylation (Togni's reagents) is already widely recognized, but other transformations have also attracted strong interest recently. In this Review, the development in the area since 2011 will be presented. After a short summary of synthetic methods to prepare benziodoxol(on)e reagents, their use to construct carbon–heteroatom and carbon–carbon bonds will be presented. In particular, the introduction of alkynes by using ethynylbenziodoxol(on)e (EBX) reagents has been highly successful. Breakthroughs in the introduction of alkoxy, azido, difluoromethyl, and cyano groups will also be described.
The exceptional reactivity of hypervalent iodine compounds make them privileged reagents in organic synthesis. Of them, cyclic derivatives stand apart because of their enhanced stability. The use of benziodoxol(on)es for trifluoromethylation (Togni's reagents) is now well recognized, but other transformations have also attracted strong interest recently. The progress in the area since 2011 is presented in this Review. Q=8‐aminoquinoline.
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Electrical power system (EPS) forecasting plays a significant role in economic and social development but it remains an extremely challenging task. Because of its significance, relevant studies on ...EPS are especially needed. More specifically, only a few of the previous studies in this area conducted in-depth investigations of the entire EPS forecasting and merely focused on modeling individual signals centered on wind speed or electrical load. Moreover, most of these past studies concentrated on accuracy improvements and usually ignore the significance of forecasting stability. Therefore, to simultaneously achieve high accuracy and dependable stability, a hybrid forecasting framework based on the multi-objective dragonfly algorithm (MODA) was successfully developed in this study. The framework consists of four modules—data preprocessing, optimization, forecasting, and evaluation modules. In this framework, MODA is employed to optimize the Elman neural network (ENN) model as a part of the optimization module to overcome the drawbacks of single-objective optimization algorithms. In addition, data preprocessing and evaluation modules are incorporated to improve forecasting performance and conduct a comprehensive evaluation for this framework, respectively. Empirical results reveal that the developed forecasting framework can be an effective tool for the planning and management of power grids.
•A novel forecasting framework is developed for electrical power system.•Propose a modified Elman neural network based on multi-objective optimization.•The accuracy and stability of developed framework are improved simultaneously.•The results are validated well in a whole electrical power system.
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To address climate change effectively, it is essential to quantify CO2 emissions and the driving factors in high-energy-consuming countries. China is the top CO2-emitting country; moreover, there is ...a lack of comprehensive analytical studies on quantifying the contributions of key drivers to high-energy-consuming countries' CO2 emissions. Therefore, based on data of China's energy consumption from 2005 to 2016, this paper combines the extended Kaya identity with the logarithmic mean Divisia index (LMDI) decomposition method to construct an optimized carbon emission decomposition model. Carbon emission and carbon emission intensity are measured and decomposed. Then, the results of the decomposition are discussed, and the effects of various drivers on carbon emissions from energy consumption in China are analysed. Furthermore, we demonstrate real applications of decomposition analysis in policy-making using examples from China and present some ideas to reduce CO2. The results show that from 2005 to 2016, China's total carbon emissions accounted for nearly one-third of the world's total carbon emissions, and the intensity of carbon emissions in China was generally higher than that of worldwide. The rapid development of economy and acceleration of urbanization are not conducive to reduction of carbon emissions. Reducing the intensity of energy consumption, adjusting the internal structure of the industry and perfecting the economic policy system should be important means used to promote the development of China's low-carbon economy in the future.
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•Economic output increase is the largest driving factor promoting the CO2 emissions of China.•In China, CO2 missions from energy consumption in industrial sector accounted for the greatest proportion.•Phasing out excess capacity in China reduced emissions in the industry sector.•Energy intensity reduction is a key for high-energy-consuming countries to reduce CO2 emissions.•Promoting the transformation of the industrial structure is an efficient way to reduce CO2 emissions.
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Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder that potentially threatened people's cardiovascular system. As an alternative to polysomnography for OSA detection, ...ECG-based methods have been developed for several years. However, previous work is focused on feature engineering, which is highly dependent on the prior knowledge of human experts and maybe subjective. Moreover, feature engineering also highlights the prominent shortcoming of current learning algorithms that the features are unable to extracted and organized from the data. In this study, we proposed a method to detect OSA based on deep neural network and Hidden Markov model (HMM) using single-lead ECG signal. The method utilized sparse auto-encoder to learn features, which belongs to unsupervised learning that only requires unlabeled ECG signals. Two types classifiers (SVM and ANN) are used to classify the features extracted from the sparse auto-encoder. Considering the temporal dependency, HMM was adopted to improve the classification accuracy. Finally, a decision fusion method is adopted to improve the classification performance. About 85% classification accuracy is achieved in the per-segment OSA detection, and the sensitivity is up to 88.9%. Based on the results of per-segment OSA detection, we perfectly separate the OSA recording from normal with accuracy of 100%. Experimental results demonstrated that our proposed method is reliable for OSA detection.
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Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time ...series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.
Copper is uniquely active for the electrocatalytic reduction of carbon dioxide (CO2) to products beyond carbon monoxide, such as methane (CH4) and ethylene (C2H4). Therefore, understanding ...selectivity trends for CO2 electrocatalysis on copper surfaces is critical for developing more efficient catalysts for CO2 conversion to higher order products. Herein, we investigate the electrocatalytic activity of ultrathin (diameter ∼20 nm) 5-fold twinned copper nanowires (Cu NWs) for CO2 reduction. These Cu NW catalysts were found to exhibit high CH4 selectivity over other carbon products, reaching 55% Faradaic efficiency (FE) at −1.25 V versus reversible hydrogen electrode while other products were produced with less than 5% FE. This selectivity was found to be sensitive to morphological changes in the nanowire catalyst observed over the course of electrolysis. Wrapping the wires with graphene oxide was found to be a successful strategy for preserving both the morphology and reaction selectivity of the Cu NWs. These results suggest that product selectivity on Cu NWs is highly dependent on morphological features and that hydrocarbon selectivity can be manipulated by structural evolution or the prevention thereof.
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The Devonian Chattanooga Shale contains an uppermost black shale interval with dispersed phosphate nodules. This interval extends from Tennessee to correlative strata in Kentucky, Indiana, and Ohio ...and represents a significant period of marine phosphate fixation during the Late Devonian of North America. It overlies black shales that lack phosphate nodules but otherwise look very similar in outcrop. The purpose of this study is to examine what sets these two shales apart and what this difference tells us about the sedimentary history of the uppermost Chattanooga Shale.
In thin section, the lower black shales (PBS) show pyrite enriched laminae and compositional banding. The overlying phosphatic black shales (PhBS) are characterized by phosbioclasts, have a general banded to homogenized texture with reworked layers, and show well defined horizons of phosphate nodules that are reworked and transported. In the PhBS, up to 8000 particles of P-debris per cm2 occur in reworked beds, whereas the background black shale shows between 37–88 particles per cm2. In the PBS, the shale matrix contains between 8–16 phosphatic particles per cm2. The shale matrix in the PhBS contains 5.6% inertinite, whereas just 1% inertinite occurs in the PBS. The shale matrix in both units is characterized by flat REE patterns (shale-normalized), whereas Phosbioclast-rich layers in the PhBS show high concentrations of REEs and enrichment of MREEs. Negative Ce-anomalies are common to all samples, but are best developed in association with Phosbioclasts. Redox-sensitive elements (Co, U, Mo) are more strongly enriched in the PBS when compared to the PhBS. Trace elements associated with organic matter (Cu, Zn, Cd, Ni) show an inverse trend of enrichment.
Deposited atop a sequence boundary that separates the two shale units, the PhBS unit represents a transgressive systems tract and probably was deposited in shallower water than the underlying PBS interval. The higher phosphate content in the PhBS is interpreted as the result of a combination of lower sedimentation rates with reworking/winnowing episodes. Three types of phosphatic beds that reflect different degrees of reworking intensity are observed. Strong negative Ce anomalies and abundant secondary marcasite formation in the PhBS suggests improved aeration of the water column, and improved downward diffusion of oxygen into the sediment. The associated oxidation of previously formed pyrite resulted in a lowering of pore water pH and forced dissolution of biogenic phosphate. Phosphate dissolution was followed by formation of secondary marcasite and phosphate. Repeated, episodic reworking caused repetitive cycles of phosphatic dissolution and reprecipitation, enriching MREEs in reprecipitated apatite. A generally “deeper” seated redox boundary favored P-remineralization within the sediment matrix, and multiple repeats of this process in combination with wave and current reworking at the seabed led to the formation of larger phosphatic aggregates and concentration of phosphate nodules in discrete horizons.
•A hierarchy of mechanical phosphate enrichment was observed.•Lower sedimentation rates and reworking episodes favored high phosphate enrichment.•Fluctuation of redox boundary and pH levels caused phosphate remineralization.•Microbial related phosphate was observed.•Two types of Tasmanites cysts indicated diagenetic sequence in phosphate nodules.
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In 1935, Einstein, Podolsky, and Rosen (EPR) conceived a gedanken experiment which became a cornerstone of quantum technology and still challenges our understanding of reality and locality today. ...While the experiment has been realized with small quantum systems, a demonstration of the EPR paradox with massive many-particle systems remains an important challenge, as such systems are particularly closely tied to the concept of local realism in our everyday experience and may serve as probes for new physics at the quantum-to-classical transition. In this work we report an EPR experiment with two spatially separated Bose-Einstein condensates, each containing about 700 rubidium atoms. Entanglement between the condensates results in strong correlations of their collective spins, allowing us to demonstrate the EPR paradox between them. Our results represent the first observation of the EPR paradox with spatially separated, massive many-particle systems. They show that the conflict between quantum mechanics and local realism does not disappear as the system size increases to more than a thousand massive particles. Furthermore, EPR entanglement in conjunction with individual manipulation of the two condensates on the quantum level, as we demonstrate here, constitutes an important resource for quantum metrology and information processing with many-particle systems.
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