Applications of Robotic Process Automation in Smart Governance to Empower COVID-19 Prevention.

This paper explores how the robotic process automation (RPA) can benefit financial applications. To fully exploit RPA technologies potential will empower higher education and finance, which makes a better future together. The mechanism of RPA to mimic the process of human thinking in solving financial problems was discussed. Important technologies, challenges from cooperativeness, responsiveness and interconnectedness were explored. Exploration of automation technologies for COVID-19 prevention will reduce virus transmission, which empowers society governance, higher education and finance.

A novel hybrid learning paradigm with feature extraction for carbon price prediction based on Bi-directional long short-term memory network optimized by an improved sparrow search algorithm.

An efficient carbon trading market can effectively curb excessive carbon emissions and thus slow down the pace of global warming, which heightens the necessity of improving the accuracy of carbon price forecasting. In order to overcome the weakness of previous prediction model that always trained data in one-way neural networks and propagated the data sequentially, this paper proposes a novel hybrid learning paradigm WPD-ISSA-BiLSTM combining wavelet packet decomposition (WPD), improved sparrow search algorithm (ISSA), and Bi-directional long short-term memory network for deep feature exploration of carbon prices. Firstly, WPD decomposes and reconstructs the original carbon price series into several independent subseries. Then, the input features of the all subseries are filtered with random forest to select the best input features for the prediction model. Finally, a Bi-directional long short-term memory network optimized by the ISSA is employed to deeply delineate the intrinsic evolutionary trends of carbon prices, and the prediction results of all subseries are superimposed on each other to obtain the final carbon price prediction results. The actual carbon emission trading prices are collected as input to the model, and the experimental results show that the RMSE values of the proposed model are 0.2516 and 0.2962 under the mild and severe volatility scenarios, respectively. The proposed model has superiority and robustness compared to the comparison model and several existing models and better understands the intrinsic correlation between historical carbon price data. The results of this study can provide meaningful references for the carbon market development and emission reduction pathways.

A Fluorescent Chemodosimeter for Live-Cell Monitoring of Aqueous Sulfides.

Aqueous sulfides are emerging signaling agents implicated in various pathological and physiological processes. The development of sensitive and selective methods for the sensing of these sulfides is therefore very important. Herein, we report that the as-synthesized 1-oxo-1H-phenalene-2,3-dicarbonitrile (OPD) compound provides promising fluorescent properties and unique reactive properties toward aqueous sulfides. It was found that OPD showed high selectivity and sensitivity toward Na2S over thiols and other inorganic sulfur compounds through a sulfide involved reaction which was confirmed by high-resolution mass spectroscopy (HRMS) and nuclear magnetic resonance (NMR) results. The fluorescence intensity increases linearly with sulfide concentration in the range of 1.0-30 µM with a limit of detection of 52 nM. This novel fluorescent probe was further exploited for the fluorescence imaging sensing of aqueous sulfide in HeLa cells.

Simultaneous detection of hydrogen peroxide and glucose in human serum with upconversion luminescence.

In this paper, a green upconversion photoluminescence (UCPL) system for the highly sensitive and selective detection of H2O2 and glucose in human sera was developed by utilizing the excellent optical properties of NaYF4:Yb(3+)/Er(3+) upconversion nanoparticles (UCNPs). In the presence of H2O2, the colorless 3,3',5,5'-tetramethylbenzidine (TMB) was oxidized into blue oxidized TMB (oxTMB) by assistance of horseradish peroxidase (HRP). And the green UCPL of UCNPs was quenched by the oxTMB linearly over the range of 100 nM-4.0 µM with a low limit of detection (LOD) of 45.0 nM for H2O2. Based on the transformation of glucose into H2O2 by means of glucose oxidase, the TMB-UCNPs-HRP system was further exploited for the highly sensitive detection of glucose in the range of 0.1-5.0 µM with a LOD of 64.0 nM in human sera independent of preconcentration and purification. The results are in good agreement with the clinical data, suggesting that this UCPL nanosensor is highly practical.