Nuclear power will continue to provide energy for the foreseeable future, but it can pose significant challenges in terms of the disposal of waste and potential release of untreated radioactive substances. Iodine is a volatile product from uranium fission and is particularly problematic due to its solubility. Different isotopes of iodine present different issues for people and the environment. 129I has an extremely long half-life of 1.57 × 107 years and poses a long-term environmental risk due to bioaccumulation. In contrast, 131I has a shorter half-life of 8.02 days and poses a significant risk to human health. There is, therefore, an urgent need to develop secure, efficient and economic stores to capture and sequester ionic and neutral iodine residues. Metal–organic framework (MOF) materials are a new generation of solid sorbents that have wide potential applicability for gas adsorption and substrate binding, and recently there is emerging research on their use for the selective adsorptive removal of iodine. Herein, we review the state-of-the-art performance of MOFs for iodine adsorption and their host–guest chemistry. Various aspects are discussed, including establishing structure–property relationships between the functionality of the MOF host and iodine binding. The techniques and methodologies used for the characterisation of iodine adsorption and of iodine-loaded MOFs are also discussed together with strategies for designing new MOFs that show improved performance for iodine adsorption.

Adsorption of iodine in metal–organic framework materials

Stock market forecasting is one of the biggest challenges in the financial market since its time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. However, due to computing development, an intelligent model can help investors and professional analysts reduce the risk of their investments. As Deep Learning models have been extensively studied in recent years, several studies have explored these techniques to predict stock prices using historical data and technical indicators. However, as the objective is to generate forecasts for the financial market, it is essential to validate the model through profitability metrics and model performance. Therefore, this systematic review focuses on Deep Learning models implemented for stock market forecasting using technical analysis. Discussions were made based on four main points of view: predictor techniques, trading strategies, profitability metrics, and risk management. This study showed that the LSTM technique is widely applied in this scenario (73.5%). This work significant contribution is to highlight some limitations found in the literature, such as only 35.3% of the studies analysed profitability, and only two articles implemented risk management. Therefore, despite the widely explored theme, there are still interesting open areas for research and development.

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Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review

The stock market is very unstable and volatile due to several factors such as public sentiments, economic factors and more Several Petabytes volumes of data are generated every second from different sources, which affect the stock market A fair and efficient fusion of these data sources (factors) into intelligence is expected to offer better prediction accuracy on the stock market However, integrating these factors from different data sources as one dataset for market analysis is seen as challenging because they come in a different format (numerical or text) In this study, we propose a novel multi-source information-fusion stock price prediction framework based on a hybrid deep neural network architecture (Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM)) named IKN-ConvLSTM Precisely, we design a predictive framework to integrate stock-related information from six (6) heterogeneous sources Secondly, we construct a base model using CNN, and random search algorithm as a feature selector to optimise our initial training parameters Finally, a stacked LSTM network is fine-tuned by using the tuned parameter (features) from the base-model to enhance prediction accuracy Our approach's emperical evaluation was carried out with stock data (January 3, 2017, to January 31, 2020) from the Ghana Stock Exchange (GSE) The results show a good prediction accuracy of 9831%, specificity (09975), sensitivity (08939%) and F-score (09672) of the amalgamated dataset compared with the distinct dataset Based on the study outcome, it can be concluded that efficient information fusion of different stock price indicators as a single data source for market prediction offer high prediction accuracy than individual data sources

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A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

Automated asphalt pavement damage rate detection based on optimized GA-CNN

Exchange rate movements can significantly impact not only foreign trade, capital flows, and asset portfolio management, but also real economic activity. Therefore, the forecast of exchange rates has always been of great interest among academics, economic agents, and institutions. However, exchange rate series are essentially dynamic and nonlinear in nature and thus, forecasting exchange rates is a difficult task. On the other hand, deep learning models in solving time series forecasting tasks have been proposed in the last half-decade. But the number of formal comparative study in terms of exchange rate forecasting with deep learning models is quite limited. For this purpose, this study applies ten different models (Random Walk, Autoregressive Moving Average, Threshold Autoregression, Autoregressive Fractionally Integrated Moving Average, Support Vector Regression, Multilayer Perceptron, Recurrent Neural Network, Long Short Term Memory, Gated Recurrent Unit and Autoregressive Moving Average-Long Short Term Memory Hybrid Models) and two forecasting modes (recursive and rolling window) to predict three major exchange rate returnsnamely, the Canadian dollar, Australian dollar and British pound against the US Dollar in monthly terms. To evaluate the forecasting performances of the models, we used Model Confidence Set procedure as an advanced test. According to our results, the proposed hybrid model produced the best out-of-sample forecast performance in all samples, without exception.

Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting

LSTM neural network and RNN neural network models in deep learning are used to forecast the price of foreign exchange financial time series. The existing foreign exchange price and technical analysis indexes are taken as input parameters. By comparing the evaluation indexes of two deep learning models, the optimal neural network model is selected. The experimental results show that the LSTM neural network model has smaller root mean square error (RMSE) and mean absolute error (MAE) than the RNN network model, and the predicted price is more accurate.

https://iopscience.iop.org/article/10.1088/1742-6596/1237/4/042036/pdf

Application of LSTM Neural Network in Forecasting Foreign Exchange Price

Adsorption behavior of iodide ion by silver-doped zeolite 4A in LiCl-KCl molten salt

Applications of photothermally mediated nanohybrids for white spot lesions in orthodontics.

Removal of rare earth fission products from LiCl–KCl molten salt by sulfide precipitation

Islanded DC microgrids composed of distributed generators (DGs), constant power loads (CPLs), parallel converters, batteries and supercapacitors (SCs) are typical nonlinear systems, and guaranteeing large-signal stability is a key issue. In this paper, the nonlinear model of a DC microgrid with a hybrid energy storage system (HESS) is established, and large-signal stability criteria are obtained. The HESS consists of batteries and SCs. The derived criteria reveal the influences of the filter parameters, CPL power, DG power and the proportional control parameters of the battery converter and the SC converter on the system large-signal stability. Furthermore, important large-signal stabilization methods for regulating the HESS converter’s control parameters can easily achieve the large-signal stabilization of islanded DC microgrids without extra equipment. The paper is summarized as follows: First, the topology of and control strategy for a DC microgrid with an HESS and CPLs are proposed. Then, according to the characteristics of the HESS, the DGs and the CPLs, the system is equivalently simplified. Finally, the nonlinear model and large-signal stability criteria are both derived using the mixed potential theory, and a large-signal stabilization design method for the HESS converter’s control parameters is proposed. The experimental and simulation results show the effectiveness of the proposed large-signal stabilization method.

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Yaxin Qu

Papers

Application of LSTM Neural Network in Forecasting Foreign Exchange Price

Adsorption behavior of iodide ion by silver-doped zeolite 4A in LiCl-KCl molten salt

Applications of photothermally mediated nanohybrids for white spot lesions in orthodontics.

Removal of rare earth fission products from LiCl–KCl molten salt by sulfide precipitation

Large-Signal Stabilization Method for Islanded DC Microgrids Considering Battery and Supercapacitor Hybrid Energy Storage Systems