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BackgroundWith the continuous development of society and economy, nuclear energy has emerged as a crucial solution to address global energy shortages. However, uranium reserves on land have diminished after nearly half a century of extraction, resulting in irreversible harm to the natural environment. Consequently, seawater uranium extraction has become a more eco-friendly and abundant source of uranium resources, compared to terrestrial uranium mining. And extracting uranium from seawater emerges as a prudent strategy.PurposeThis study aims to use kinetic chromatography to extract uranium from seawater and investigate the separation mechanism.MethodsLeveraging the concepts of dynamic chromatography, a pulsed-injection energy-endowed kinetic chromatography column was developed. The column utilized spherical SiO2 with a diameter of 0.2 mm and a length of 5 m as the filler. After filling, it encompassed approximately 30 600 chromatographic separation units. A peristaltic pump for pulse injection was applied to the kinetic chromatography system, and a switching pulse injection device was equipped to enable time control splitting. Subsequently, a self-developed on-line spectrophotometric detector was utilized to monitor the concentration of target components in the solution in real-time so as to avoid human control errors during multistage separation experiments. Additionally, the behavior of uranyl ions in kinetic chromatography under varying conditions and determine the best separation conditions was investigated by experiments using different mobile phase carriers, pH values, injection flow rates, energy-endowed modes, and energy-endowed series, and various energy-endowed methods, including water bath heating, ultrasonic, and external magnetic fields, were employed to achieve optimal separation conditions. Finally, the separation factor of uranium and sodium ions in actual seawater uranium extraction was calculated to explore the separation mechanism by separate studies of uranium, europium, and sodium ions, as well as an actual seawater uranium extraction.ResultResults of this series of studies demonstrates that the best separation effect is achieved when using hydrochloric acid as the mobile phase carrier in dynamic chromatography, with a pH of 2, a sample flow rate of 4.109 mL·min-1, water bath heating as the energy-endowed mode, a heating temperature of 50 ℃, and a heating series of 4. Under these optimal conditions, the separation factor between uranium and sodium ions can reach 1.185 4 in the separation studies of uranium, europium and sodium ions. In the real seawater uranium extraction study, the separation factor between uranium and sodium ions can reach 1.575. After simulation and calculation, the theoretical separation of uranium and sodium ions in seawater requires a minimum of 20 levels.ConclusionsThe efficient and rapid extraction and separation of uranium from seawater is facilitated in this study using a pulsed-injection energy-endowed kinetic chromatography column. This separation strategy allows for the efficient separation of light and heavy particles without interaction of the mobile and stationary, resulting in a high sample recovery rate and no need for column regeneration. This technique has potential for the separation of other nuclides, making it a versatile tool for nuclear chemistry research.

With the successful bidding for the 2022 Beijing Winter Olympics, the ice and snow sports tourism industry has received extensive attention from society. In this paper, under the background of the integration of sports and tourism, we construct an efficiency assessment system for the ice and snow sports tourism industry and then use the entropy value method and coefficient of variation method to improve the DEA model and construct a combination-weighted SE-SBM model to explore the operational and developmental efficiency value of the ice and snow sports tourism industry. In addition, considering the changes in regional industrial productivity over time, the Malmquist index model is utilized to respond to the dynamic development efficiency of the ice and snow sports tourism industry. Finally, the development efficiency of the ice and snow sports tourism industry is analyzed based on the panel data of some regions in China from 2014-2023, in which the mean value of the development efficiency of Jilin and Heilongjiang reaches 1.22778. Among the influencing factors, the value of weight gained from expanding consumption is 0.5426, which is greater than that of the other three factors. This study proposes that expanding consumption is the key to promoting the development of the ice and snow sports tourism industry.

In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.

Under the "dual carbon" background, the development of high-performance energy and environmental catalysis materials is of great significance for promoting energy clean transformation and environmental pollution control. The traditional research and development mode of catalysts mainly relies on experimental and trial-and-error methods, which to a large extent cannot meet the research and development needs of efficient catalysts in emerging energy and environmental fields. The rapid development of data science technologies such as machine learning is expected to bring about a paradigm shift in catalyst research and development. By using machine learning methods to quickly screen high-performance energy and environmental catalysis materials using experimental or computational data, the limitations of traditional trial-and-error methods could be overcome, and the problem of low efficiency and high cost in catalyst research and development could be solved. This article reviewed the main processes and research progress of machine learning methods in the development of energy and environmental catalysis materials from the perspective of active-sites prediction,catalysts screening, morphology design and reaction mechanism revelation, and the ML methods construction corresponding to various training data acquisition and their applications in the catalytic research. We also discussed the future direction of this method in the catalysis field, in order to provide perspective and promote its application in the energy and environmental fields.