A SYSTEMATIC REVIEW OF THE RESEARCH PAPERS ON CHEMISTRY-FOCUSED SOCIO-SCIENTIFIC ISSUES

Although chemistry-focused socio-scientific issues support the ‘relevance’ model of chemistry education, the related literature has lacked any systematic review handling them together. For this reason, this research aimed to thematically synthesize the research papers on chemistry-focused socio scientific issues (SSI) from 2008 to 2020 and inferentially evaluate them in terms of the relevance model of chemistry education. After searching international and national well-known databases through relevant keyword patterns (e.g., Pattern 1: socio-scientific issues and chemistry education), 65 research papers were apparent for the systematic review. Then, the authors generated primary and secondary codes for the research papers and then inferentially marked their ‘relevance’ components. The systematic review indicated variation of research areas (e.g., relevance model of chemistry education) and dominant research foci for different themes (e.g., competencies and related variables for the theme ‘aims’; pollution, energy, industry and fabrication-based problems for the theme ‘SSI’; organic compounds for the theme ‘chemistry concepts’). Further, it revealed that the research papers on chemistry-focused SSI had some shortcomings at handling all components of the relevance model in a balanced way. The current research suggests professionally training teachers about how to integrate chemistry-focused SSI and the relevance model into school chemistry. Keywords: chemistry education, relevance model, socio-scientific issues, systematic review

Discovering related data at scale

Analysts frequently require data from multiple sources for their tasks, but finding these sources is challenging in exabyte-scale data lakes. In this paper, we address this problem for our enterprise's data lake by using machine-learning to identify related data sources. Leveraging queries made to the data lake over a month, we build a relevance model that determines whether two columns across two data streams are related or not. We then use the model to find relations at scale across tens of millions of column-pairs and thereafter construct a data relationship graph in a scalable fashion, processing a data lake that has 4.5 Petabytes of data in approximately 80 minutes. Using manually labeled datasets as ground-truth, we show that our techniques show improvements of at least 23% when compared to state-of-the-art methods.

Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling

At the present stage, China’s energy development has the following characteristics: continuous development of new energy technology, continuous expansion of comprehensive energy system scale, and wide application of multi-energy coupling technology. Under the new situation, the accurate prediction of power load is the key to alleviate the problem that the planning and dispatching of the current power system is more complex and more demanding than the traditional power system. Therefore, firstly, this paper designs the calculation method of the power load demand of the grid under the multi-energy coupling mode, aiming at the important role of the grid in the power dispatching in the comprehensive energy system. This load calculation method for regional power grid operating load forecasting is proposed for the first time, which takes the total regional load demand and multi-energy coupling into consideration. Then, according to the participants and typical models in the multi-energy coupling mode, the key factors affecting the load in the multi-energy coupling mode are analyzed. At this stage, we fully consider the supply side resources and the demand side resources, innovatively extract the energy system structure characteristics under the condition of multi-energy coupling technology, and design a key factor index system for this mode. Finally, a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is proposed, to carry out load forecasting for multi-energy coupling scenarios. Aiming at the complexity energy system analysis and prediction accuracy improvement of multi-energy coupling scenarios, this method applies minimal redundancy maximal relevance model to the selection of key factors in scenario analysis. It is also the first time that adaptive fireworks algorithm is applied to the optimization of adaptive fireworks algorithm, and the results show that the model optimization effect is good. In the case of A region quarterly load forecasting in southwest China, the average absolute percentage error of a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is 2.08%, which means that this model has a high forecasting accuracy.