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Predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO2) emissions. This research paper delves into an extensive exploration of different algorithms, encompassing neural networks with diverse architectures, optimization, training, ensemble, and specialized algorithms. The primary objective of this research is to evaluate the efficacy of supervised and unsupervised algorithms, including Deep Belief Networks, Feed Forward Neural Networks, Gradient Boosting, and Regression, as well as Convolutional Neural Networks, Gaussian, Grey, and Markov models, and clustering and optimization algorithms. The study places particular emphasis on data-driven methodologies and cross-validation techniques with an evaluation of the learning models entailing comprehensive training, validation, and testing, employing evaluation metrics such as R2, MAE, and RMSE. The study employs correlation analysis to examine the relationship between input parameters and emission characteristics. The research highlights the advantageous attributes of these algorithms in accurately forecasting CO2 emissions, evaluating energy sources, improving prediction accuracy, and estimating emissions. Notably, deep learning, Artificial Neural Networks (ANN), and Support Vector Machines (SVM) demonstrate effectiveness across diverse industries, while the Modified Regularized Fast Orthogonal-Extreme Learning Machine (MRFO-ELM) algorithm optimizes predictions specifically related to coal chemical emissions. Hybrid techniques demonstrate accuracy in predicting carbon emissions and energy consumption, whereas gray prediction models provide reliable estimates even with limited data. However, it is important to acknowledge certain limitations, including data requirements, potential inaccuracies arising from complex factors, constraints faced by developing countries, and the impact of electric vehicle expansion on the power grid. To optimize models, a survey is conducted, involving customization of parameters and learning rates, while exploring various performance metrics to evaluate model accuracy. The research outcomes contribute to the effective monitoring of CO2 emissions in operational environments, thereby aiding executive decision-making processes. [ABSTRACT FROM AUTHOR]

Vietnam and China have a long history of cultural contact, but the connotative concept of "contact" is still a difficult question which requires more investigation. Thus, to this day, there is still the notion that the Vietnamese culture is a complete replication of Chinese culture, especially when it comes to higher culture such as ideologies, literature, and art. Tang poetry can be considered the pinnacle of Chinese literature. This genre of poetry spread out and influenced the literature of East Asian countries, especially Vietnamese medieval literature. The artic le will survey the linguistic forms of the "four seasons" symbols in T ang and Nom poetry, going into the core of similarities and exploring the differences. [ABSTRACT FROM AUTHOR]

Abstract: A numerical simulation of the performance of a fin‐tube‐type adsorption bed with silica gel/water working pairs was conducted. Three models of the heat recovery cycle, the mass recovery cycle, and a combined heat and mass recovery cycle were closely examined. The main goals were to determine 1) the conditions under which these advanced cycles were most effective and 2) the optimum recovery time. Mass recovery enhanced both the coefficient of performance (COP) and specific cooling power (SCP) by up to 24 and 37.5 %, respectively, at 60 °C, and the enhancements of the COP and SCP were 5.0 and 16.0 %, respectively, at 90 °C. Heat recovery increased the COP by 12.56 %, but reduced the SCP by 10.84 % at 60 °C, whereas, at 90 °C, the COP increased by 11.83 % and SCP decreased by 5.96 %. The mass recovery is more influential at a low heating temperature than that at a high heating temperature. Therefore, in the combined heat and mass recovery cycles, the main contribution to the enhancement of the COP comes from mass recovery at lower water temperature. However, at a high heating temperature, the COP increases mainly due to heat recovery. [ABSTRACT FROM AUTHOR]