Search

Soil moisture is recognized as a crucial variable in land–atmosphere interactions. This study introduces the Convolutional Attention Encoder–Decoder Long Short-Term Memory (CAEDLSTM) model to address the uncertainties and limitations inherent in traditional soil moisture prediction methods, especially in capturing complex temporal dynamics across diverse environmental conditions. Unlike existing approaches, this model integrates convolutional layers, an encoder–decoder framework, and multi-head attention mechanisms for the first time in soil moisture prediction. The convolutional layers capture local spatial features, while the encoder–decoder architecture effectively manages temporal dependencies. Additionally, the multi-head attention mechanism enhances the model's ability to simultaneously focus on multiple key influencing factors, ensuring a comprehensive understanding of complex environmental variables. This synergistic combination significantly improves predictive performance, particularly in challenging climatic conditions. The model was validated using the LandBench1.0 dataset, which includes multiple high-resolution datasets, such as ERA5-land, ERA5 atmospheric variables, and SoilGrids, covering various climatic regions, including high latitudes, temperate zones, and tropical areas. The superior performance of the CAEDLSTM model is evidenced by comparisons with advanced models such as AEDLSTM, CNNLSTM, EDLSTM, and AttLSTM. Relative to the traditional LSTM model, CAEDLSTM achieved an average increase of 5.01% in R2, a 12.89% reduction in RMSE, a 16.67% decrease in bias, and a 4.35% increase in KGE. Moreover, it effectively addresses the limitations of traditional deep learning methods in challenging climates, including tropical Africa, the Tibetan Plateau, and Southeast Asia, resulting in significant enhancements in predictive accuracy within these regions, with R2 values improving by as much as 20%. These results underscore the capabilities of CAEDLSTM in capturing complex soil moisture dynamics, demonstrating its considerable potential for applications in agriculture and water resource monitoring across diverse climates. [ABSTRACT FROM AUTHOR]

Improving the aromatic selectivity in the alkane aromatization process is of great importance for its practical utilization but challenge to make because the high H/C ratio of alkanes would lead to a serious hydrogen transfer process and a large amount of light alkanes. Herein, CO2 is introduced into the cyclohexane conversion process on the HZSM-5 zeolite, which can improve the aromatic selectivity. By optimizing the reaction conditions, an improved aromatic (benzene, toluene, xylene, and C9+) selectivity of 48.2% can be obtained at the conditions of 2.7 MPa (CO2), 450 °C, and 1.7 h−1, which is better than that without CO2 (aromatic selectivity = 43.2%). In situ transmission Fourier transform infrared spectroscopy spectra illustrate that many oxygenated chemical intermediates (e.g., carboxylic acid, anhydride, unsaturated aldehydes/ketones or ketene) would be formed during the cyclohexane conversion process in the presence of CO2. 13C isotope labeling experimental results demonstrate that CO2 can enter into the aromatics through the formation of oxygenated chemical intermediates and thereby improve the aromatic selectivity. This study may open a green, economic, and promising way to improve the aromatic selectivity for alkane aromatization process. [ABSTRACT FROM AUTHOR]

Objective: Reminiscence therapy provides benefits among first-episode acute ischemic stroke (AIS) patients and their caregivers. This study intended to further compare the effect of reminiscence therapy plus usual care (RTUC) and usual care (UC) on cognitive function, anxiety, and depression among recurrent AIS patients. Methods: Totally, 160 recurrent AIS patients were enrolled and randomized in a 1:1 ratio into the RTUC group (N = 81) and UC group (N = 79), then a 12-month corresponding intervention was conducted in each group. Mini-Mental State Examination (MMSE) score and Hospital Anxiety and Depression Scale (HADS) were evaluated on discharge (M0), at month (M) 3, M6, M9, and M12 after discharge. Results: MMSE scores at M6 (27.0 ± 1.6 vs. 26.3 ± 2.3, P = 0.031) and M12 (27.0 ± 1.7 vs. 26.1 ± 2.4, P = 0.009) were elevated, while cognitive impairment rate at M12 (29.2% vs. 45.7%, P = 0.042) and cognitive impairment severity at M12 (P = 0.029) were declined in RTUC group compared to UC group. Meanwhile, the HADS-anxiety scores at M9 (5.7 ± 3.1 vs. 6.9 ± 4.0, P = 0.046) and M12 (5.6 ± 2.7 vs. 7.0 ± 4.3, P = 0.024), anxiety rate at M12 (22.2% vs. 38.2%, P = 0.039) and anxiety severity at M12 (P = 0.018) were declined in RTUC group compared to UC group. Besides, the HADS-depression score at M12 (5.7 ± 3.1 vs. 6.8 ± 3.3, P = 0.043) was decreased in RTUC group compared to UC group, but depression rate and severity were not different between the two groups at each visit point (all P > 0.05). Conclusion: RTUC program elevates cognitive functions and alleviates mental problems in recurrent AIS patients. [ABSTRACT FROM AUTHOR]

Recognizing the structure and nature of the nuclei for zeolites crystallization on an atomic level is of great importance, which can provide guidance on the control of crystallization kinetics and the rational synthesis of zeolites. However, it remains a long‐standing challenge due to the difficulty in characterization of amorphous precursor with limited crystal nuclei. Herein, a top‐down synthesis system was designed for SAPO‐34 molecular sieve and well investigated. A clear precursor solution with abundant SAPO‐34 crystal nuclei was obtained under a depolymerization‐dominant condition. The species in the liquid precursor were identified by FT‐ICR MS, solid‐state MAS NMR and atomic pair distribution function analyses. In combination with various designed experiments, it is revealed that both the formation of small species containing Si−O−Al bonds and reaching a certain concentration, is crucial for driving the crystallization of SAPO‐34, rather than structural units with specific spatial conformation. This work provides an important understanding on the (pre)nucleation of SAPO‐34 and sheds light on the synthesis control of SAPO molecular sieves. [ABSTRACT FROM AUTHOR]