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Objectives We assessed the effects of different exercise modalities and doses on depression levels in older adults. Methods Systematic searches of the PubMed, Web of Science, Embase, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Scopus databases were conducted from the start of database construction until December 2023. Studies were included if they were randomised controlled trials (RCTs) of exercise interventions in older adults. Pairwise, network and dose-network meta-analyses were conducted using random-effects models for the outcome of depression in older adults. Results A total of 80 RCTs with 5536 participants were included in this study. Network meta-analyses showed that resistance exercise [standardized mean difference (SMD) = − 0.68, 95% confidence interval (CI): − 0.90, − 0.46] and mind–body exercise (MBE; SMD = − 0.54, 95% CI: − 0.72, − 0.37) were the most effective forms of exercise for improving depression in older adults, followed by aerobic exercise (SMD = − 0.31, 95% CI: − 0.50, − 0.13) and mixed exercise (SMD = − 0.23, 95% CI: − 44, − 0.01). In addition, a U-shaped dose–response relationship was found between overall exercise dose and depression levels in older adults, and a significant response was seen after 390 metabolic equivalent (MET)-min/week. Conclusions Our study determined the effectiveness of different exercises in improving levels of older adults and found that resistance exercise and MBE were more effective adjunctive treatments. By providing the most effective treatments, older adults can reap the benefits of improving depression in older adults at doses lower than the World Health Organization guidelines. [ABSTRACT FROM AUTHOR]

As the capital of Tang Dynasty, Chang'an was one of the most prosperous cities in the world at that time and had a profound influence on Tang poetry. Poets described Chang'an to illustrate the cultural features of the Tang Dynasty while also invoking emotions in readers. The study of Tang Chang'an poetry has important literary and historical value. In order to understand the interpretation and emotional expression of Tang Chang'an poetry more conveniently and clearly, we conducted a study using deep learning to classify Chang'an poetry into four classes: imperially assigned poetry (应制), emotional poetry (感怀), parting poetry (离别), and other poetry (其他). We suggested a comprehensive framework of text classification based on deep learning, including a text input module, feature encoder module, and classification module. We applied several mainstream deep neural network structures to extract features in different ways, which comprised convolutional neural network (CNN), Fasttext, bi-direction long-short-term memory network, and Attention mechanism. Based on our experimental findings, the CNN-based method achieved the best performance for the task. Our inference was that, in Chinese ancient poetry, the analysis of semantic content is more facilitated by local textual features rather than contextual features. We combined this inference with the theory of image in Chinese ancient poetry to analyze the suitability of the deep learning techniques for the study of Chinese ancient poetry. [ABSTRACT FROM AUTHOR]

Doping engineering is an effective strategy to improve the electrocatalytic activity of manganese oxides by enhancing their poor electrical conductivity and oxygen adsorption capacity. Herein, p‐block aluminum group metal ions (Al3+, Ga3+, and In3+) are introduced into cryptomelane‐type manganese oxide octahedral molecular sieves (OMS‐2), leading to p−d orbital hybridization between the p‐orbitals of the aluminum group metals and d‐orbitals of Mn, facilitating the oxygen reduction reaction. The aluminum group metal‐doped OMS‐2 exhibits excellent catalytic activity, rapid reaction kinetics, and favorable stability compared to commercial Pt/C. Among the three prepared catalysts, Ga‐doped OMS‐2 (Ga‐OMS‐2) has stronger oxygen reduction activity. Experimental and theoretical calculations show that the superiority of Ga‐OMS‐2 is attributed to p−d hybridization, which enriches the reaction sites and enhances the binding strength of the catalyst to the O2 reaction intermediates. As a proof of concept, Zinc−air batteries assembled with Ga‐OMS‐2 as a catalyst exhibit superior power density and cycle life to commercial Pt/C. This p−d hybridization strategy gives insight into the p‐block metal doping of catalysts prepared with other transition metals with excellent electrocatalytic activity and durability for energy storage and conversion. [ABSTRACT FROM AUTHOR]