Layered O3-type oxides are one of the most promising cathode materials for Na-ion batteries owing to their high capacity and straightforward synthesis. However, these materials often experience irreversible structure transitions at elevated cutoff voltages, resulting in compromised cycling stability and rate performance. To address such issues, understanding the interplay of the composition, structure, and properties is crucial. Here, we successfully introduced a P-type characteristic into the O3-type layered structure, achieving a P3-dominated solid-solution phase transition upon cycling. This modification facilitated a reversible transformation of the O3-P3-P3′ structure with minimal and gradual volume changes. Consequently, the Na_0.75Ni_0.25Cu_0.10Fe_0.05Mn_0.15Ti_0.45O₂ cathode exhibited a specific capacity of approximately 113 mAh/g, coupled with exceptional cycling performance (maintaining over 70% capacity retention after 900 cycles). These findings shed light on the composition-structure-property relationships of Na-ion layered oxides, offering valuable insights for the advancement of Na-ion batteries.

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To realize concurrently the high-energy density and excellent cycling stability, maximum utilization of redox couple, minimization of detrimental phase transition, and structural degradation of O3-type layered oxide cathodes are critical for developing Na-ion batteries. Ni²⁺/Ni⁴⁺ redox couple showing multielectron reaction and higher redox potential is favorable to increase the energy density. However, the Jahn-Teller distortion of Ni³⁺ generated upon (dis)charging results in a strong anisotropy in the local crystal structure that causes irreversible interlayer bending and chemo-mechanical cracks of the cathode particles, compromising the electrochemical properties eventually. In this work, we show a slight multielement doping strategy that enlarges the amount of active redox components while minimizing the inactive contents. The results show that the uniform distribution of multiple components can help increase the disorder degree of atom arrangement and alleviate the structural changes and detrimental anisotropy cracks. As a proof of concept, a multielement-doped O3-type Na_0.9Ni_0.25Cu_0.05Mg_0.05Zn_0.05Fe_0.05Al_0.05Mn_0.40Ti_0.05Sn_0.05O₂ oxide is rationally prepared that presents better chemo-mechanical stability and delayed O3-P3 phase transition behavior. Compared to the high Ni-content Na_0.9Ni_0.35Fe_0.2Mn_0.45O₂ cathode, this as-prepared multielement material delivers a reversible capacity of about 120 mAh/g in the voltage range of 2-4.0 V, superior cycling stability with 90% of capacity retention after 500 cycles, and excellent rate capability (more than 70% of initial capacity at 5.0 C). This work indicates that the multielement doping method is highly suitable for the development of advanced Na-ion layered oxide cathodes.

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Emotion recognition based on physiological data has attracted increasing attention in physiological monitoring, affective computing, and other fields. This paper proposes a method to classify human's emotion for health monitoring in physical activities by using machine learning. Participants completed the experiment including walking, running, and other physical activities. The data of photoplethysmography (PPG) and electrodermal activity (EDA) were recorded by wearable sensors on participants. After the data processing and feature extraction, two classifiers, support vector machine (SVM) and random forest (RF) were applied independently on the dataset to classify human's emotion, including calm, excited, relaxed, bored, and afraid. As a result, the SVM classifier achieved an accuracy of 81.87% and the accuracy of RF classifier is 86.61%. These results demonstrated the effectiveness of the proposed method on emotion recognition in human's physical activities.

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Background
Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.
Results
We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships.
Conclusions
We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.
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