Crafting Core-Shell Heterostructures with Enriched Active Centers for High-Energy-Density Symmetric Lithium-Ion Batteries.

Current research strives to create sustainable and ecofriendly organic electrode materials (OEMs) due to the rising concerns about traditional inorganic electrode materials that call for substantial resource consumption in battery manufacturing. However, OEMs often exhibit unbalanced performance, with high capacity conflicting with a long lifespan. Herein, a 2D fully conjugated covalent organic framework featuring abundant C═O and C═N groups (HTPT-COF) was strategically synthesized by coupling 2,3,7,8-tetraamino-1,4,6,9-tetraketone with hexaketocyclohexane octahydrate. It stabilizes the enriched active centers by an extended π-conjugated skeleton, thereby affording a high theoretical capacity in conjunction with potential structure stability. To further unlock the barriers of fast charge, the HTPT-COF was interwoven around highly conductive carbon nanotubes, creating a robust core-sheath heterostructure (HTPT-COF@CNT). Consequently, the crafted HTPT-COF@CNT achieves large reversible capacities of 507.7 mA h g-1, high-rate performance (247.8 mA h g-1 at 20.0 A g-1), and long-term durability (1000 cycles). Aiming to streamline the process and cut the cost of battery manufacturing, all-organic symmetric batteries were well fabricated using HTPT-COF@CNT as both cathode and anode, demonstrating high energy/power density (up to 191.7 W h kg-1 and 3800.3 W kg-1, respectively) and long-term stability over 1000 cycles. Such HTPT-COF@CNT represents a promising sustainable electrode that effectively addresses irreconcilable contradictions encountered by OEMs, boosting the development of advanced organic batteries with high capacity and cycling stability.

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis.

BACKGROUND: Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable. OBJECTIVE: We aimed to assess the value of applying machine learning in predicting the time of stroke onset. METHODS: PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team). RESULTS: Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86). CONCLUSIONS: Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds. TRIAL REGISTRATION: PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898.

[Study on consistency of assessed results according to the Standard of Evaluated Injured Severity].

OBJECTIVE: To explore the consistency of assessed results according to Standard of Evaluated Injured Severity, finding out some factors that influenced appraisal conclusion. METHODS: 102 cases examined by Beijing Institute of Forensic Medicine and Science in 1998 were re-evaluated respectively by nine appraisers. RESULTS: The results showed that distinction of appraisal conclusion between appraisers in the same institute was small, but in different institute was big. The work experience and professional train were important to reduce errors. CONCLUSION: Standard of Evaluated Injured Severity strong take on character of profession. Veracity of assessed injured severity is related with unitive authoritative explanation, training and experience of appraiser.