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Succinonitrile‐based plastic crystal electrolytes have emerged for high‐energy‐density Li metal batteries in terms of their superior ambient ionic conductivity, low flammability, and benign compatibility with high voltage cathode, but are hampered by inherent instabilities toward Li anodes. Constructing hierarchical solid electrolytes structure is a fundamental approach to protect Li anode from succinonitrile attacks, with succinonitrile‐based oxidation‐resistance layer facing high voltage cathode and reduction‐tolerant layer contacting Li anode. However, free succinonitrile molecules in succinonitrile‐based electrolyte layer can diffuse across the electrolyte/electrolyte interface and further reach Li anode surface during the battery cycle. This chemical "crosstalk" cause reduction‐tolerant electrolyte layer to fail to protect the Li anode from the attacks of free succinonitrile molecules. Nano Li6.4La3Zr1.4Ta0.6O12 is introduced creatively into succinonitrile‐based electrolyte layer. By taking advantage of the complexation between La atoms in Li6.4La3Zr1.4Ta0.6O12 and N atoms in succinonitrile, the free succinonitrile molecules are successfully immobilized in succinonitrile‐based electrolyte layer. The resulting low resistance and highly durable solid electrolyte interphase and cathode electrolyte interphase endow NCM622||Li batteries with remarkable cycle stability. Our research provides a new idea for the real application of plastic crystal electrolytes in high voltage solid‐state lithium metal batteries. [ABSTRACT FROM AUTHOR]

Purpose: Evaluate the efficacy and safety of triple therapeutic method (Hepatic Aarterial Infusion Chemotherapy—HAIC, lenvatinib and sequential ablation) in the treatment for Advanced Hepatocellular carcinoma (Ad‐HCC). Materials and Methods: From November 2018 to June 2021, data from 150 consecutive Ad‐HCC patients were collected. All patients received HAIC combined with lenvatinib (H‐L group, n = 97) or HAIC combined with lenvatinib and sequential ablation (H‐L‐A group, n = 53). Complications, overall survival (OS), progression‐free survival (PFS) and intrahepatic progression‐free survival (IPFS) were compared between both groups. Results: No significant differences of baseline characteristics were found between groups. The time of median follow‐up was 17.8 months (range, 6.8, 37.6 months). In comparison to the H‐L group, the H‐L‐A group patients showed significantly longer median OS (>30 months vs 13.6 months, respectively; p = 0.010), PFS (12.8 vs. 5.6 months, respectively; p < 0.001), and IPFS (14.6 vs. 6.8 months, respectively; p = 0.002). According to the results from uni‐ and multivariable analyses, we considered α‐fetoprotein and treatment modality as two survival independent prognostic factors. No significant change of the complication incidences was observed between H‐L group and H‐L‐A group (12.4% vs. 11.3%, p = 0.890). Conclusion: Compared to HAIC combined with lenvatinib only, HAIC combined with lenvatinib and sequential ablation was safer and more effective, improving survival outcomes of Ad‐HCC patients. A prospective study will be designed validate the retrospective results. [ABSTRACT FROM AUTHOR]

Obesity and overweight are closely related to cardiovascular mortality. Arterial stiffness is one of the important risks for cardiovascular diseases and is strongly related with the cardiovascular mortality. However, the relationship between obesity and arterial stiffness is still controversial. A cross‐sectional study was performed to examine the relationship of body fat percentage (BFP), an indicator of obesity, with carotid‐femoral pulse wave velocity (cfPWV) in 2603 subjects (aged 58.62 ± 11.27 years, male 71.07%, hypertension 64.89%). All participants were divided into four groups according to the gender and the presence of arterial stiffening based on a value ≥10 m/s of cfPWV(group1 : male with cfPWV <10 m/s, group2 : male with cfPWV ≥10 m/s, group3 : female with cfPWV <10 m/s, group4 : female with cfPWV ≥10 m/s). Body weight, height, waist circumference, blood pressure were measured and clinical biochemical tests were recorded. cfPWV was measured using a non‐invasive automatic device (Complior Analysis, France). BFP were calculated by CUN‐BAE equation. The level of cfPWV was significantly increased with the increasing trend of BFP in both males and females. Stepwise multiple regression analysis revealed that SBP, DBP, HR, Hcy, BFP, FPG were independent associated with cfPWV in females and SBP, eGFR, FPG, BFP, DBP were independent associated with cfPWV in males. In the subgroups stratified by age, BFP was correlated with cfPWV only in females over 60 years old, but not in female those aged under 60 years old and males. In addition to the age and blood pressure, BFP was one of important predictor of arterial stiffening special in females aged over 60 years old. [ABSTRACT FROM AUTHOR]

Premise: Plant biodiversity is threatened, yet many species remain undescribed. It is estimated that >50% of undescribed species have already been collected and are awaiting discovery in herbaria. Robust automatic species identification algorithms using machine learning could accelerate species discovery. Methods: To encourage the development of an automatic species identification algorithm, we submitted our Herbarium 2019 data set to the Fine‐Grained Visual Categorization sub‐competition (FGVC6) hosted on the Kaggle platform. We chose to focus on the flowering plant family Melastomataceae because we have a large collection of imaged herbarium specimens (46,469 specimens representing 683 species) and taxonomic expertise in the family. As is common for herbarium collections, some species in this data set are represented by few specimens and others by many. Results: In less than three months, the FGVC6 Herbarium 2019 Challenge drew 22 teams who entered 254 models for Melastomataceae species identification. The four best algorithms identified species with >88% accuracy. Discussion: The FGVC competitions provide a unique opportunity for computer vision and machine learning experts to address difficult species‐recognition problems. The Herbarium 2019 Challenge brought together a novel combination of collections resources, taxonomic expertise, and collaboration between botanists and computer scientists. [ABSTRACT FROM AUTHOR]

Gear and its transmission are widely used in different transmission systems, and its complicated and changeable condition brings a series of problems to the fault feature extraction and diagnosis. In recent years, deep learning techniques have been gradually applied to feature extraction and pattern recognition, and the features of feature extraction and fault diagnosis in complex working environments have shown certain advantages. This study is based on stacked autoencoder under deep learning model, and improve training network performance by modified activation function. Through the network training before and after the experiment done, and to extract the fault feature data comparison in testing, improving network after activation function to extract fault features showed a greater advantage, can be a very good application in practical fault feature extraction. [ABSTRACT FROM AUTHOR]

Development of LiFePO4 cathode material with high cycle life through Si doping (substituting Si at P sites of LiFePO4) has shown that small changes to the structure of LiFePO4 can significantly affect the performance of Li ion batteries. However, determining the nature of elemental doping and their effects on the electronic and atomic structure of LiFePO4 remains challenging. Here we present x‐ray absorption spectroscopy and x‐ray fluorescence mapping as ideal characterization methods for understanding the effect of Si doped LiFePO4 prepared using a melt‐synthesis process. x‐ray absorption spectra of silicon doped LiFePO4 indicate subtle changes in the local structure surrounding the dopants compared to SiO2 and amorphous glass phases formed as impurities in Si containing undoped samples. The study of Fe and P K‐edges x‐ray absorption spectra illustrates the effect of Si in the LiFePO4 structure in doped and impurity containing samples. Utilizing x‐ray absorption spectroscopy, in conjunction with x‐ray fluorescence mapping, has enabled a better overview of the non‐uniform nature of prepared ingot samples. These results highlight the power of x‐ray absorption spectroscopy as a tool for better understanding the structure of modified LiFePO4 and subsequently designing materials for Li ion batteries. [ABSTRACT FROM AUTHOR]

LiFePO4 has been widely used as a cathode material in lithium ion batteries. However, some impurity phases existing in LiFePO4 have a significant influence on its electrochemical performance. Therefore, detection of the impurity phases is necessary for the manufacturer in order to improve the quality of LiFePO4. In particular, visualization of the impurity and secondary phase distributions immersed in the bulk LiFePO4 crystal can help to understand the origin of the impurity and secondary phases, providing clear guidance towards the synthesis of high purity LiFePO4. The results show that with the combination of Raman and EDS, we are capable of identifying the low melting lithium phosphate phase in LFP ingot. Through micro XRF mapping, more detailed information about the LFP materials after carbon coating are observed. The secondary phases are easily defined due to the high sensitivity of this technology. [ABSTRACT FROM AUTHOR]

New-onset diabetes mellitus (NOD) refers to forms of diabetes mellitus that develop during the therapeutic processes of other diseases such as hypertension. This study has been conducted in a network meta-analysis to compare antihypertensive drugs by identifying both the advantages and disadvantages on NOD by focusing on their respective effect rates. Odd ratios and corresponding 95% confidence intervals or credible intervals were calculated within pairwise and network meta-analysis. A total of 38 articles with 224 140 patients were included to evaluate the preventive effect of hypertension drugs on NOD. From the network meta-analysis it was evident that both angiotensin-converting enzyme inhibitor as well as angiotensin receptor blocker treatments are associated with a lower risk of developing NOD compared with placebo, with ranking probabilities of 79.81% and 72.77%, respectively, while β-blockers and calcium channel blockers may significantly increase the probability of developing NOD (β-blockers: odds ratio, 2.18 [95% credible intervals: 1.36-3.50]; calcium channel blockers: odds ratio, 1.16 [95% credible intervals, 1.05-1.29]). In conclusion, angiotensin receptor blockers have an advantage over the other treatments regarding the NOD. [ABSTRACT FROM AUTHOR]

Li6Zr2O7 nanofibers were synthesized by a simple electrospinning technique. The thermal decomposition behavior, crystal structure, micromorphology, and electrical conductivity of the as-prepared Li6Zr2O7 nanofibers were characterized. The results show that Li6Zr2O7 nanofibers were of pure phase after calcined at 750°C for 1 h. In addition, the as-prepared Li6Zr2O7 nanofibers reveal high conductivity in the measured temperature region, which can be attributed to the huge surface and nanosize effect of the nanofiber electrolyte. Moreover, we provide a general method to improve the conductivity of Li-ion solid electrolyte. [ABSTRACT FROM AUTHOR]

Based on the frame theory and signalling theory, this article uses financial technology (fintech) to analyse online news sentiment and proposes the relevant hypothesis of the influence of news relevance on stock market risk, and conducts an empirical study on the VAR model of the stock return rate by using the news relevance and news sentiment data of the Uqer database. The results show a two‐way Granger causality between news sentiment and stock returns. News relevance is not Granger causality of yield but Granger causality of news sentiment. In addition, combined with the impulse response, variance decomposition, and relevance analysis, it is found that the news relevance degree positively affects news sentiment and then indirectly affects the stock return rate. Our findings indicate that news relevance is an essential variable in analysing online text sentiment and stock market volatility, and online news will increase the volatility of stock market risk. These results contribute to the financialization literature and guide fintech enterprises to balance market risk by combining signalling and frame theory to improve news relevance. [ABSTRACT FROM AUTHOR]