The flexibility evaluation of OneShape files.

<p>Compared with ProTaper files (the control), OneShape files presented significantly lower bending values at deflections of 45° (<i>P</i> < .05), 60° (<i>P</i> < .05) and 75° (<i>P</i> < .01) (A). OneShape files presented a higher NCF in both 60° and 90° canals than the control (P < .01). No significant difference of NCF was found between OneShape and ProTaper files in 30° canals. (B).</p

SEM images of the fracture surfaces.

<p>The crack initiation areas were pointed by the arrows and the fast fracture zones were surrounded by black dots.</p

The force generated by OneShape and ProTaper instruments during simulated canal preparation.

<p>In group of 30° canals, the negative force generated by OneShape was significantly higher at D3 and D2 (<i>P</i> < .05) (A). In group of 60°, the negative force generated by OneShape was significantly higher at D2 (<i>P</i> < .05), D1 and D0 (<i>P</i> < .01), while the positive force generated by ProTaper F2 was significantly higher at D1 and D0 (<i>P</i> < .01) (B). In group of 90°, the negative force generated by OneShape was significantly higher at D4 and D3 (<i>P</i> < .01), while the positive force generated by ProTaper F2 was significantly higher at D4 and D3 (<i>P</i> < .01) (C). (*: <i>P</i> < .05, **: <i>P</i> < .01)</p

Formation and Characterization of Carbon-Radical Precursors in Char Steam Gasification

Highly reactive radicals play an important role in high-temperature gasification processes. However, the effect of radicals on gasification has not been systematically investigated. In the present study, the formation of carbon-radical precursors using atomic radicals such as OH, O, and H and molecules such as H₂ and O₂ was characterized, and the effect of the precursors on the adsorption step of steam char gasification was studied using quantum chemistry methods. The results revealed that the radicals can be chemisorbed exothermically on char active sites, and the following order of reactivity was observed: O > H₂ > H > OH > O₂. Moreover, hydrogen bonds are formed between steam molecules and carbon-radical complexes. Steam molecule adsorption onto carbon-O and carbon-OH complexes is easier than adsorption onto clean carbon surfaces. Alternatively, adsorption on carbon-O₂, carbon-H₂, and carbon-H complexes is at the same level with that of clean carbon surfaces; thus, OH and O radicals accelerate the physical adsorption of steam onto the char surface, H radical and O₂ and H₂ molecules do not have a significant effect on adsorption

Synthesis of Boron-Fused 1,4-Dithiin via Cobalt-Mediated Disulfuration of Alkyne at the <i>o</i>‑Carborane-9,12-dithiolate Unit

We present the first synthesis and characterization of a series of boron-fused 1,4-dithiin compounds through the reactions of newly established boron-substituted 16<i>e</i> half-sandwich complex Cp*Co­(9,12-S₂C₂B₁₀H₁₀) with alkynes. The generated C₂S₂B₂ ring in these 1,4-dithiin species is a stable structural motif with electron-negative sulfur atoms, as evidenced by theoretical calculation and its solid-state self-assembly. Single-crystal X-ray analysis indicates that C_carb–H···S hydrogen bonding is involved in the self-assemblies of these compounds, which serves as a compatible interaction with stronger C_carb–H···π, C_carb–H···O, or C_carb–H···F hydrogen bonding. All new compounds are characterized by NMR, mass spectroscopy, and X-ray structural analysis

1,1,1,3,3,3-Hexafluoroisopropanol (HFIP) Promoted N‑Alkylation of Quinazolinones through Nucleophilic Substitution of Benzyl Alcohols

We describe here a protocol for alkylation of quinazolinones with a series of primary or secondary alcohols under metal-free conditions. A class of quinazolinone derivatives were obtained in 31%–97% yield with good functional group tolerance. This protocol provides chemists a direct and effective way to obtain bioactive quinazolinone derivatives

Image1_Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study.jpg

Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared.Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment.Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.</p

Image2_Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study.PNG

Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared.Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment.Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.</p

Image3_Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study.PNG

Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared.Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment.Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.</p

Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace

<div><p>“The Belt and Road” initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along “the Belt and Road” are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along “the Belt and Road”. This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of “the Belt and Road”, show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city.</p></div