Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common form of sleep breathing disorder characterized by apnea and hypopnea resulting from recurrent upper airway obstruction during sleep.This leads to intermitten...Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common form of sleep breathing disorder characterized by apnea and hypopnea resulting from recurrent upper airway obstruction during sleep.This leads to intermittent hypoxia in the brain and disruptions in sleep architecture,ultimately causing cognitive impairment.In OSAHS patients,cognitive dysfunction manifests mainly as diminished attention,memory,and executive function.These effects impact an individual’s daily and social abilities,significantly reducing their quality of life.This article primarily reviews four aspects of OSAHS patients’cognitive function,namely,characteristics,pathogenesis,assessment tools,influencing factors,and heterogeneity,to provide a theoretical basis for healthcare professionals to identify high-risk groups for cognitive impairment among OSAHS patients at an early stage and to construct a more objective and feasible intervention program to further prevent the occurrence and development of dementia.展开更多
The flocculation behavior of carbon black (CB)-filled isoprene rubber (IR) nanocomposites was systematically investigated under both dynamic and static conditions to unravel the distinct mechanisms governing filler ne...The flocculation behavior of carbon black (CB)-filled isoprene rubber (IR) nanocomposites was systematically investigated under both dynamic and static conditions to unravel the distinct mechanisms governing filler network evolution.Under dynamic conditions,small oscillatory shear strains (0.1%) significantly enhanced filler particle motion,leading to pronounced agglomeration and a flocculation degree of about 4.3MPa at 145℃.In contrast,static flocculation exhibited a fundamentally different mechanism dominated by polymer chain dynamics,which is driven mainly by thermal activation.Radial distribution function (RDF) analysis of transmission electron microscopy (TEM) images revealed a slight decrease (2 nm) in the interparticle distance peak after static annealing at 100℃ for 7 h,indicating localized motion of CB particles.However,the overall filler network remained stable,with no significant agglomeration observed.The increase in bound rubber content from about 23% to 28% with rising temperature further confirmed the dominant role of polymer chain adsorption and interfacial reinforcement in static flocculation.These findings highlight the critical influence of external strain on filler network formation and provide new insights into the polymer-dominated mechanism of static flocculation.The results offer practical guidance for optimizing the storage and processing of rubber nanocomposites,particularly in applications where static flocculation during prolonged storage is a concern.展开更多
The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material,type of fillers,and the processing ways.Carbon black-filled rubber composites(CRC)exemplify ...The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material,type of fillers,and the processing ways.Carbon black-filled rubber composites(CRC)exemplify this,playing a crucial role in various industries.However,due to the complex interplay between these factors and the resulting properties,a simple yet accurate model to predict the mechanical properties of CRC,considering different rubbers,fillers,and processing techniques,is highly desired.This study aims to predict the dispersion of fillers in CRC and forecast the resultant mechanical properties of CRC by leveraging machine learning.We selected various rubbers and carbon black fillers,conducted mixing and vulcanizing,and subsequently measured filler dispersion and tensile performance.Based on 215 experimental data points,we evaluated the performance of different machine learning models.Our findings indicate that the manually designed deep neural network(DNN)models achieved superior results,exhibiting the highest coefficient of determination(R^(2))values(>0.95).Shapley additive explanations(SHAP)analysis of the DNN models revealed the intricate relationship between the properties of CRC and process parameters.Moreover,based on the robust predictive capabilities of the DNN models,we can recommend or optimize CRC fabrication process.This work provides valuable insights for employing machine learning in predicting polymer composite material properties and optimizing the fabrication of high-performance CRC.展开更多
基金supported by the 2023 Jinzhou Medical University Education and Teaching Research and Reform Project(YB2023004).
文摘Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common form of sleep breathing disorder characterized by apnea and hypopnea resulting from recurrent upper airway obstruction during sleep.This leads to intermittent hypoxia in the brain and disruptions in sleep architecture,ultimately causing cognitive impairment.In OSAHS patients,cognitive dysfunction manifests mainly as diminished attention,memory,and executive function.These effects impact an individual’s daily and social abilities,significantly reducing their quality of life.This article primarily reviews four aspects of OSAHS patients’cognitive function,namely,characteristics,pathogenesis,assessment tools,influencing factors,and heterogeneity,to provide a theoretical basis for healthcare professionals to identify high-risk groups for cognitive impairment among OSAHS patients at an early stage and to construct a more objective and feasible intervention program to further prevent the occurrence and development of dementia.
基金supported by the National Natural Science Foundation of China(No.52293471)National Key R&D Program of China(No.2022YFB3707303).
文摘The flocculation behavior of carbon black (CB)-filled isoprene rubber (IR) nanocomposites was systematically investigated under both dynamic and static conditions to unravel the distinct mechanisms governing filler network evolution.Under dynamic conditions,small oscillatory shear strains (0.1%) significantly enhanced filler particle motion,leading to pronounced agglomeration and a flocculation degree of about 4.3MPa at 145℃.In contrast,static flocculation exhibited a fundamentally different mechanism dominated by polymer chain dynamics,which is driven mainly by thermal activation.Radial distribution function (RDF) analysis of transmission electron microscopy (TEM) images revealed a slight decrease (2 nm) in the interparticle distance peak after static annealing at 100℃ for 7 h,indicating localized motion of CB particles.However,the overall filler network remained stable,with no significant agglomeration observed.The increase in bound rubber content from about 23% to 28% with rising temperature further confirmed the dominant role of polymer chain adsorption and interfacial reinforcement in static flocculation.These findings highlight the critical influence of external strain on filler network formation and provide new insights into the polymer-dominated mechanism of static flocculation.The results offer practical guidance for optimizing the storage and processing of rubber nanocomposites,particularly in applications where static flocculation during prolonged storage is a concern.
基金supported by the National Key R&D Program of China(No.2022YFB3707303)the National Natural Science Foundation of China(No.52293471).
文摘The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material,type of fillers,and the processing ways.Carbon black-filled rubber composites(CRC)exemplify this,playing a crucial role in various industries.However,due to the complex interplay between these factors and the resulting properties,a simple yet accurate model to predict the mechanical properties of CRC,considering different rubbers,fillers,and processing techniques,is highly desired.This study aims to predict the dispersion of fillers in CRC and forecast the resultant mechanical properties of CRC by leveraging machine learning.We selected various rubbers and carbon black fillers,conducted mixing and vulcanizing,and subsequently measured filler dispersion and tensile performance.Based on 215 experimental data points,we evaluated the performance of different machine learning models.Our findings indicate that the manually designed deep neural network(DNN)models achieved superior results,exhibiting the highest coefficient of determination(R^(2))values(>0.95).Shapley additive explanations(SHAP)analysis of the DNN models revealed the intricate relationship between the properties of CRC and process parameters.Moreover,based on the robust predictive capabilities of the DNN models,we can recommend or optimize CRC fabrication process.This work provides valuable insights for employing machine learning in predicting polymer composite material properties and optimizing the fabrication of high-performance CRC.
