E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analyt...E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.展开更多
Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the ...Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.展开更多
With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent lea...With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.展开更多
To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator a...To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator at temperatures of 380-440℃and strain rates of 0.05-1 s^(−1).The Johnson-Cook model,Hensel-Spittel model,strain-compensated Arrhenius model,and critical fracture strain model were established.Results show that through the evaluation of the models using the correlation coefficient(R)and the average absolute relative error,the strain-compensated Arrhenius model can represent the flow behavior of the alloy more accurately.Shear bands are more pronounced in the as-homogenized specimens,whereas dynamic recrystallization is predominantly observed in as-rolled specimens.Fracture morphology analysis reveals that a mixed fracture mechanism is prevalent in the as-homogenized specimen,whereas a ductile fracture mechanism is predominant in the as-rolled specimen.The processing maps indicate that the unstable region is reduced in the as-rolled specimens compared with that in the as-homogenized specimens.The optimal hot working windows for the as-homogenized and as-rolled specimens are determined as 410-440℃/0.14-1 s^(−1)and 380-400℃/0.05-0.29 s^(−1),respectively.展开更多
Correction to:Nano-Micro Letters(2026)18:10.https://doi.org/10.1007/s40820-025-01852-8 Following publication of the original article[1],the authors reported that the last author’s name was inadvertently misspelled.Th...Correction to:Nano-Micro Letters(2026)18:10.https://doi.org/10.1007/s40820-025-01852-8 Following publication of the original article[1],the authors reported that the last author’s name was inadvertently misspelled.The published version showed“Hongzhen Chen”,whereas the correct spelling should be“Hongzheng Chen”.The correct author name has been provided in this Correction,and the original article[1]has been corrected.展开更多
This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low...This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low-alloy(HSLA)steel in industrially polluted environments.The corrosion process of 650 MPa HSLA steel occurred in two distinct stages:an initial corrosion stage and a stable corrosion stage.During the initial phase,the weight loss rate increased rapidly owing to the instability of the rust layer.Notably,this study demonstrated that 650 MPa HSLA steel exhibited superior corrosion resistance in Cl-containing environments.The formation of a corrosion-product film eventually reduced the weight-loss rate.However,the intrusion of Cl^(-)at increasing concentrations gradually destabilized theα/γ^(*)phases of the rust layer,leading to a looser structure and lower polarization resistance(R_(p)).The application of corrosion big data technology in this study facilitated the validation and analysis of the experimental results,offering new insights into the corrosion mechanisms of HSLA steel in chloride-rich environments.展开更多
The volume change behavior of natural gas hydrate-bearing sediment is essential as it influences settlement,strength,and stiffness,which directly affect the stability of hydrate reservoirs during hydrate extraction or...The volume change behavior of natural gas hydrate-bearing sediment is essential as it influences settlement,strength,and stiffness,which directly affect the stability of hydrate reservoirs during hydrate extraction or in response to environmental changes.The volume change is influenced not only by stress but also by the formation and dissociation of hydrates.This study adopted a customized apparatus for one-dimensional compression tests,allowing independent control of gas pressure and effective stress.Tests were conducted on samples with different hydrate saturations along various temperature-gas pressure-effective stress paths,yielding some conclusions related to compressibility and creep.An unusual phenomenon was observed under low-stress conditions:hydrate formation led to shrinkage rather than expansion.Three potential mechanisms behind this occurrence were discussed.As hydrate saturation increases,the yield stress rises while the compression and swelling indexes remain minimally affected.After hydrate dissociation,the compression curve of hydrate-bearing sediment drops to that of hydrate-free sediment.Once hydrate is formed,the compression curve of hydrate-free sediment gradually approaches that of hydrate-bearing sediment during the subsequent loading.Under low-stress conditions,the creep of both hydrate-free and hydrate-bearing sediments is very weak.However,when stress increases,significantly beyond the yield stress,the creep of both sediments increases significantly,with hydrate-bearing sediment exhibiting much greater creep than hydrate-free sediment.展开更多
Drilling and blasting tunneling is a cyclic process in which tunnel rock undergoes repeated blast loading,affecting its dynamic characteristics,energy evolution,and damage progression.To explore the dynamic mechanical...Drilling and blasting tunneling is a cyclic process in which tunnel rock undergoes repeated blast loading,affecting its dynamic characteristics,energy evolution,and damage progression.To explore the dynamic mechanical properties and damage mechanisms of carbonaceous slate under cyclic impact loads of varying intensities,cyclic dynamic tests are conducted using a triaxial split Hopkinson pressure bar.This study analyzes the stress-strain relationship,energy damage evolution,and macro-to-micro failure characteristics.The results show that peak stress and strain are significantly influenced by impact intensity and the number of impacts.The initial dynamic stress is positively correlated with the impact intensity,but with more impact,the dynamic stress decreases while the peak strain increases.Energy evolution follows a pattern of"slow growthfluctuating growthrapid growth,"with the crack initiation stress and its proportion decreasing.CT and SEM analyses reveal that as the impact intensity increases,failure becomes more chaotic,the fracture volume increases,and the fracture mode shifts from interlayer and intergranular to through-layer and trans-granular fractures.These findings provide an experimental basis for soft rock tunnel stability analysis.展开更多
BACKGROUND Despite societal guidelines recommending targeted screening for Barrett’s esophagus(BE)and esophageal adenocarcinoma(EAC)in individuals with gastroesophageal reflux symptoms(GERS),screening adherence is su...BACKGROUND Despite societal guidelines recommending targeted screening for Barrett’s esophagus(BE)and esophageal adenocarcinoma(EAC)in individuals with gastroesophageal reflux symptoms(GERS),screening adherence is suboptimal.Current screening approaches fail to identify individuals not seeking medical consultation for GERS or whose GERS are managed with‘over-the-counter’(OTC)acid suppressant therapies.AIM To assess patients’self-management and help-seeking behavior for GERS.METHODS This cross-sectional study collected data from the Dutch general population aged 18-75 years between January and April 2023 using a web-based survey.The survey included questions regarding self-management(e.g.,use of acid suppressant therapy with or without prescription)and help-seeking behavior(e.g.,consulting a primary care provider)for GERS.Simple random sampling was performed to select individuals within the target age group.In total,18156 randomly selected individuals were invited to participate.The study protocol was registered in ClinicalTrials.gov(identifier:NCT05689918).RESULTS Of the 18156 invited individuals,3214 participants(17.7%)completed the survey,of which 1572 participants(48.9%)reported GERS.Of these,904 participants(57.5%)had never consulted a primary care provider for these symptoms,of which 331 participants(36.6%)reported taking OTC acid suppressant therapy in the past six months and 100 participants(11.1%)fulfilled the screening criteria for BE and EAC according to the European Society of Gastrointestinal Endoscopy Guideline.CONCLUSION The population fulfilling the screening criteria for BE and EAC is incompletely identified,suggesting potential underutilization of medical consultation.Raising public awareness of GERS as a risk factor for EAC is needed.展开更多
Mesenchymal stem cell-derived extracellular vesicles have emerged as a promising form of regenerative and immunomodulatory therapy;indeed,micro(mi)RNAs contained within mesenchymal stem cell-derived extracellular vesi...Mesenchymal stem cell-derived extracellular vesicles have emerged as a promising form of regenerative and immunomodulatory therapy;indeed,micro(mi)RNAs contained within mesenchymal stem cell-derived extracellular vesicles modulate target gene expression and impact disease-associated pathways.Chronic alcohol consumption leads to neuroinflammation,brain damage,and impaired cognition.Evidence indicates that females are more vulnerable to alcohol-induced damage than males.While mesenchymal stem cell-derived extracellular vesicles have been studied in various neuroinflammatory conditions,their potential to counteract alcohol-induced brain damage remains unclear.In this study,we investigated whether repeated intravenous administration of mesenchymal stem cell-derived extracellular vesicles could ameliorate neuroinflammation and behavioral impairment induced by chronic alcohol consumption in female mice.Mesenchymal stem cell-derived extracellular vesicles diminished the increased binding of a micro-positron emission tomography tracer(^(18)F-FDG)when analyzing whole-brain 3D images and brain coronal sections of ethanol-treated mice.Mesenchymal stem cell-derived extracellular vesicle administration protected against ethanol-induced proinflammatory gene upregulation,cognitive dysfunction,and the conditioned rewarding effects of cocaine.MiRNA sequencing data from mesenchymal stem cell-derived extracellular vesicles revealed the elevated expression of extracellular vesicle-derived miR-483-5p and miR-140-3p in the brains of ethanol-treated female mice following mesenchymal stem cell-derived extracellular vesicle administration.In addition,mesenchymal stem cell-derived extracellular vesicles modulated the expression of pro-inflammatory-related miRNA target genes(e.g.,Socs3,Tnf,Mtor,and Atf6)in the brains of ethanol-treated female mice.These results suggest that mesenchymal stem cell-derived extracellular vesicles could function as a neuroprotective therapy to ameliorate the neuroinflammation,cognitive dysfunction,and conditioned rewarding effects of cocaine associated with chronic alcohol consumption.展开更多
This paper prepared a novel as-cast W-Zr-Ti metallic ESM using high-frequency vacuum induction melting technique.The above ESM performs a typical elastic-brittle material feature and strain rate strengthening behavior...This paper prepared a novel as-cast W-Zr-Ti metallic ESM using high-frequency vacuum induction melting technique.The above ESM performs a typical elastic-brittle material feature and strain rate strengthening behavior.The specimens exhibit violent chemical reaction during the fracture process under the impact loading,and the size distribution of their residual debris follows Rosin-Rammler model.The dynamic fracture toughness is obtained by the fitting of debris length scale,approximately 1.87 MPa·m~(1/2).Microstructure observation on residual debris indicates that the failure process is determined by primary crack propagation under quasi-static compression,while it is affected by multiple cracks propagation in both particle and matrix in the case of dynamic impact.Impact test demonstrates that the novel energetic fragment performs brilliant penetration and combustion effect behind the front target,leading to the effective ignition of fuel tank.For the brittleness of as-cast W-ZrTi ESM,further study conducted bond-based peridynamic(BB-PD)C++computational code to simulate its fracture behavior during penetration.The BB-PD method successfully captured the fracture process and debris cloud formation of the energetic fragment.This paper explores a novel as-cast metallic ESM,and provides an available numerical avenue to the simulation of brittle energetic fragment.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
基金The authors thank to the deanship of scientific research at Shaqra University for funding this research work through the Project Number(SU-ANN-2023017).
文摘E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures.This article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments.The proposed e-learning analytics system includes a new deep forest model.It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks.The developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final exam.Experiments have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 students.Our proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous studies.That allows academic advisors to support expected failed students significantly and improve their academic level at the right time.Consequently,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.
基金Under the auspices of the National Natural Science Foundation of China(No.41571144)。
文摘Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.
文摘With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.
文摘To investigate the influence of Al-Zn-Mg-Cu alloy with as-homogenized and as-rolled initial microstructures on the tensile flow behavior,isothermal tensile tests were conducted on a GLEEBLE-3500 isothermal simulator at temperatures of 380-440℃and strain rates of 0.05-1 s^(−1).The Johnson-Cook model,Hensel-Spittel model,strain-compensated Arrhenius model,and critical fracture strain model were established.Results show that through the evaluation of the models using the correlation coefficient(R)and the average absolute relative error,the strain-compensated Arrhenius model can represent the flow behavior of the alloy more accurately.Shear bands are more pronounced in the as-homogenized specimens,whereas dynamic recrystallization is predominantly observed in as-rolled specimens.Fracture morphology analysis reveals that a mixed fracture mechanism is prevalent in the as-homogenized specimen,whereas a ductile fracture mechanism is predominant in the as-rolled specimen.The processing maps indicate that the unstable region is reduced in the as-rolled specimens compared with that in the as-homogenized specimens.The optimal hot working windows for the as-homogenized and as-rolled specimens are determined as 410-440℃/0.14-1 s^(−1)and 380-400℃/0.05-0.29 s^(−1),respectively.
文摘Correction to:Nano-Micro Letters(2026)18:10.https://doi.org/10.1007/s40820-025-01852-8 Following publication of the original article[1],the authors reported that the last author’s name was inadvertently misspelled.The published version showed“Hongzhen Chen”,whereas the correct spelling should be“Hongzheng Chen”.The correct author name has been provided in this Correction,and the original article[1]has been corrected.
基金financially supported by the National Natural Science Foundation of China(Nos.52104319 and 52374323)。
文摘This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low-alloy(HSLA)steel in industrially polluted environments.The corrosion process of 650 MPa HSLA steel occurred in two distinct stages:an initial corrosion stage and a stable corrosion stage.During the initial phase,the weight loss rate increased rapidly owing to the instability of the rust layer.Notably,this study demonstrated that 650 MPa HSLA steel exhibited superior corrosion resistance in Cl-containing environments.The formation of a corrosion-product film eventually reduced the weight-loss rate.However,the intrusion of Cl^(-)at increasing concentrations gradually destabilized theα/γ^(*)phases of the rust layer,leading to a looser structure and lower polarization resistance(R_(p)).The application of corrosion big data technology in this study facilitated the validation and analysis of the experimental results,offering new insights into the corrosion mechanisms of HSLA steel in chloride-rich environments.
基金supported by the National Natural Science Foundation of China(Grant No.42171135)the Science and Technology Program of CNOOC Research Institute(Grant No.2023OTKK03)the“CUG Scholar”Scientific Research Funds at China University of Geosciences(Project No.2022098).
文摘The volume change behavior of natural gas hydrate-bearing sediment is essential as it influences settlement,strength,and stiffness,which directly affect the stability of hydrate reservoirs during hydrate extraction or in response to environmental changes.The volume change is influenced not only by stress but also by the formation and dissociation of hydrates.This study adopted a customized apparatus for one-dimensional compression tests,allowing independent control of gas pressure and effective stress.Tests were conducted on samples with different hydrate saturations along various temperature-gas pressure-effective stress paths,yielding some conclusions related to compressibility and creep.An unusual phenomenon was observed under low-stress conditions:hydrate formation led to shrinkage rather than expansion.Three potential mechanisms behind this occurrence were discussed.As hydrate saturation increases,the yield stress rises while the compression and swelling indexes remain minimally affected.After hydrate dissociation,the compression curve of hydrate-bearing sediment drops to that of hydrate-free sediment.Once hydrate is formed,the compression curve of hydrate-free sediment gradually approaches that of hydrate-bearing sediment during the subsequent loading.Under low-stress conditions,the creep of both hydrate-free and hydrate-bearing sediments is very weak.However,when stress increases,significantly beyond the yield stress,the creep of both sediments increases significantly,with hydrate-bearing sediment exhibiting much greater creep than hydrate-free sediment.
基金support from the Joint Funds of the National Natural Science Foundation of China(Grant No.U23A2060)the National Natural Science Foundation of China(Grant Nos.42177143 and 52474150).
文摘Drilling and blasting tunneling is a cyclic process in which tunnel rock undergoes repeated blast loading,affecting its dynamic characteristics,energy evolution,and damage progression.To explore the dynamic mechanical properties and damage mechanisms of carbonaceous slate under cyclic impact loads of varying intensities,cyclic dynamic tests are conducted using a triaxial split Hopkinson pressure bar.This study analyzes the stress-strain relationship,energy damage evolution,and macro-to-micro failure characteristics.The results show that peak stress and strain are significantly influenced by impact intensity and the number of impacts.The initial dynamic stress is positively correlated with the impact intensity,but with more impact,the dynamic stress decreases while the peak strain increases.Energy evolution follows a pattern of"slow growthfluctuating growthrapid growth,"with the crack initiation stress and its proportion decreasing.CT and SEM analyses reveal that as the impact intensity increases,failure becomes more chaotic,the fracture volume increases,and the fracture mode shifts from interlayer and intergranular to through-layer and trans-granular fractures.These findings provide an experimental basis for soft rock tunnel stability analysis.
文摘BACKGROUND Despite societal guidelines recommending targeted screening for Barrett’s esophagus(BE)and esophageal adenocarcinoma(EAC)in individuals with gastroesophageal reflux symptoms(GERS),screening adherence is suboptimal.Current screening approaches fail to identify individuals not seeking medical consultation for GERS or whose GERS are managed with‘over-the-counter’(OTC)acid suppressant therapies.AIM To assess patients’self-management and help-seeking behavior for GERS.METHODS This cross-sectional study collected data from the Dutch general population aged 18-75 years between January and April 2023 using a web-based survey.The survey included questions regarding self-management(e.g.,use of acid suppressant therapy with or without prescription)and help-seeking behavior(e.g.,consulting a primary care provider)for GERS.Simple random sampling was performed to select individuals within the target age group.In total,18156 randomly selected individuals were invited to participate.The study protocol was registered in ClinicalTrials.gov(identifier:NCT05689918).RESULTS Of the 18156 invited individuals,3214 participants(17.7%)completed the survey,of which 1572 participants(48.9%)reported GERS.Of these,904 participants(57.5%)had never consulted a primary care provider for these symptoms,of which 331 participants(36.6%)reported taking OTC acid suppressant therapy in the past six months and 100 participants(11.1%)fulfilled the screening criteria for BE and EAC according to the European Society of Gastrointestinal Endoscopy Guideline.CONCLUSION The population fulfilling the screening criteria for BE and EAC is incompletely identified,suggesting potential underutilization of medical consultation.Raising public awareness of GERS as a risk factor for EAC is needed.
基金supported by the Spanish Ministry of Health‐Plan Nacional sobre Drogas(2023‐I024)the the Ministry of Science,Innovation and Universities/State ResearchAgency/10.13039/501100011033(PID2023-146865OB-I00)+2 种基金Generalitat Valenciana(CIAICO/2021/203)the Primary Addiction Care Research Network(RD21/0009/0005)FEDER Funds,GVA.
文摘Mesenchymal stem cell-derived extracellular vesicles have emerged as a promising form of regenerative and immunomodulatory therapy;indeed,micro(mi)RNAs contained within mesenchymal stem cell-derived extracellular vesicles modulate target gene expression and impact disease-associated pathways.Chronic alcohol consumption leads to neuroinflammation,brain damage,and impaired cognition.Evidence indicates that females are more vulnerable to alcohol-induced damage than males.While mesenchymal stem cell-derived extracellular vesicles have been studied in various neuroinflammatory conditions,their potential to counteract alcohol-induced brain damage remains unclear.In this study,we investigated whether repeated intravenous administration of mesenchymal stem cell-derived extracellular vesicles could ameliorate neuroinflammation and behavioral impairment induced by chronic alcohol consumption in female mice.Mesenchymal stem cell-derived extracellular vesicles diminished the increased binding of a micro-positron emission tomography tracer(^(18)F-FDG)when analyzing whole-brain 3D images and brain coronal sections of ethanol-treated mice.Mesenchymal stem cell-derived extracellular vesicle administration protected against ethanol-induced proinflammatory gene upregulation,cognitive dysfunction,and the conditioned rewarding effects of cocaine.MiRNA sequencing data from mesenchymal stem cell-derived extracellular vesicles revealed the elevated expression of extracellular vesicle-derived miR-483-5p and miR-140-3p in the brains of ethanol-treated female mice following mesenchymal stem cell-derived extracellular vesicle administration.In addition,mesenchymal stem cell-derived extracellular vesicles modulated the expression of pro-inflammatory-related miRNA target genes(e.g.,Socs3,Tnf,Mtor,and Atf6)in the brains of ethanol-treated female mice.These results suggest that mesenchymal stem cell-derived extracellular vesicles could function as a neuroprotective therapy to ameliorate the neuroinflammation,cognitive dysfunction,and conditioned rewarding effects of cocaine associated with chronic alcohol consumption.
文摘This paper prepared a novel as-cast W-Zr-Ti metallic ESM using high-frequency vacuum induction melting technique.The above ESM performs a typical elastic-brittle material feature and strain rate strengthening behavior.The specimens exhibit violent chemical reaction during the fracture process under the impact loading,and the size distribution of their residual debris follows Rosin-Rammler model.The dynamic fracture toughness is obtained by the fitting of debris length scale,approximately 1.87 MPa·m~(1/2).Microstructure observation on residual debris indicates that the failure process is determined by primary crack propagation under quasi-static compression,while it is affected by multiple cracks propagation in both particle and matrix in the case of dynamic impact.Impact test demonstrates that the novel energetic fragment performs brilliant penetration and combustion effect behind the front target,leading to the effective ignition of fuel tank.For the brittleness of as-cast W-ZrTi ESM,further study conducted bond-based peridynamic(BB-PD)C++computational code to simulate its fracture behavior during penetration.The BB-PD method successfully captured the fracture process and debris cloud formation of the energetic fragment.This paper explores a novel as-cast metallic ESM,and provides an available numerical avenue to the simulation of brittle energetic fragment.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
