This paper presents an efficient model reduction technique for linear time-varying systems based on shifted Legendre polynomials.The approach constructs approximate low-rank decomposition factors of finite-time Gramia...This paper presents an efficient model reduction technique for linear time-varying systems based on shifted Legendre polynomials.The approach constructs approximate low-rank decomposition factors of finite-time Gramians directly from the expansion coefficients of impulse responses.Leveraging these factors,we develop two model reduction algorithms that integrate the low-rank square root method with dominant subspace projection.Our method is computationally efficient and flexible,requiring only a few matrix-vector operations and a singular value decomposition of a low-dimensional matrix,thereby avoiding the need to solve differential Lyapunov equations.Numerical experiments confirm the effectiveness of the proposed approach.展开更多
With the evolution of next-generation network technologies,the complexity of network management has significantly increased,and the means of network attacks are diversified,bringing new challenges to network traffic c...With the evolution of next-generation network technologies,the complexity of network management has significantly increased,and the means of network attacks are diversified,bringing new challenges to network traffic classification.This paper presents a general AIdriven network traffic classification workflow and elaborates on a traffic data and feature engineering framework.Most importantly,it analyzes the concept and causes of data distribution shifts in ne twork traffic,proposing detection methods and countermeasures.Experimental results on real traffic collected at different time intervals show that application evolution can induce data distribution shifts,which in turn lead to a noticeable degradation in traffic classification performance.Comparative drift detection experiments further confirm that such shifts are more evident over long-term intervals,while short-term traffic remains relatively stable.These findings demonstrate the necessity of incorporating drift-aware mechanisms into AI-driven network traffic classification systems.展开更多
Northeast China(NEC),a critical agricultural and ecological zone,has experienced intensified hydrological variability under global warming,with cascading impacts on food security and ecosystem resilience.This study ut...Northeast China(NEC),a critical agricultural and ecological zone,has experienced intensified hydrological variability under global warming,with cascading impacts on food security and ecosystem resilience.This study utilized observational data and two new generation reanalysis products(i.e.,the fifth major global reanalysis produced by ECMWF(ERA5)and the Japanese Reanalysis for Three Quarters of a Century(JRA-3Q))to investigate the shift changes in precipitation in NEC around 2000 and associated water vapor transport.The analysis identified a pivotal interdecadal shift in 1998/99,transitioning from moderate increases(17.5 mm/10 yr during 1980-1998)to accelerated but more variable precipitation growth(85.4 mm/10 yr post-1999).While the mean precipitation during the post-shift period decreased,enhanced anticyclonic circulation amplified moisture divergence over continental NEC,redirecting vapor flux toward coastal regions.Crucially,trajectory analysis demonstrated regime-dependent moisture sourcing:midlatitude westerlies dominated during wet extremes(44% of trajectories in 1998),whereas East Asian monsoon flows prevailed in drought years(36% of trajectories in 2007).The post-1998 period exhibited increased reliance on localized recycling(45%of mid-tropospheric trajectories),reflecting weakened monsoonal inflow.These findings highlight NEC’s growing vulnerability to competing moisture pathways and atmospheric blocking-a dual mechanism that explains rising extremes despite declining mean precipitation.By reconciling dataset discrepancies(ERA5 vs.JRA-3Q trends)and elucidating circulation-precipitation linkages,this work provides actionable insights for climate-resilient agriculture in NEC’s water-stressed ecosystems.展开更多
Objective:To investigate the distribution of Traditional Chinese Medicine(TCM)constitution among night-shift nurses at a public tertiary-level Class A hospital,thereby identifying new approaches for developing effecti...Objective:To investigate the distribution of Traditional Chinese Medicine(TCM)constitution among night-shift nurses at a public tertiary-level Class A hospital,thereby identifying new approaches for developing effective interventions to enhance their health status.Methods:A total of 601 nurses employed at a public tertiary-level Class A hospital from August 2024 to August 2025 participated in the survey.Analysis was conducted using the Traditional Chinese Medicine Constitution Questionnaire.Results:The most prevalent TCM constitution types among night-shift nurses were Yang Deficiency Constitution(50.4%),Yin Deficiency Constitution(49.8%),Qi Deficiency Constitution(45.4%),and Phlegm-Dampness Constitution(45.1%).Conclusion:A high proportion of clinical night-shift nursing staff exhibit imbalanced constitution types,severely impacting their physical and mental health.Nursing administrators should implement targeted TCM constitution-based health maintenance and regulation to improve the health status of nursing personnel.展开更多
As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpi...As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpine plant distribution and their profound impact on fragile ecosystems have become a focus of ecological research and conservation efforts,with increasing urgency.Meconopsis,a typical representative of Chinese alpine plants,exhibits diverse adaptability,making it an ideal model for studying how alpine species respond to extreme environmental changes.A lack of comprehensive genus-level analyses may hinder the development of long-term conservation and management strategies.Given the genus's ecological importance,vulnerability,and the risk of trait homogenization in genus-level modeling,there is an urgent need to assess its future distribution patterns,migration trends,and adaptive mechanisms based on habitat classification.In this study,we employed the Maxent model,integrating multidimensional environmental variables,to develop genus-level models and representative habitat-based models(forest,meadows,and periglacial).Results indicate a northwestward expansion and southeastward contraction of suitable habitats under future climate scenarios,with migration patterns in latitude and elevation showing stage-specific characteristics.Key environmental factors varied across models but were mostly associated with seasonal growth traits and microhabitat conditions,highlighting both the universal ecological requirements and niche differentiation within Meconopsis.Based on these findings,we propose a dynamic conservation strategy framework informed by stage-specific responses and habitat differences.Future efforts should focus on incorporating alpine-specific environmental variables and optimizing specimen collection strategies to enhance model performance and support landscape planning and biodiversity conservation.展开更多
Heat-induced emission peak shift(HIEPS),encompassing both redshift and blueshift,remains mechanistically unresolved in phosphor materials.Using state-of-the-art first-principles calculations of M_(2)SiO_(4):Eu^(2+)(M=...Heat-induced emission peak shift(HIEPS),encompassing both redshift and blueshift,remains mechanistically unresolved in phosphor materials.Using state-of-the-art first-principles calculations of M_(2)SiO_(4):Eu^(2+)(M=Sr,Ba,Ca),we reveal that conventional thermal expansion theory cannot adequately explain these phenomena.Instead,our frozen phonon analysis identifies local electron-phonon coupling as the dominant mechanism,where anisotropic thermal vibrations selectively distort the asymmetric Eu-5d potential well that arises from the dopant’s coordination environment.This distortion manifests through the temperature-sensitiveΔ_(f−d) parameter governing the 5d→4f transition energy,directly controlling spectral shifts.Our findings establish a universal framework for HIEPS in rare-earth phosphors and enable a Δ_(f−d)-guided strategy for designing thermally stable phosphors.展开更多
Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established ba...Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established based on data of meteorological stations laid the foundation for the discipline but were unable to meet the dynamic demands of climate change,spatial heterogeneity,and individual experience.Global climate change is reshaping the landscape of tourism comfort and driving the assessment to shift towards future risk prediction.Downscaling technology becomes the key to connecting global scenarios and local assessments.Remote sensing and Internet of Things technologies have constructed a"sky-ground"collaborative sensing network,achieving a revolution in data acquisition.Artificial intelligence and big data analysis serve as the intelligent core to drive research from description to prediction.The new paradigm has significant potential in improving assessment accuracy and timeliness,but also faces challenges such as data integration,model interpretability,interdisciplinary integration,and ethical privacy.In the future,it is needed to develop interpretable AI,construct climate digital twins,and promote full-chain coupling research.This transformation is not merely an upgrade of methods,but a fundamental shift in the study of philosophy from an"environment-centered"perspective to an"experience-centered"one,providing key scientific support for sustainable tourism.展开更多
Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1...Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1].However,the stress caused by fast-paced life often leads to sleep deprivation(SD).SD is strongly associated with damage to the auditory system[2,3].Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common sleep disorder.Clinical observations indicate that some patients with OSAHS experience persistent hearing loss accompanied by tinnitus and other symptoms[4].More than 61.8%of patients with sudden deafness experienced SD[5].展开更多
Academic journals,consulting firms,and mainstream media have often published“predictions”about the future develop-ment of medical and health care.These publications often emphasize the potential of cutting-edge scie...Academic journals,consulting firms,and mainstream media have often published“predictions”about the future develop-ment of medical and health care.These publications often emphasize the potential of cutting-edge scientific or technical breakthroughs.Health Care Science looks at the problem from another perspective.We focus on how these changes enter the health system,how they operate in the real world,and how they reshape the organization and governance of medical services.At the beginning of 2026,we envision the following three major shifts that will reshape healthcare.展开更多
At the start of China’s 15th Five-Year Plan period(2026–2030),signals from March’s Two Sessions point to a subtle but notable change in direction.Policymakers are moving beyond an emphasis on headline growth figure...At the start of China’s 15th Five-Year Plan period(2026–2030),signals from March’s Two Sessions point to a subtle but notable change in direction.Policymakers are moving beyond an emphasis on headline growth figures and are instead concentrating on building a more resilient economic foundation in a volatile global environment.展开更多
The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculati...The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.展开更多
Owing to a lack of gaugemeter and the variety of steel grades and standards in some plate mills, the longand short-term self-learning models of rolling force based on gauge soft-measuring with high precision were brou...Owing to a lack of gaugemeter and the variety of steel grades and standards in some plate mills, the longand short-term self-learning models of rolling force based on gauge soft-measuring with high precision were brought up. The soft-measuring method and target value locked method were used in these models to confirm the actual exit gauge of passes, and thick layer division and exponential smoothing method were used to dispose the deformation resistance parameter, which could be calculated from the actual data of the rolling process. The correlative mathematical methods can also be adapted to self-learning with gaugemeter. The models were applied to the process control system of AGC （automatic gauge control） reconstruction on 2800 mm finishing mill of Anyang steel and favorable effect was obtained.展开更多
On the basis of a simulated bright continuous annealing experimental machine, a process control model for heating system was built. The heating model was simplified and self-learning parameters were normalized to enha...On the basis of a simulated bright continuous annealing experimental machine, a process control model for heating system was built. The heating model was simplified and self-learning parameters were normalized to enhance the precision of temperature control. By means of the division of temperature layers and the exponential smoothing disposal of the annealing experimental data, the self-learning of the heating model was carried out. Through exponentially smoothing the deviation of self-learning parameters of the heated phase in heating process, dynamic modifications of self-learning parameters and heating electric current were carried out, and the precision of temperature control was confirmed. The application indicated that the process control model for the heating system can control temperature with high precision, and the deviation can be controlled within 8 ℃.展开更多
In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples fr...In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.展开更多
To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time wen...To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.展开更多
This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the ...This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.展开更多
Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control p...Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.展开更多
A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced th...A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.展开更多
This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globall...This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.展开更多
文摘This paper presents an efficient model reduction technique for linear time-varying systems based on shifted Legendre polynomials.The approach constructs approximate low-rank decomposition factors of finite-time Gramians directly from the expansion coefficients of impulse responses.Leveraging these factors,we develop two model reduction algorithms that integrate the low-rank square root method with dominant subspace projection.Our method is computationally efficient and flexible,requiring only a few matrix-vector operations and a singular value decomposition of a low-dimensional matrix,thereby avoiding the need to solve differential Lyapunov equations.Numerical experiments confirm the effectiveness of the proposed approach.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.HC-CN-20220607009。
文摘With the evolution of next-generation network technologies,the complexity of network management has significantly increased,and the means of network attacks are diversified,bringing new challenges to network traffic classification.This paper presents a general AIdriven network traffic classification workflow and elaborates on a traffic data and feature engineering framework.Most importantly,it analyzes the concept and causes of data distribution shifts in ne twork traffic,proposing detection methods and countermeasures.Experimental results on real traffic collected at different time intervals show that application evolution can induce data distribution shifts,which in turn lead to a noticeable degradation in traffic classification performance.Comparative drift detection experiments further confirm that such shifts are more evident over long-term intervals,while short-term traffic remains relatively stable.These findings demonstrate the necessity of incorporating drift-aware mechanisms into AI-driven network traffic classification systems.
基金supported by the National Natural Science Foundation of China[grant numbers 42275185 and 42205032]the Fundamental Research Funds for the Central Universities[grant number B250201118]。
文摘Northeast China(NEC),a critical agricultural and ecological zone,has experienced intensified hydrological variability under global warming,with cascading impacts on food security and ecosystem resilience.This study utilized observational data and two new generation reanalysis products(i.e.,the fifth major global reanalysis produced by ECMWF(ERA5)and the Japanese Reanalysis for Three Quarters of a Century(JRA-3Q))to investigate the shift changes in precipitation in NEC around 2000 and associated water vapor transport.The analysis identified a pivotal interdecadal shift in 1998/99,transitioning from moderate increases(17.5 mm/10 yr during 1980-1998)to accelerated but more variable precipitation growth(85.4 mm/10 yr post-1999).While the mean precipitation during the post-shift period decreased,enhanced anticyclonic circulation amplified moisture divergence over continental NEC,redirecting vapor flux toward coastal regions.Crucially,trajectory analysis demonstrated regime-dependent moisture sourcing:midlatitude westerlies dominated during wet extremes(44% of trajectories in 1998),whereas East Asian monsoon flows prevailed in drought years(36% of trajectories in 2007).The post-1998 period exhibited increased reliance on localized recycling(45%of mid-tropospheric trajectories),reflecting weakened monsoonal inflow.These findings highlight NEC’s growing vulnerability to competing moisture pathways and atmospheric blocking-a dual mechanism that explains rising extremes despite declining mean precipitation.By reconciling dataset discrepancies(ERA5 vs.JRA-3Q trends)and elucidating circulation-precipitation linkages,this work provides actionable insights for climate-resilient agriculture in NEC’s water-stressed ecosystems.
文摘Objective:To investigate the distribution of Traditional Chinese Medicine(TCM)constitution among night-shift nurses at a public tertiary-level Class A hospital,thereby identifying new approaches for developing effective interventions to enhance their health status.Methods:A total of 601 nurses employed at a public tertiary-level Class A hospital from August 2024 to August 2025 participated in the survey.Analysis was conducted using the Traditional Chinese Medicine Constitution Questionnaire.Results:The most prevalent TCM constitution types among night-shift nurses were Yang Deficiency Constitution(50.4%),Yin Deficiency Constitution(49.8%),Qi Deficiency Constitution(45.4%),and Phlegm-Dampness Constitution(45.1%).Conclusion:A high proportion of clinical night-shift nursing staff exhibit imbalanced constitution types,severely impacting their physical and mental health.Nursing administrators should implement targeted TCM constitution-based health maintenance and regulation to improve the health status of nursing personnel.
文摘As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpine plant distribution and their profound impact on fragile ecosystems have become a focus of ecological research and conservation efforts,with increasing urgency.Meconopsis,a typical representative of Chinese alpine plants,exhibits diverse adaptability,making it an ideal model for studying how alpine species respond to extreme environmental changes.A lack of comprehensive genus-level analyses may hinder the development of long-term conservation and management strategies.Given the genus's ecological importance,vulnerability,and the risk of trait homogenization in genus-level modeling,there is an urgent need to assess its future distribution patterns,migration trends,and adaptive mechanisms based on habitat classification.In this study,we employed the Maxent model,integrating multidimensional environmental variables,to develop genus-level models and representative habitat-based models(forest,meadows,and periglacial).Results indicate a northwestward expansion and southeastward contraction of suitable habitats under future climate scenarios,with migration patterns in latitude and elevation showing stage-specific characteristics.Key environmental factors varied across models but were mostly associated with seasonal growth traits and microhabitat conditions,highlighting both the universal ecological requirements and niche differentiation within Meconopsis.Based on these findings,we propose a dynamic conservation strategy framework informed by stage-specific responses and habitat differences.Future efforts should focus on incorporating alpine-specific environmental variables and optimizing specimen collection strategies to enhance model performance and support landscape planning and biodiversity conservation.
基金supported by the National Natural Science Foundation(NSF)of China(62475265,22031009,22075282,12404064)the National Key Research and Development Program of China(2021YFB3601501)+1 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(XDB1170000)NSF of Fujian Province(2023J01212,2024J08106).
文摘Heat-induced emission peak shift(HIEPS),encompassing both redshift and blueshift,remains mechanistically unresolved in phosphor materials.Using state-of-the-art first-principles calculations of M_(2)SiO_(4):Eu^(2+)(M=Sr,Ba,Ca),we reveal that conventional thermal expansion theory cannot adequately explain these phenomena.Instead,our frozen phonon analysis identifies local electron-phonon coupling as the dominant mechanism,where anisotropic thermal vibrations selectively distort the asymmetric Eu-5d potential well that arises from the dopant’s coordination environment.This distortion manifests through the temperature-sensitiveΔ_(f−d) parameter governing the 5d→4f transition energy,directly controlling spectral shifts.Our findings establish a universal framework for HIEPS in rare-earth phosphors and enable a Δ_(f−d)-guided strategy for designing thermally stable phosphors.
基金Supported by the School-level Project of Sichuan Minzu College(XYZB2017ZB).
文摘Research on tourism climate comfort is undergoing a paradigm shift from classic static assessment to intelligent dynamic sensing.Early models(such as temperature-humidity index and tourism climate index)established based on data of meteorological stations laid the foundation for the discipline but were unable to meet the dynamic demands of climate change,spatial heterogeneity,and individual experience.Global climate change is reshaping the landscape of tourism comfort and driving the assessment to shift towards future risk prediction.Downscaling technology becomes the key to connecting global scenarios and local assessments.Remote sensing and Internet of Things technologies have constructed a"sky-ground"collaborative sensing network,achieving a revolution in data acquisition.Artificial intelligence and big data analysis serve as the intelligent core to drive research from description to prediction.The new paradigm has significant potential in improving assessment accuracy and timeliness,but also faces challenges such as data integration,model interpretability,interdisciplinary integration,and ethical privacy.In the future,it is needed to develop interpretable AI,construct climate digital twins,and promote full-chain coupling research.This transformation is not merely an upgrade of methods,but a fundamental shift in the study of philosophy from an"environment-centered"perspective to an"experience-centered"one,providing key scientific support for sustainable tourism.
文摘Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1].However,the stress caused by fast-paced life often leads to sleep deprivation(SD).SD is strongly associated with damage to the auditory system[2,3].Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common sleep disorder.Clinical observations indicate that some patients with OSAHS experience persistent hearing loss accompanied by tinnitus and other symptoms[4].More than 61.8%of patients with sudden deafness experienced SD[5].
文摘Academic journals,consulting firms,and mainstream media have often published“predictions”about the future develop-ment of medical and health care.These publications often emphasize the potential of cutting-edge scientific or technical breakthroughs.Health Care Science looks at the problem from another perspective.We focus on how these changes enter the health system,how they operate in the real world,and how they reshape the organization and governance of medical services.At the beginning of 2026,we envision the following three major shifts that will reshape healthcare.
文摘At the start of China’s 15th Five-Year Plan period(2026–2030),signals from March’s Two Sessions point to a subtle but notable change in direction.Policymakers are moving beyond an emphasis on headline growth figures and are instead concentrating on building a more resilient economic foundation in a volatile global environment.
基金Item Sponsored by National Natural Science Foundation of China(50474016)
文摘The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.
基金Item Sponsored by National Natural Science Foundation of China (50604006)
文摘Owing to a lack of gaugemeter and the variety of steel grades and standards in some plate mills, the longand short-term self-learning models of rolling force based on gauge soft-measuring with high precision were brought up. The soft-measuring method and target value locked method were used in these models to confirm the actual exit gauge of passes, and thick layer division and exponential smoothing method were used to dispose the deformation resistance parameter, which could be calculated from the actual data of the rolling process. The correlative mathematical methods can also be adapted to self-learning with gaugemeter. The models were applied to the process control system of AGC （automatic gauge control） reconstruction on 2800 mm finishing mill of Anyang steel and favorable effect was obtained.
基金Item Sponsored by National Natural Science Foundation of China (50527402)
文摘On the basis of a simulated bright continuous annealing experimental machine, a process control model for heating system was built. The heating model was simplified and self-learning parameters were normalized to enhance the precision of temperature control. By means of the division of temperature layers and the exponential smoothing disposal of the annealing experimental data, the self-learning of the heating model was carried out. Through exponentially smoothing the deviation of self-learning parameters of the heated phase in heating process, dynamic modifications of self-learning parameters and heating electric current were carried out, and the precision of temperature control was confirmed. The application indicated that the process control model for the heating system can control temperature with high precision, and the deviation can be controlled within 8 ℃.
文摘In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.
基金the National Natural Science Foundation of China(No.61375086)the Key Project of Science and Technique Plan of Beijing Municipal Commission of Education(No.KZ201210005001)+1 种基金the National Basic Research Program(973)of China(No.2012CB720000)the China Scholarship Council Program(No.201406540017)
文摘To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.
文摘This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.
基金Project supported by the National Key Technology Research and Development Program (Grant No.2006BAE03A08)
文摘Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.
文摘A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.
文摘This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.
