BACKGROUND Pediatric asthma is a significant public health issue that impacts the quality of life of children globally.Traditional management approaches focus on symptom control and medication adherence but often over...BACKGROUND Pediatric asthma is a significant public health issue that impacts the quality of life of children globally.Traditional management approaches focus on symptom control and medication adherence but often overlook the comprehensive educational needs of patients and their families.A multifaceted health education approach may offer a more holistic strategy in managing pediatric asthma,especially in outpatient settings.AIM To evaluate the efficacy of a comprehensive health education strategy in improving disease management,medication adherence,and quality of life among children with asthma in outpatient settings.METHODS In total,100 pediatric patients with severe asthma were enrolled from January 2021 to November 2022 and randomly allocated to a control group(n=50)or an observation group(n=50).The control group received standard nursing care,including basic nursing interventions and health education upon admission.In contrast,the observation group was exposed to a broad spectrum of health education methodologies,including internet-based hospital systems,social media channels,one-on-one verbal education,informational brochures,slide present ations,telephone check-ins,animated videos,and illustrated health education manuals.Data on asthma management knowledge,symptom control,quality of life[St.George’s Respiratory Questionnaire(SGRQ)],treatment adherence,and nursing satisfaction were collected and analyzed.RESULTS The scores of the observation group in knowledge areas,such as medication,home care,disease understanding,symptom management,prevention strategies,and nutritional guidance,were significantly higher than those of the control group(P<0.05).In addition,the observation group exhibited greater symptom control,improved quality of life based on their SGRQ scores,and higher treatment adherence post-intervention(P<0.05).Nursing satisfaction was also rated higher in the observation group across all evaluated areas(P<0.05).CONCLUSION Implementing a diversified health education approach in pediatric asthma management significantly enhances disease understanding,symptom management,and treatment adherence,leading to improved quality of life for affected children.These findings underscore the importance of multifaceted clinical health education in augmenting disease awareness and facilitating continuous improvements in asthma control rates,highlighting the potential benefits of incorporating comprehensive educational strategies into pediatric asthma care protocols.展开更多
Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE...Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.展开更多
In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models...In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.展开更多
The Parkinson＇s disease （PD）-associated protein DJ-1 is considered a multifunctional protein involved in oxidative stress responses. Although a large variety of functions has been attributed to DJ- 1, currently no ...The Parkinson＇s disease （PD）-associated protein DJ-1 is considered a multifunctional protein involved in oxidative stress responses. Although a large variety of functions has been attributed to DJ- 1, currently no consensus has been found on its effective cellular activity. Indeed, the protein has been described as a transcriptional modulator, a regulator of mitochondrial activity, a stabilizer of the nuclear factor erythroid 2-related factor （Nrf2）, a chaperone, a pro- tease, and a glutathione （GSH）-independent glyoxalase （Ariga et al., 2013）. Very recently, the additional participation of the protein to carbonvl stress defense has been emereing （Richarme et al., 2015）.展开更多
Climate change is the most phenomenal challenge to humanity,and its roots are intervened with unsustainable industrialization,exercising overexploitation of natural resources.Therefore,the departure from non-renewable...Climate change is the most phenomenal challenge to humanity,and its roots are intervened with unsustainable industrialization,exercising overexploitation of natural resources.Therefore,the departure from non-renewable to renewables has become inevitable,though thought-provoking.In this respect,we explore how green energy transformation moderates the impacts of multifaceted natural resources on sustainable industrial development in the presence of other covariates involving technological progress,financial development,and economic progress.We compiled data from Group of Seven(G-7)members over the 19952018 period and applied panel quantile regression(PQREG)to capture the effects across varying levels of quantiles of sustainable industrial development.Results revealed a positive role of natural gas rents,while coal,forest,and total natural resource rents contributed adverse implications for sustainable industrial development.However,the green energy transformation proved to be the game changer because it not only directly induced sustainable industrial development improvement but also turned the unfavorable effects of coal,forest,and total natural resources into favorable ones by interacting with those multifaceted natural resources.Technological,financial,and economic progress supported sustainable industrial development in G-7 nations,particularly in members with existing middle and upper scales of sustainable industrial development.These findings are robust enough when subjected to different estimation tools.In light of these outcomes,the interaction between green energy transformation and natural resource policy is inevitably critical to attaining natural resource efficiency for sustainable industrial development.Therefore,it is imperative to establish a close policy coordination between advancing green energy technology and allocating natural resource revenue to achieve sustainable development goals(SDGs),with a particular emphasis on SDG-7 and SDG-13。展开更多
Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we ...Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we propose a Hybrid Channel Stock model that incorporates multiple features of basic stock data,K-line charts and technical indicator factors for predicting the closing price of a stock on day n+1.The model combines multiple aspects of data and uses a multi-channel structure including improved CNN-TW,bidirectional LSTM and Transformer network.First,we construct the multi-channel branches of the multi-faceted feature fusion input network model;second,in this paper,we will use the concatenate method to stitch the output of each branch as the input of the rest of the network;the last layer in the network is the fully connected layer,which combines the linear activation function regression to output the predicted prices.Finally,we conducted extensive experiments on the Dow 30,SSH 50 and CSI100 indices.The experimental results show that the Hybrid Channel Stock method has the best performance with the smallest MSE,RMSE,MAE and MAPE compared with existing models.in addition,the experiments on different trading days validate the stability and effectiveness of the model,providing an important reference for investors to make stock investment decisions.展开更多
This paper proposes a new network structure,namely the ProNet network.Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment.The basel...This paper proposes a new network structure,namely the ProNet network.Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment.The baseline model of the ProNet network is UperNet(Unified perceptual parsing Network),and the backbone network is ConvNext(Convolutional Network).A network structure based on depth-separable convolution and 1×1 convolution is used,which has good performance and robustness.We further optimise ProNet mainly in two aspects.One is data enhancement using increased noise and slight angle rotation,which can significantly increase the diversity of data and help the model better learn the patterns and features of the data and improve the model’s performance.Meanwhile,it can effectively expand the training data set,reduce the influence of noise and abnormal data in the data set on the model,and improve the accuracy and reliability of the model.Another is the loss function aspect,and we finally use the focal loss function.The focal loss function is well suited for complex tasks such as object detection.The function will penalise the loss carried by samples that the model misclassifies,thus enabling better training of the model to avoid these errors while solving the category imbalance problem as a way to improve image segmentation density and segmentation accuracy.From the experimental results,the evaluation metrics mIoU(mean Intersection over Union)enhanced by 4.47%,and mDice enhanced by 2.92% compared to the baseline network.Better generalization effects and more accurate image segmentation are achieved.展开更多
MicroRNAs (miRNAs) are essential regulators,involved in almost all aspects of plant growth and development.In plants,miRNAs prese nt in all an giosperms are regarded as con served miRNAs;in contrast,miRNAs restricted ...MicroRNAs (miRNAs) are essential regulators,involved in almost all aspects of plant growth and development.In plants,miRNAs prese nt in all an giosperms are regarded as con served miRNAs;in contrast,miRNAs restricted to certain lineages (lessconserved) or a single species (species-specific) constitute the non-conserved miRNAs (Cuperus et al.,2011).Different members of a miRNA family usually target similar target genes from a gene family among different species.For in stance,in most analyzed plants,the well-known miR156 family,usually consist!ng of a number of members in a given species,collectively target SQUAMOSA-PROMOTER BINDING PROTEINLIKE (SPL) genes.Gen erally,con served miRNAs target genes encoding transcript factors which function in diverse biological processes.This functional diversity of miRNAs is mainly achieved by the plasticity of their target genes from the same family,such as miR156-targeted SPLs and miR167-targeted ARF (AUXIN RESPONSIVE FACTOR) genes,on regulating distinct downstream gen es.展开更多
Massive Open Online Courses(MOOCs)often provide online discussion forum tools to facilitate learner interaction and communication.Having massive forum messages posted by learners everyday,MOOC forums are regarded as a...Massive Open Online Courses(MOOCs)often provide online discussion forum tools to facilitate learner interaction and communication.Having massive forum messages posted by learners everyday,MOOC forums are regarded as an important source for understanding learners activities and opinions.However,the high volume and heterogeneity of MOOC forum contents make it challenging to analyze forum data effectively from different perspectives of discussions and to integrate diverse information into a coherent understanding of issues of concern.In this paper,we report a study on the design of a visual analytics tool to facilitate the multifaceted analysis of online discussion forums.This tool,called MessageLens,aims at helping MOOC instructors to gain a better understanding of forum discussions from three facets:discussion topic,learner attitude,and communication among learners.With various visualization tools,instructors can investigate learner activities from different perspectives.We report a case study with real-world MOOC forum data to present the features of MessageLens and a preliminary evaluation study on the benefits and areas of improvement of the system.Our research suggests an approach to analyzing rich communication contents as well as dynamic social interactions among people.展开更多
Fluorescent nanomaterials have long been recognized as essential contributors to the advancement of material technologies.Over the years,the rapid expansion in this massive selection of materials has led to the emerge...Fluorescent nanomaterials have long been recognized as essential contributors to the advancement of material technologies.Over the years,the rapid expansion in this massive selection of materials has led to the emergence of systems with tunable and unique fluorescent properties,occupying pivotal roles across niche areas in imaging,photonics,micro-encryption,and steganographic applications.In recent years,research interest in the translation of laser-based operations towards the production and modulation of nanomaterial fluorescence has been reignited,owing to its ease of operation and low cost.In this paper,we summarize the assortment of laser operations for the fabrication,modification,and spatial positioning of various fluorescent nanomaterials,ranging from metallic nanoparticles,carbon dots,2D ultrathin films to wide-bandgap nanomaterials,and upconversion nanocrystals.In addition,we evaluate the importance of laser-modified fluorescence for various applications and offer our perspective on the role of laser-based techniques in the forthcoming advancement of nanomaterials.展开更多
随着新型电力系统和能源互联的持续推进,构建高效、低碳和经济的能源供应系统对发展双碳战略至关重要。为此,提出一种绿证—碳交易联合机制下考虑多类型需求响应和氢能多元利用的综合能源系统(integrated energy system,IES)优化运行策...随着新型电力系统和能源互联的持续推进,构建高效、低碳和经济的能源供应系统对发展双碳战略至关重要。为此,提出一种绿证—碳交易联合机制下考虑多类型需求响应和氢能多元利用的综合能源系统(integrated energy system,IES)优化运行策略。首先,为充分发挥需求侧资源的调节能力,构建含价格型、激励型和替代型的多类型需求响应模型。其次,针对氢能的清洁特性,构建含电制氢、氢制甲烷、氢转热电和天然气混氢的氢能多元利用模型。最后,将绿证交易和碳交易相结合,提出绿证—碳联合交易机制,并构建计及绿证—碳联合交易机制的IES低碳经济运行模型。算例仿真设置不同运行方案对比,验证该文所提模型在提升可再生能源消纳、能源利用效率和降低碳排放量等方面的有效性。展开更多
基金Self-raised project of Health and Health Commission of Guangxi Zhuang Autonomous Region,NO.Z-A20220429Guangxi Natural Science Foundation,NO.2020JJA140193.
文摘BACKGROUND Pediatric asthma is a significant public health issue that impacts the quality of life of children globally.Traditional management approaches focus on symptom control and medication adherence but often overlook the comprehensive educational needs of patients and their families.A multifaceted health education approach may offer a more holistic strategy in managing pediatric asthma,especially in outpatient settings.AIM To evaluate the efficacy of a comprehensive health education strategy in improving disease management,medication adherence,and quality of life among children with asthma in outpatient settings.METHODS In total,100 pediatric patients with severe asthma were enrolled from January 2021 to November 2022 and randomly allocated to a control group(n=50)or an observation group(n=50).The control group received standard nursing care,including basic nursing interventions and health education upon admission.In contrast,the observation group was exposed to a broad spectrum of health education methodologies,including internet-based hospital systems,social media channels,one-on-one verbal education,informational brochures,slide present ations,telephone check-ins,animated videos,and illustrated health education manuals.Data on asthma management knowledge,symptom control,quality of life[St.George’s Respiratory Questionnaire(SGRQ)],treatment adherence,and nursing satisfaction were collected and analyzed.RESULTS The scores of the observation group in knowledge areas,such as medication,home care,disease understanding,symptom management,prevention strategies,and nutritional guidance,were significantly higher than those of the control group(P<0.05).In addition,the observation group exhibited greater symptom control,improved quality of life based on their SGRQ scores,and higher treatment adherence post-intervention(P<0.05).Nursing satisfaction was also rated higher in the observation group across all evaluated areas(P<0.05).CONCLUSION Implementing a diversified health education approach in pediatric asthma management significantly enhances disease understanding,symptom management,and treatment adherence,leading to improved quality of life for affected children.These findings underscore the importance of multifaceted clinical health education in augmenting disease awareness and facilitating continuous improvements in asthma control rates,highlighting the potential benefits of incorporating comprehensive educational strategies into pediatric asthma care protocols.
基金This work is supported by the National MCF Energy R&D Program of China(Grant Nos.2018YFE0302100 and 2019YFE03010003)the National Natural Science Foundation of China(Grant Nos.12005264,12105322,and 12075285)+3 种基金the National Magnetic Confinement Fusion Science Program of China(Grant No.2022YFE03100003)the Natural Science Foundation of Anhui Province of China(Grant No.2108085QA38)the Chinese Postdoctoral Science Found(Grant No.2021000278)the Presidential Foundation of Hefei institutes of Physical Science(Grant No.YZJJ2021QN12).
文摘Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.
文摘In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.
文摘The Parkinson＇s disease （PD）-associated protein DJ-1 is considered a multifunctional protein involved in oxidative stress responses. Although a large variety of functions has been attributed to DJ- 1, currently no consensus has been found on its effective cellular activity. Indeed, the protein has been described as a transcriptional modulator, a regulator of mitochondrial activity, a stabilizer of the nuclear factor erythroid 2-related factor （Nrf2）, a chaperone, a pro- tease, and a glutathione （GSH）-independent glyoxalase （Ariga et al., 2013）. Very recently, the additional participation of the protein to carbonvl stress defense has been emereing （Richarme et al., 2015）.
基金supported by the Key Project of National Social Science Foundation of China(21AGL014)Shenzhen Science and Technology Program(JCYJ20210324093208022)Shenzhen University Humanities and Social Sciences High-level Innovation Team Project for Leading Scholars(24LJXZ06).
文摘Climate change is the most phenomenal challenge to humanity,and its roots are intervened with unsustainable industrialization,exercising overexploitation of natural resources.Therefore,the departure from non-renewable to renewables has become inevitable,though thought-provoking.In this respect,we explore how green energy transformation moderates the impacts of multifaceted natural resources on sustainable industrial development in the presence of other covariates involving technological progress,financial development,and economic progress.We compiled data from Group of Seven(G-7)members over the 19952018 period and applied panel quantile regression(PQREG)to capture the effects across varying levels of quantiles of sustainable industrial development.Results revealed a positive role of natural gas rents,while coal,forest,and total natural resource rents contributed adverse implications for sustainable industrial development.However,the green energy transformation proved to be the game changer because it not only directly induced sustainable industrial development improvement but also turned the unfavorable effects of coal,forest,and total natural resources into favorable ones by interacting with those multifaceted natural resources.Technological,financial,and economic progress supported sustainable industrial development in G-7 nations,particularly in members with existing middle and upper scales of sustainable industrial development.These findings are robust enough when subjected to different estimation tools.In light of these outcomes,the interaction between green energy transformation and natural resource policy is inevitably critical to attaining natural resource efficiency for sustainable industrial development.Therefore,it is imperative to establish a close policy coordination between advancing green energy technology and allocating natural resource revenue to achieve sustainable development goals(SDGs),with a particular emphasis on SDG-7 and SDG-13。
基金supported by these three foundation programs:the Science and Technology Research Project(Youth)of Chongqing Municipal Education Commission(KJQN202201142)the Chongqing Research Program of Basic Research Frontier Technology(CSTB2022BSXM-JCX0069CCCC)the Training Program of the National Natural Science Foundation of China and National Social Science Fund of China of Chongqing University of Technology(2022PYZ030)。
文摘Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we propose a Hybrid Channel Stock model that incorporates multiple features of basic stock data,K-line charts and technical indicator factors for predicting the closing price of a stock on day n+1.The model combines multiple aspects of data and uses a multi-channel structure including improved CNN-TW,bidirectional LSTM and Transformer network.First,we construct the multi-channel branches of the multi-faceted feature fusion input network model;second,in this paper,we will use the concatenate method to stitch the output of each branch as the input of the rest of the network;the last layer in the network is the fully connected layer,which combines the linear activation function regression to output the predicted prices.Finally,we conducted extensive experiments on the Dow 30,SSH 50 and CSI100 indices.The experimental results show that the Hybrid Channel Stock method has the best performance with the smallest MSE,RMSE,MAE and MAPE compared with existing models.in addition,the experiments on different trading days validate the stability and effectiveness of the model,providing an important reference for investors to make stock investment decisions.
文摘This paper proposes a new network structure,namely the ProNet network.Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment.The baseline model of the ProNet network is UperNet(Unified perceptual parsing Network),and the backbone network is ConvNext(Convolutional Network).A network structure based on depth-separable convolution and 1×1 convolution is used,which has good performance and robustness.We further optimise ProNet mainly in two aspects.One is data enhancement using increased noise and slight angle rotation,which can significantly increase the diversity of data and help the model better learn the patterns and features of the data and improve the model’s performance.Meanwhile,it can effectively expand the training data set,reduce the influence of noise and abnormal data in the data set on the model,and improve the accuracy and reliability of the model.Another is the loss function aspect,and we finally use the focal loss function.The focal loss function is well suited for complex tasks such as object detection.The function will penalise the loss carried by samples that the model misclassifies,thus enabling better training of the model to avoid these errors while solving the category imbalance problem as a way to improve image segmentation density and segmentation accuracy.From the experimental results,the evaluation metrics mIoU(mean Intersection over Union)enhanced by 4.47%,and mDice enhanced by 2.92% compared to the baseline network.Better generalization effects and more accurate image segmentation are achieved.
基金funded by the National Key Research and Developmental Program of China (no.2018YFD1000104)the National Natural Science Foundation of China (no.31872063)+1 种基金supported by the Innovation Team Project of the Department of Education of Guangdong Province (no.2016KCXTD 011)the Guangzhou Science and Technology Key Project (no.201804020063).
文摘MicroRNAs (miRNAs) are essential regulators,involved in almost all aspects of plant growth and development.In plants,miRNAs prese nt in all an giosperms are regarded as con served miRNAs;in contrast,miRNAs restricted to certain lineages (lessconserved) or a single species (species-specific) constitute the non-conserved miRNAs (Cuperus et al.,2011).Different members of a miRNA family usually target similar target genes from a gene family among different species.For in stance,in most analyzed plants,the well-known miR156 family,usually consist!ng of a number of members in a given species,collectively target SQUAMOSA-PROMOTER BINDING PROTEINLIKE (SPL) genes.Gen erally,con served miRNAs target genes encoding transcript factors which function in diverse biological processes.This functional diversity of miRNAs is mainly achieved by the plasticity of their target genes from the same family,such as miR156-targeted SPLs and miR167-targeted ARF (AUXIN RESPONSIVE FACTOR) genes,on regulating distinct downstream gen es.
文摘Massive Open Online Courses(MOOCs)often provide online discussion forum tools to facilitate learner interaction and communication.Having massive forum messages posted by learners everyday,MOOC forums are regarded as an important source for understanding learners activities and opinions.However,the high volume and heterogeneity of MOOC forum contents make it challenging to analyze forum data effectively from different perspectives of discussions and to integrate diverse information into a coherent understanding of issues of concern.In this paper,we report a study on the design of a visual analytics tool to facilitate the multifaceted analysis of online discussion forums.This tool,called MessageLens,aims at helping MOOC instructors to gain a better understanding of forum discussions from three facets:discussion topic,learner attitude,and communication among learners.With various visualization tools,instructors can investigate learner activities from different perspectives.We report a case study with real-world MOOC forum data to present the features of MessageLens and a preliminary evaluation study on the benefits and areas of improvement of the system.Our research suggests an approach to analyzing rich communication contents as well as dynamic social interactions among people.
文摘Fluorescent nanomaterials have long been recognized as essential contributors to the advancement of material technologies.Over the years,the rapid expansion in this massive selection of materials has led to the emergence of systems with tunable and unique fluorescent properties,occupying pivotal roles across niche areas in imaging,photonics,micro-encryption,and steganographic applications.In recent years,research interest in the translation of laser-based operations towards the production and modulation of nanomaterial fluorescence has been reignited,owing to its ease of operation and low cost.In this paper,we summarize the assortment of laser operations for the fabrication,modification,and spatial positioning of various fluorescent nanomaterials,ranging from metallic nanoparticles,carbon dots,2D ultrathin films to wide-bandgap nanomaterials,and upconversion nanocrystals.In addition,we evaluate the importance of laser-modified fluorescence for various applications and offer our perspective on the role of laser-based techniques in the forthcoming advancement of nanomaterials.
文摘随着新型电力系统和能源互联的持续推进,构建高效、低碳和经济的能源供应系统对发展双碳战略至关重要。为此,提出一种绿证—碳交易联合机制下考虑多类型需求响应和氢能多元利用的综合能源系统(integrated energy system,IES)优化运行策略。首先,为充分发挥需求侧资源的调节能力,构建含价格型、激励型和替代型的多类型需求响应模型。其次,针对氢能的清洁特性,构建含电制氢、氢制甲烷、氢转热电和天然气混氢的氢能多元利用模型。最后,将绿证交易和碳交易相结合,提出绿证—碳联合交易机制,并构建计及绿证—碳联合交易机制的IES低碳经济运行模型。算例仿真设置不同运行方案对比,验证该文所提模型在提升可再生能源消纳、能源利用效率和降低碳排放量等方面的有效性。
