The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain function...The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain functions,the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal.Recent studies utilized one single long run for task trials of the same condition,the so-called continuous design,to investigate functional connectivity based on task functional magnetic resonance imaging.Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment,which has been examined for working memory,sensory,motor,and semantic task experiments in previous research.But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks.This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks,by using repeated-measures analysis of variance.Across 50 young healthy adults,behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load.Imaging results revealed that the cingulo-opercular,fronto-parietal,and default model networks were associated with not only task activation,but significant main effects of design and load as well as their interaction on intra-and inter-network functional connectivity and global network topology.Moreover,a significant behavior-brain association was identified for the continuous design.This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.展开更多
Facial and vocal expressions are essential modalities mediating the perception of emotion and social communication.Nonetheless,currently little is known about how emotion perception and its neural substrates differ ac...Facial and vocal expressions are essential modalities mediating the perception of emotion and social communication.Nonetheless,currently little is known about how emotion perception and its neural substrates differ across facial expression and vocal prosody.To clarify this issue,functional MRI scans were acquired in Study 1,in which participants were asked to discriminate the valence of emotional expression(angry,happy or neutral)from facial,vocal,or bimodal stimuli.In Study 2,we used an affective priming task(unimodal materials as primers and bimodal materials as target)and participants were asked to rate the intensity,valence,and arousal of the targets.Study 1 showed higher accuracy and shorter response latencies in the facial than in the vocal modality for a happy expression.Whole-brain analysis showed enhanced activation during facial compared to vocal emotions in the inferior temporal-occipital regions.Region of interest analysis showed a higher percentage signal change for facial than for vocal anger in the superior temporal sulcus.Study 2 showed that facial relative to vocal priming of anger had a greater influence on perceived emotion for bimodal targets,irrespective of the target valence.These findings suggest that facial expression is associated with enhanced emotion perception compared to equivalent vocal prosodies.展开更多
Converting CO2 to carbon-containing fuels is an effective approach to relieving energy shortages.Carbon quantum dots(CQDs) have shown distinct properties and attracted tremendous interest in CO2 reduction.Herein,we re...Converting CO2 to carbon-containing fuels is an effective approach to relieving energy shortages.Carbon quantum dots(CQDs) have shown distinct properties and attracted tremendous interest in CO2 reduction.Herein,we report a joint experimental-computational mechanistic study of photoreduction CO2 to CO on the model catalyst 9-hydroxyphenal-1-one(HPHN) CQDs with known structure.Our theoretical calculations reveal that the rate-determining step is COOH·formation,which is closely related to the proton and electron transfer induced by hydrogen bonding in the excited state.According to the calculated volcano plot,the solution we proposed is addition Zn^(2+) ions.The active center changed from the hydroxyl oxygen atom to the Zn atom and the barrier of the COOH·formation step is noticeably decreased when Zn^(2+) ions are added.It is further confirmed by the experimental data that the activity of CO2 reduction increases 2.9 times when Zn^(2+) ions are added.展开更多
Free of noble-metal and high in unit internal quantum efficiency of electroluminescence,organic molecules with thermally activated delayed fluorescence(TADF)features pose the potential to substitute metal-based phosph...Free of noble-metal and high in unit internal quantum efficiency of electroluminescence,organic molecules with thermally activated delayed fluorescence(TADF)features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes(OLEDs)display.Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge.The advances in deep learning(DL)based artificial intelligence(Al)offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation.However,data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed.nspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures,we developed the Electronic Structure-lnfused Network(ESIN)for TADF emitter screening.Designed with capacities of accurate prediction of the photoluminescence quantum yields(PLQYs)of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals(FMOs)weightbased representation and modeling features,ESIN is a promising interpretable tool for emission efficiency evaluation and moleculardesign of TADF emitters.展开更多
Modem product development becomes increasingly collaborative and integrated, which raises the need for effectively and efficiently sharing and re-using design knowledge in a distributed and collaborative environment. ...Modem product development becomes increasingly collaborative and integrated, which raises the need for effectively and efficiently sharing and re-using design knowledge in a distributed and collaborative environment. To address this need, a framework is developed in this research to support design knowledge representation, retrieval, reasoning and fusion, which takes account of structural, functional and behavioral data, various design attributes and knowledge reasoning cases. Specifically, a multi-level knowledge representation based on the Base Object Model （BOM） is proposed to enable knowledge sharing using Web services technologies. On this basis, a multi-level knowledge reuse method is developed to support the retrieval, matching and assembly of knowledge records. Due to the tree structure of BOM, both depth-first and breadth-first searching strategies are employed in the retrieval algorithm while a novel measure is proposed to evaluate similarity. Moreover, a method based on the D-S evidence theory is developed to enable knowledge fusion and thus support effective decision-making. The framework has been implemented and integrated into an HLA-based simulation platform on which the development of a missile simulation example is conducted. It is demonstrated in the case study that the proposed framework and methods are useful and effective for design knowledge representation and reuse.展开更多
Extant giant pandas are divided into Sichuan and Qinling subspecies.The giant panda has many speciesspecific characteristics,including comparatively small organs for body size,small genitalia of male individuals,and l...Extant giant pandas are divided into Sichuan and Qinling subspecies.The giant panda has many speciesspecific characteristics,including comparatively small organs for body size,small genitalia of male individuals,and low reproduction.Here,we report the most contiguous,high-quality chromosomelevel genomes of two extant giant panda subspecies to date,with the first genome assembly of the Qinling subspecies.Compared with the previously assembled giant panda genomes based on short reads,our two assembled genomes increased contiguity over 200-fold at the contig level.Additional sequencing of 25 individuals dated the divergence of the Sichuan and Qinling subspecies into two distinct clusters from 10,000 to 12,000 years ago.Comparative genomic analyses identified the loss of regulatory elements in the dachshund family transcription factor 2(DACH2)gene and specific changes in the synaptotagmin 6(SYT6)gene,which may be responsible for the reduced fertility of the giant panda.Positive selection analysis between the two subspecies indicated that the reproduction-associated IQ motif containing D(IQCD)gene may at least partly explain the different reproduction rates of the two subspecies.Furthermore,several genes in the Hippo pathway exhibited signs of rapid evolution with giant panda-specific variants and divergent regulatory elements,which may contribute to the reduced inner organ sizes of the giant panda.展开更多
Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of CPS.Simulation-oriented model reuse can be used to build a whole CPS model by reusi...Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of CPS.Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of submodels.Model composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems.In this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel nodes.Specifically,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching.Submodel properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are quantified.Based on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are retrieved.Results show that the effectiveness of the model integration method proposed in this paper is verified.展开更多
In order to realize the agility,collaboration and visualization of alloy material devel-opment process,a product development platform based on simulation and modeling technologies is established in this study.In this ...In order to realize the agility,collaboration and visualization of alloy material devel-opment process,a product development platform based on simulation and modeling technologies is established in this study.In this platform,the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level,the thermo-mechanical coupling field level and the microstructure evolution level.The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model,including customers’requirement knowledge,material component knowledge,process design knowledge and quality inspection knowledge,and utilizes the case-based reasoning approach to reuse the knowledge.The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties,material components,and process parameters from historical samples,and utilizes multi-objective optimiza-tion algorithms to find the optimal combination of process parameters.Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.展开更多
Multimodal sentiment analysis(MSA)is an evolving field that integrates information from multiple modalities such as text,audio,and visual data to analyze and interpret human emotions and sentiments.This review provide...Multimodal sentiment analysis(MSA)is an evolving field that integrates information from multiple modalities such as text,audio,and visual data to analyze and interpret human emotions and sentiments.This review provides an extensive survey of the current state of multimodal sentiment analysis,highlighting fundamental concepts,popular datasets,techniques,models,challenges,applications,and future trends.By examining existing research and methodologies,this paper aims to present a cohesive understanding of MSA,Multimodal sentiment analysis(MSA)integrates data from text,audio,and visual sources,each contributing unique insights that enhance the overall understanding of sentiment.Textual data provides explicit content and context,audio data captures the emotional tone through speech characteristics,and visual data offers cues from facial expressions and body language.Despite these strengths,MSA faces limitations such as data integration challenges,computational complexity,and the scarcity of annotated multimodal datasets.Future directions include the development of advanced fusion techniques,real-time processing capabilities,and explainable AI models.These advancements will enable more accurate and robust sentiment analysis,improve user experiences,and enhance applications in human-computer interaction,healthcare,and social media analysis.By addressing these challenges and leveraging diverse data sources,MSA has the potential to revolutionize sentiment analysis and drive positive outcomes across various domains.展开更多
基金supported by the National Natural Science Foundation of China(62071109 and 61871420)the Provincial Natural Science Foundation of Sichuan(2022NSFSC0504).
文摘The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain functions,the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal.Recent studies utilized one single long run for task trials of the same condition,the so-called continuous design,to investigate functional connectivity based on task functional magnetic resonance imaging.Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment,which has been examined for working memory,sensory,motor,and semantic task experiments in previous research.But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks.This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks,by using repeated-measures analysis of variance.Across 50 young healthy adults,behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load.Imaging results revealed that the cingulo-opercular,fronto-parietal,and default model networks were associated with not only task activation,but significant main effects of design and load as well as their interaction on intra-and inter-network functional connectivity and global network topology.Moreover,a significant behavior-brain association was identified for the continuous design.This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.
基金supported by the National Natural Science Foundation of China(31371042 and 31671164)
文摘Facial and vocal expressions are essential modalities mediating the perception of emotion and social communication.Nonetheless,currently little is known about how emotion perception and its neural substrates differ across facial expression and vocal prosody.To clarify this issue,functional MRI scans were acquired in Study 1,in which participants were asked to discriminate the valence of emotional expression(angry,happy or neutral)from facial,vocal,or bimodal stimuli.In Study 2,we used an affective priming task(unimodal materials as primers and bimodal materials as target)and participants were asked to rate the intensity,valence,and arousal of the targets.Study 1 showed higher accuracy and shorter response latencies in the facial than in the vocal modality for a happy expression.Whole-brain analysis showed enhanced activation during facial compared to vocal emotions in the inferior temporal-occipital regions.Region of interest analysis showed a higher percentage signal change for facial than for vocal anger in the superior temporal sulcus.Study 2 showed that facial relative to vocal priming of anger had a greater influence on perceived emotion for bimodal targets,irrespective of the target valence.These findings suggest that facial expression is associated with enhanced emotion perception compared to equivalent vocal prosodies.
基金supported by the National Natural Science Foundation of China (Grant Nos. 21677029, 21606040)the Fundamental Research Funds for the Central Universities (DUT18LK26)。
文摘Converting CO2 to carbon-containing fuels is an effective approach to relieving energy shortages.Carbon quantum dots(CQDs) have shown distinct properties and attracted tremendous interest in CO2 reduction.Herein,we report a joint experimental-computational mechanistic study of photoreduction CO2 to CO on the model catalyst 9-hydroxyphenal-1-one(HPHN) CQDs with known structure.Our theoretical calculations reveal that the rate-determining step is COOH·formation,which is closely related to the proton and electron transfer induced by hydrogen bonding in the excited state.According to the calculated volcano plot,the solution we proposed is addition Zn^(2+) ions.The active center changed from the hydroxyl oxygen atom to the Zn atom and the barrier of the COOH·formation step is noticeably decreased when Zn^(2+) ions are added.It is further confirmed by the experimental data that the activity of CO2 reduction increases 2.9 times when Zn^(2+) ions are added.
基金supported by the National Natural Science Foundation of China(52173282 and 21935005)the National Key R&D Program of China(2020YFA0714601)+2 种基金Guangdong Basic and Applied Basic Research Foundation(No.2022A1515140078)Jihua Laboratory(X190321TF190 and X210221TP210)Foshan Science and Technology Innovation Team Special Project(1920001000128)。
文摘Free of noble-metal and high in unit internal quantum efficiency of electroluminescence,organic molecules with thermally activated delayed fluorescence(TADF)features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes(OLEDs)display.Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge.The advances in deep learning(DL)based artificial intelligence(Al)offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation.However,data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed.nspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures,we developed the Electronic Structure-lnfused Network(ESIN)for TADF emitter screening.Designed with capacities of accurate prediction of the photoluminescence quantum yields(PLQYs)of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals(FMOs)weightbased representation and modeling features,ESIN is a promising interpretable tool for emission efficiency evaluation and moleculardesign of TADF emitters.
基金This research is supported by the National Natural Science Foundation of China （Grant No.61374163）, the National Key Technology R＆D Program （Grant No. 2012BAF15G00）, the National High Technology Research and Development Program （863 Program） of China （Grant No.2013AA041302）. Acknowledgments This research is supported by the National Natural Science Foundation of China （Grant No.61374163 ） , the National Key Technology R＆D Program （Grant No. 2012BAF 15G00）, the National High Technology Research and Development Program （863 Program） of China （Grant No.2013AA041302）. The original version of this paper was presented at the 18th 1EEE CSCWD Conference held in Taiwan, China in May 2014.
文摘Modem product development becomes increasingly collaborative and integrated, which raises the need for effectively and efficiently sharing and re-using design knowledge in a distributed and collaborative environment. To address this need, a framework is developed in this research to support design knowledge representation, retrieval, reasoning and fusion, which takes account of structural, functional and behavioral data, various design attributes and knowledge reasoning cases. Specifically, a multi-level knowledge representation based on the Base Object Model （BOM） is proposed to enable knowledge sharing using Web services technologies. On this basis, a multi-level knowledge reuse method is developed to support the retrieval, matching and assembly of knowledge records. Due to the tree structure of BOM, both depth-first and breadth-first searching strategies are employed in the retrieval algorithm while a novel measure is proposed to evaluate similarity. Moreover, a method based on the D-S evidence theory is developed to enable knowledge fusion and thus support effective decision-making. The framework has been implemented and integrated into an HLA-based simulation platform on which the development of a missile simulation example is conducted. It is demonstrated in the case study that the proposed framework and methods are useful and effective for design knowledge representation and reuse.
基金supported by the National Key Program(2016YFC0503200)from the Ministry of Science and Technology of Chinaa special grant for the giant panda from the State Forestry Administration of the People’s Republic of China+2 种基金the Fundamental Research Funds for the Central Universities of the People’s Republic of Chinathe Foundation of Key Laboratory of State Forestry and Grassland Administration(State Park Administration)on Conservation Biology of Rare Animals in the Giant Panda National Park(KLSFGAGP2020.002)the Guangdong Provincial Key Laboratory of Genome Read and Write(2017B030301011)。
文摘Extant giant pandas are divided into Sichuan and Qinling subspecies.The giant panda has many speciesspecific characteristics,including comparatively small organs for body size,small genitalia of male individuals,and low reproduction.Here,we report the most contiguous,high-quality chromosomelevel genomes of two extant giant panda subspecies to date,with the first genome assembly of the Qinling subspecies.Compared with the previously assembled giant panda genomes based on short reads,our two assembled genomes increased contiguity over 200-fold at the contig level.Additional sequencing of 25 individuals dated the divergence of the Sichuan and Qinling subspecies into two distinct clusters from 10,000 to 12,000 years ago.Comparative genomic analyses identified the loss of regulatory elements in the dachshund family transcription factor 2(DACH2)gene and specific changes in the synaptotagmin 6(SYT6)gene,which may be responsible for the reduced fertility of the giant panda.Positive selection analysis between the two subspecies indicated that the reproduction-associated IQ motif containing D(IQCD)gene may at least partly explain the different reproduction rates of the two subspecies.Furthermore,several genes in the Hippo pathway exhibited signs of rapid evolution with giant panda-specific variants and divergent regulatory elements,which may contribute to the reduced inner organ sizes of the giant panda.
基金This work was supported by the National Key R&D Program of China(No.2018YFB1701600).
文摘Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of CPS.Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of submodels.Model composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems.In this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel nodes.Specifically,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching.Submodel properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are quantified.Based on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are retrieved.Results show that the effectiveness of the model integration method proposed in this paper is verified.
基金This research is supported by the National Key R&D Program of China under the Grant No.2018YFB1701602the National Natural Science Foundation of China under the Grant No.61903031the Fundamental Research Funds for the Cen-tral Universities under the Grant No.FRF-TP-20-050A2.
文摘In order to realize the agility,collaboration and visualization of alloy material devel-opment process,a product development platform based on simulation and modeling technologies is established in this study.In this platform,the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level,the thermo-mechanical coupling field level and the microstructure evolution level.The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model,including customers’requirement knowledge,material component knowledge,process design knowledge and quality inspection knowledge,and utilizes the case-based reasoning approach to reuse the knowledge.The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties,material components,and process parameters from historical samples,and utilizes multi-objective optimiza-tion algorithms to find the optimal combination of process parameters.Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.
文摘Multimodal sentiment analysis(MSA)is an evolving field that integrates information from multiple modalities such as text,audio,and visual data to analyze and interpret human emotions and sentiments.This review provides an extensive survey of the current state of multimodal sentiment analysis,highlighting fundamental concepts,popular datasets,techniques,models,challenges,applications,and future trends.By examining existing research and methodologies,this paper aims to present a cohesive understanding of MSA,Multimodal sentiment analysis(MSA)integrates data from text,audio,and visual sources,each contributing unique insights that enhance the overall understanding of sentiment.Textual data provides explicit content and context,audio data captures the emotional tone through speech characteristics,and visual data offers cues from facial expressions and body language.Despite these strengths,MSA faces limitations such as data integration challenges,computational complexity,and the scarcity of annotated multimodal datasets.Future directions include the development of advanced fusion techniques,real-time processing capabilities,and explainable AI models.These advancements will enable more accurate and robust sentiment analysis,improve user experiences,and enhance applications in human-computer interaction,healthcare,and social media analysis.By addressing these challenges and leveraging diverse data sources,MSA has the potential to revolutionize sentiment analysis and drive positive outcomes across various domains.
