On the morning of May 31st,the parallel forum"Ecological Actions to Carry Forward the Shared Values of Mankind,"as part of the 4th Dialogue on Exchanges and Mutual Learning among Civilisations,was held in Du...On the morning of May 31st,the parallel forum"Ecological Actions to Carry Forward the Shared Values of Mankind,"as part of the 4th Dialogue on Exchanges and Mutual Learning among Civilisations,was held in Dunhuang.More than 50 experts and scholars from different countries,including China,Kenya and Japan,engaged in indepth discussions on the theme.展开更多
This paper comprehensively explores the impulsive on-orbit inspection game problem utilizing reinforcement learning and game training methods.The purpose of the spacecraft is to inspect the entire surface of a non-coo...This paper comprehensively explores the impulsive on-orbit inspection game problem utilizing reinforcement learning and game training methods.The purpose of the spacecraft is to inspect the entire surface of a non-cooperative target with active maneuverability in front lighting.First,the impulsive orbital game problem is formulated as a turn-based sequential game problem.Second,several typical relative orbit transfers are encapsulated into modules to construct a parameterized action space containing discrete modules and continuous parameters,and multi-pass deep Q-networks(MPDQN)algorithm is used to implement autonomous decision-making.Then,a curriculum learning method is used to gradually increase the difficulty of the training scenario.The backtracking proportional self-play training framework is used to enhance the agent’s ability to defeat inconsistent strategies by building a pool of opponents.The behavior variations of the agents during training indicate that the intelligent game system gradually evolves towards an equilibrium situation.The restraint relations between the agents show that the agents steadily improve the strategy.The influence of various factors on game results is tested.展开更多
Microbial corrosion of hydraulic concrete structures(HCSs)has received increasing research concerns.However,knowledge on the morphology of attached biofilms,as well as the community structures and functions cultivated...Microbial corrosion of hydraulic concrete structures(HCSs)has received increasing research concerns.However,knowledge on the morphology of attached biofilms,as well as the community structures and functions cultivated under variable nutrient levels is lacking.Here,biofilm colonization patterns and community structures responding to variable levels of ammonia and sulfate were explored.From field sampling,NH_(4)^(+)-N was proven key factor governing community structure in attached biofilms,verifying the reliability of selecting target nutrient species in batch experiments.Biofilms exhibited significant compositional differences in field sampling and incubation experiments.As the nutrient increased in batch experiments,the growth of biofilms gradually slowed down and uneven distribution was detected.The proportions of proteins and β-d-glucose polysaccharides in biofilms experienced a decrease in response to elevated levels of nutrients.With the increased of nutrients,themass losses of concretes exhibited an increase,reaching a highest value of 2.37%in the presence of 20 mg/L of ammonia.Microbial communities underwent a significant transition in structure and metabolic functions to ammonia gradient.The highest activity of nitrification was observed in biofilms colonized in the presence of 20 mg/L of ammonia.While the communities and their functions remained relativelymore stable responding to sulfate gradient.Our research provides novel insights into the structures of biofilms attached on HCSs and the metabolic functions in the presence of high level of nutrients,which is of significance for the operation and maintenance of hydraulic engineering structures.展开更多
Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,M...Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,Member of the Epilepsy Research Centre Prague-EpiReC Consortium,Prague,Czechia"should only be assigned to Radek Janca and Petr Jezdik.It is removed from the authors:Jiri Hammer,Michaela Kajsova,Adam Kalina,Petr Marusic,and Kamil Vlcek.展开更多
Let R be a commutative ring with unity and T be a triangular algebra over R.Let a sequence G={G_n}_(n∈N)of nonlinear mappings G_n:T→T associated with nonlinear Lie triple higher derivations∆={δ_n}_(n∈N)by local ac...Let R be a commutative ring with unity and T be a triangular algebra over R.Let a sequence G={G_n}_(n∈N)of nonlinear mappings G_n:T→T associated with nonlinear Lie triple higher derivations∆={δ_n}_(n∈N)by local actions be a generalized Lie triple higher derivation by local actions satisfying Gn([[x,y],z])=Σ_(i+j+k=n)[[Gi(x),δj(y)],δk(z)]for all x,y,z∈T with xyz=0.Under some mild conditions on T,we prove in this paper that every nonlinear generalized Lie triple higher derivation by local actions on triangular algebras is proper.As an application we shall give a characterization of nonlinear generalized Lie triple higher derivations by local actions on upper triangular matrix algebras and nest algebras,respectively.At the same time,it also improves some interesting conclusions,such as[J.Algebra Appl.22(3),2023,Paper No.2350059],[Axioms,11,2022,1–16].展开更多
The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-g...The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-guided actions.However,recent evidence suggests a more integrated function of these streams.We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG.We tracked neural activity in the inferior parietal lobule in the dorsal stream,and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory.We found increased alpha power in both streams during the delay,indicating their role in maintaining spatial visual information.In addition,we recorded increased alpha power in the hippocampus during the delay,but only when both object identity and location needed to be remembered.We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay.Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule,ventral temporal cortex,and hippocampus that varied across task phases.Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams,supporting an integrated processing model in which both streams contribute to memory-guided actions.展开更多
In recent years,the path planning for multi-agent technology has gradually matured,and has made breakthrough progress.The main difficulties in path planning for multi-agent are large state space,long algorithm running...In recent years,the path planning for multi-agent technology has gradually matured,and has made breakthrough progress.The main difficulties in path planning for multi-agent are large state space,long algorithm running time,multiple optimization objectives,and asynchronous action of multiple agents.To solve the above problems,this paper first introduces the main problem of the research:multi-objective multi-agent path finding with asynchronous action,and proposes the algorithm framework of multi-objective loose synchronous(MO-LS)search.By combining A*and M*,MO-LS-A*and MO-LS-M*algorithms are respectively proposed.The completeness and optimality of the algorithm are proved,and a series of comparative experiments are designed to analyze the factors affecting the performance of the algorithm,verifying that the proposed MO-LS-M*algorithm has certain advantages.展开更多
This article focuses on the clinical efficacy and mechanism of action of heatclearing and detoxifying traditional Chinese medicines(TCMs)in the treatment of erosive gastritis,providing a reference for the treatment of...This article focuses on the clinical efficacy and mechanism of action of heatclearing and detoxifying traditional Chinese medicines(TCMs)in the treatment of erosive gastritis,providing a reference for the treatment of this disease.In the clinical treatment of erosive gastritis,TCM combinations such as Qing Gastric San,Semixia Diarrheal Heart Soup,and single-flavored heat-clearing and detoxifying drugs such as dandelion and Huanglian have specific efficacies and effectively improve the patient's symptoms,including killing or inhibiting Helicobacter pylori,reducing inflammatory reactions,protecting the gastric mucosa,inhibiting gastric acid secretion,regulating gastrointestinal hormones,and regulating immune function,playing therapeutic roles through multi-level and multi-target mechanisms.Thus,heat-clearing and detoxifying TCMs have broad application prospects in clinical practice for erosive gastritis.展开更多
It is shown that time asymmetry is essential for deriving thermodynamic law and arises from the turnover of energy while reducing its information content and driving entropy increase. A dynamically interpreted princip...It is shown that time asymmetry is essential for deriving thermodynamic law and arises from the turnover of energy while reducing its information content and driving entropy increase. A dynamically interpreted principle of least action enables time asymmetry and time flow as a generation of action and redefines useful energy as an information system which implements a form of acting information. This is demonstrated using a basic formula, originally applied for time symmetry/energy conservation considerations, relating time asymmetry (which is conventionally denied but here expressly allowed), to energy behaviour. The results derived then explained that a dynamic energy is driving time asymmetry. It is doing it by decreasing the information content of useful energy, thus generating action and entropy increase, explaining action-time as an information phenomenon. Thermodynamic laws follow directly. The formalism derived readily explains what energy is, why it is conserved (1st law of thermodynamics), why entropy increases (2nd law) and that maximum entropy production within the restraints of the system controls self-organized processes of non-linear irreversible thermodynamics. The general significance of the principle of least action arises from its role of controlling the action generating oriented time of nature. These results contrast with present understanding of time neutrality and clock-time, which are here considered a source of paradoxes, intellectual contradictions and dead-end roads in models explaining nature and the universe.展开更多
Objective: To explore the effect of Health Action Process Approach (HAPA) theory in patients with type D personality psoriasis. Methods: A total of 66 patients with type D personality psoriasis admitted to the dermato...Objective: To explore the effect of Health Action Process Approach (HAPA) theory in patients with type D personality psoriasis. Methods: A total of 66 patients with type D personality psoriasis admitted to the dermatology department of a top-three hospital in Jingzhou City from November 2022 to July 2023 were selected and divided into control group and test group with 33 cases in each group by random number table method. The control group received routine health education, and the experimental group received health education based on the HAPA theory. Chronic disease self-efficacy scale, hospital anxiety and depression scale and skin disease quality of life scale were used to evaluate the effect of intervention. Results: After 3 months of intervention, the scores of self-efficacy in experimental group were higher than those in control group (P P Conclusion: Health education based on the theory of HAPA can enhance the self-efficacy of patients with type D personality psoriasis, relieve negative emotions and improve their quality of life.展开更多
The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos,providing a foundation for realizing intelligent and accurate teaching.However,th...The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos,providing a foundation for realizing intelligent and accurate teaching.However,the complex nature of the classroom environment has added challenges and difficulties in the process of student action recognition.In this research article,with regard to the circumstances where students are prone to be occluded and classroom computing resources are restricted in real classroom scenarios,a lightweight multi-modal fusion action recognition approach is put forward.This proposed method is capable of enhancing the accuracy of student action recognition while concurrently diminishing the number of parameters of the model and the Computation Amount,thereby achieving a more efficient and accurate recognition performance.In the feature extraction stage,this method fuses the keypoint heatmap with the RGB(Red-Green-Blue color model)image.In order to fully utilize the unique information of different modalities for feature complementarity,a Feature Fusion Module(FFE)is introduced.The FFE encodes and fuses the unique features of the two modalities during the feature extraction process.This fusion strategy not only achieves fusion and complementarity between modalities,but also improves the overall model performance.Furthermore,to reduce the computational load and parameter scale of the model,we use keypoint information to crop RGB images.At the same time,the first three networks of the lightweight feature extraction network X3D are used to extract dual-branch features.These methods significantly reduce the computational load and parameter scale.The number of parameters of the model is 1.40 million,and the computation amount is 5.04 billion floating-point operations per second(GFLOPs),achieving an efficient lightweight design.In the Student Classroom Action Dataset(SCAD),the accuracy of the model is 88.36%.In NTU 60(Nanyang Technological University Red-Green-Blue-Depth RGB+Ddataset with 60 categories),the accuracies on X-Sub(The people in the training set are different from those in the test set)and X-View(The perspectives of the training set and the test set are different)are 95.76%and 98.82%,respectively.On the NTU 120 dataset(Nanyang Technological University Red-Green-Blue-Depth dataset with 120 categories),RGB+Dthe accuracies on X-Sub and X-Set(the perspectives of the training set and the test set are different)are 91.97%and 93.45%,respectively.The model has achieved a balance in terms of accuracy,computation amount,and the number of parameters.展开更多
ln order to improve the level of investment promotion and redouble effortsto enhance services,on February l9th,the 2025 Action Plan for StabilizingForeign lnvestment was released,proposing 20 measures in four aspects....ln order to improve the level of investment promotion and redouble effortsto enhance services,on February l9th,the 2025 Action Plan for StabilizingForeign lnvestment was released,proposing 20 measures in four aspects.Cur-rently,with increasing uncertainties in the external environment,China facesmultple difficulties and challenges in attracting foreign investment.展开更多
Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous studies.However,most current skeleton-based action recognition using GCN methods u...Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous studies.However,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion features.The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms.In this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action recognition.Firstly,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature extraction.Secondly,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature extraction.Finally,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition performance.We evaluate the model using top-1 and top-5 classification accuracy.On the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,respectively.On the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.展开更多
Video action recognition(VAR)aims to analyze dynamic behaviors in videos and achieve semantic understanding.VAR faces challenges such as temporal dynamics,action-scene coupling,and the complexity of human interactions...Video action recognition(VAR)aims to analyze dynamic behaviors in videos and achieve semantic understanding.VAR faces challenges such as temporal dynamics,action-scene coupling,and the complexity of human interactions.Existing methods can be categorized into motion-level,event-level,and story-level ones based on spatiotemporal granularity.However,single-modal approaches struggle to capture complex behavioral semantics and human factors.Therefore,in recent years,vision-language models(VLMs)have been introduced into this field,providing new research perspectives for VAR.In this paper,we systematically review spatiotemporal hierarchical methods in VAR and explore how the introduction of large models has advanced the field.Additionally,we propose the concept of“Factor”to identify and integrate key information from both visual and textual modalities,enhancing multimodal alignment.We also summarize various multimodal alignment methods and provide in-depth analysis and insights into future research directions.展开更多
Arsenic(As)pollution in coastal wetlands has been receiving growing attention.However,the exact mechanism of As mobility driven by tidal action is still not completely understood.The results reveal that lower total As...Arsenic(As)pollution in coastal wetlands has been receiving growing attention.However,the exact mechanism of As mobility driven by tidal action is still not completely understood.The results reveal that lower total As concentrations in solution were observed in the flood-ebb treatment(FE),with the highest concentration being 7.1μg/L,and As(V)was the predominant species.However,elevated levels of total As in solution were found in the flooded treatment(FL),with a maximum value of 14.5μg/L after 30 days,and As(III)was the predominant form.The results of dissolved organicmatter(DOM)suggest that in the early to mid-stages of the incubation,fulvic acid-like substances might be utilized by microorganisms as electron donors or shuttle bodies,facilitating the reductive release of As/Fe from sediments.Both flood-ebb and flooded treatments promoted the transformation of crystalline iron hydrous oxides-bound As into residual forms.However,prolonged flooded conditions more readily facilitated the formation of specific adsorption forms of As and the reduction of crystalline iron hydrous oxides-bound As,increasing As mobility.In addition,the flood-ebb tides have been found to increase the diversity ofmicrobial populations.The main microbial genera in the flood-ebb treatment included Salinimicrobium,Erythrobacter,Yangia,Sulfitobacter,and Marinobacter.Bacillus,Psychrobacter,and Yangia showed a significant correlation with As(V).In flooded treatment,Bacillus,Pseudomonas,and Geothermobacter played a major role in the reduction and release of As.This study significantly contributes to the current understanding of how As behaves in diverse natural environments.展开更多
Coal direct liquefaction technology is a crucial contemporary coal chemical technology for efficient and clean use of coal resources. The development of direct coal liquefaction technology and the promotion of alterna...Coal direct liquefaction technology is a crucial contemporary coal chemical technology for efficient and clean use of coal resources. The development of direct coal liquefaction technology and the promotion of alternative energy sources are important measures to guarantee energy security and economic security. However, several challenges need to be addressed, including low conversion rate, inadequate oil yield, significant coking, demanding reaction conditions, and high energy consumption. Extensive research has been conducted on these issues, but further exploration is required in certain aspects such as pyrolysis of macromolecules during the liquefaction process, hydrogen activation, catalysts' performance and stability, solvent hydrogenation, as well as interactions between free radicals to understand their mechanisms better. This paper presents a comprehensive analysis of the design strategy for efficient catalysts in coal liquefaction, encompassing the mechanism of coal liquefaction, catalyst construction,and enhancement of catalytic conversion efficiency. It serves as a comprehensive guide for further research endeavors. Firstly, it systematically summarizes the conversion mechanism of direct coal liquefaction, provides detailed descriptions of various catalyst design strategies, and especially outlines the catalytic mechanism. Furthermore, it addresses the challenges and prospects associated with constructing efficient catalysts for direct coal liquefaction based on an understanding of their action mechanisms.展开更多
Action recognition,a fundamental task in the field of video understanding,has been extensively researched and applied.In contrast to an image,a video introduces an extra temporal dimension.However,many existing action...Action recognition,a fundamental task in the field of video understanding,has been extensively researched and applied.In contrast to an image,a video introduces an extra temporal dimension.However,many existing action recognition networks either perform simple temporal fusion through averaging or rely on pre-trained models from image recognition,resulting in limited temporal information extraction capabilities.This work proposes a highly efficient temporal decoding module that can be seamlessly integrated into any action recognition backbone network to enhance the focus on temporal relationships between video frames.Firstly,the decoder initializes a set of learnable queries,termed video-level action category prediction queries.Then,they are combined with the video frame features extracted by the backbone network after self-attention learning to extract video context information.Finally,these prediction queries with rich temporal features are used for category prediction.Experimental results on HMDB51,MSRDailyAct3D,Diving48 and Breakfast datasets show that using TokShift-Transformer and VideoMAE as encoders results in a significant improvement in Top-1 accuracy compared to the original models(TokShift-Transformer and VideoMAE),after introducing the proposed temporal decoder.The introduction of the temporal decoder results in an average performance increase exceeding 11%for TokShift-Transformer and nearly 5%for VideoMAE across the four datasets.Furthermore,the work explores the combination of the decoder with various action recognition networks,including Timesformer,as encoders.This results in an average accuracy improvement of more than 3.5%on the HMDB51 dataset.The code is available at https://github.com/huangturbo/TempDecoder.展开更多
Water decoction is the main form of traditional Chinese medicine(TCM)administered in clinics.Polysaccharides are major components of decoction.Recent studies reported that polysaccharides possess multiple pharmacologi...Water decoction is the main form of traditional Chinese medicine(TCM)administered in clinics.Polysaccharides are major components of decoction.Recent studies reported that polysaccharides possess multiple pharmacological activities.However,the mechanism by which oral Chinese herbal polysaccharides play vital roles in the body remains uncertain.This review discussed the polysaccharides in Chinese herbal decoctions and their effects,direct and indirect.The direct impact of polysaccharides includes being absorbed into the body immunity regulation through Peyer’s patches;electrostatic adsorption,hydrophobic interaction,and glycoprotein receptors-induced antibacterial effects;prebiotic functions;gut microbiota structural regulation;and increasing the relative abundance of beneficial bacteria.The indirect effects of the polysaccharides in Chinese herbal decoctions include phytochemical toxicity reduction and activity enhancement.Finally,their clinical and research significance is summarized and future research directions are discussed.展开更多
Representation learning from unlabeled skeleton data is a challenging task.Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations....Representation learning from unlabeled skeleton data is a challenging task.Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations.However,the structural information of the skeleton data,which also plays a critical role in action recognition,is rarely explored in existing unsupervised methods.To deal with this limitation,we propose a novel twostream autoencoder network to combine the topological information with temporal information of skeleton data.Specifically,we encode the graph structure by graph convolutional network(GCN)and integrate the extracted GCN-based representations into the gate recurrent unit stream.Then we design a transfer module to merge the representations of the two streams adaptively.According to the characteristics of the two-stream autoencoder,a unified loss function composed of multiple tasks is proposed to update the learnable parameters of our model.Comprehensive experiments on NW-UCLA,UWA3D,and NTU-RGBD 60 datasets demonstrate that our proposed method can achieve an excellent performance among the unsupervised skeleton-based methods and even perform a similar or superior performance over numerous supervised skeleton-based methods.展开更多
文摘On the morning of May 31st,the parallel forum"Ecological Actions to Carry Forward the Shared Values of Mankind,"as part of the 4th Dialogue on Exchanges and Mutual Learning among Civilisations,was held in Dunhuang.More than 50 experts and scholars from different countries,including China,Kenya and Japan,engaged in indepth discussions on the theme.
文摘This paper comprehensively explores the impulsive on-orbit inspection game problem utilizing reinforcement learning and game training methods.The purpose of the spacecraft is to inspect the entire surface of a non-cooperative target with active maneuverability in front lighting.First,the impulsive orbital game problem is formulated as a turn-based sequential game problem.Second,several typical relative orbit transfers are encapsulated into modules to construct a parameterized action space containing discrete modules and continuous parameters,and multi-pass deep Q-networks(MPDQN)algorithm is used to implement autonomous decision-making.Then,a curriculum learning method is used to gradually increase the difficulty of the training scenario.The backtracking proportional self-play training framework is used to enhance the agent’s ability to defeat inconsistent strategies by building a pool of opponents.The behavior variations of the agents during training indicate that the intelligent game system gradually evolves towards an equilibrium situation.The restraint relations between the agents show that the agents steadily improve the strategy.The influence of various factors on game results is tested.
基金supported by the National Key Research and Development Project of China(No.2021YFB2600200)the National Natural Science Foundation of China(Nos.52470185 and 52170159)the Open Research Fund of National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety,the Fund of National Key Laboratory of Water Disaster Prevention and Key Research and Development Program of Jiangsu Province(No.BE2022601).
文摘Microbial corrosion of hydraulic concrete structures(HCSs)has received increasing research concerns.However,knowledge on the morphology of attached biofilms,as well as the community structures and functions cultivated under variable nutrient levels is lacking.Here,biofilm colonization patterns and community structures responding to variable levels of ammonia and sulfate were explored.From field sampling,NH_(4)^(+)-N was proven key factor governing community structure in attached biofilms,verifying the reliability of selecting target nutrient species in batch experiments.Biofilms exhibited significant compositional differences in field sampling and incubation experiments.As the nutrient increased in batch experiments,the growth of biofilms gradually slowed down and uneven distribution was detected.The proportions of proteins and β-d-glucose polysaccharides in biofilms experienced a decrease in response to elevated levels of nutrients.With the increased of nutrients,themass losses of concretes exhibited an increase,reaching a highest value of 2.37%in the presence of 20 mg/L of ammonia.Microbial communities underwent a significant transition in structure and metabolic functions to ammonia gradient.The highest activity of nitrification was observed in biofilms colonized in the presence of 20 mg/L of ammonia.While the communities and their functions remained relativelymore stable responding to sulfate gradient.Our research provides novel insights into the structures of biofilms attached on HCSs and the metabolic functions in the presence of high level of nutrients,which is of significance for the operation and maintenance of hydraulic engineering structures.
文摘Correction to:Neuroscience Bulletin https://doi.org/10.1007/s12264-025-01371-x In this article the affiliation"Department of Circuit Theory,Faculty of Electrical Engineering,Czech Technical University in Prague,Member of the Epilepsy Research Centre Prague-EpiReC Consortium,Prague,Czechia"should only be assigned to Radek Janca and Petr Jezdik.It is removed from the authors:Jiri Hammer,Michaela Kajsova,Adam Kalina,Petr Marusic,and Kamil Vlcek.
基金Supported by Open Research Fund of Hubei Key Laboratory of Mathematical Sciences(Central China Normal University)the Natural Science Foundation of Anhui Province(Grant No.2008085QA01)the University Natural Science Research Project of Anhui Province(Grant No.KJ2019A0107)。
文摘Let R be a commutative ring with unity and T be a triangular algebra over R.Let a sequence G={G_n}_(n∈N)of nonlinear mappings G_n:T→T associated with nonlinear Lie triple higher derivations∆={δ_n}_(n∈N)by local actions be a generalized Lie triple higher derivation by local actions satisfying Gn([[x,y],z])=Σ_(i+j+k=n)[[Gi(x),δj(y)],δk(z)]for all x,y,z∈T with xyz=0.Under some mild conditions on T,we prove in this paper that every nonlinear generalized Lie triple higher derivation by local actions on triangular algebras is proper.As an application we shall give a characterization of nonlinear generalized Lie triple higher derivations by local actions on upper triangular matrix algebras and nest algebras,respectively.At the same time,it also improves some interesting conclusions,such as[J.Algebra Appl.22(3),2023,Paper No.2350059],[Axioms,11,2022,1–16].
基金supported by European Union–Next Generation EU(LX22NPO5107(MEYS))the Czech Science Foundation(20-21339S)+2 种基金the Grant Agency of Charles University(GAUK 248122 and 272221)ERDF-Project Brain Dynamics(CZ.02.01.01/00/22_008/0004643)the Ministry of Health of the Czech Republic Project NU21J-08-00081.
文摘The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action:the dorsal stream is assumed to support real-time actions,while the ventral stream facilitates memory-guided actions.However,recent evidence suggests a more integrated function of these streams.We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG.We tracked neural activity in the inferior parietal lobule in the dorsal stream,and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory.We found increased alpha power in both streams during the delay,indicating their role in maintaining spatial visual information.In addition,we recorded increased alpha power in the hippocampus during the delay,but only when both object identity and location needed to be remembered.We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay.Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule,ventral temporal cortex,and hippocampus that varied across task phases.Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams,supporting an integrated processing model in which both streams contribute to memory-guided actions.
基金Aeronautical Science Foundation of China(No.20220001057001)。
文摘In recent years,the path planning for multi-agent technology has gradually matured,and has made breakthrough progress.The main difficulties in path planning for multi-agent are large state space,long algorithm running time,multiple optimization objectives,and asynchronous action of multiple agents.To solve the above problems,this paper first introduces the main problem of the research:multi-objective multi-agent path finding with asynchronous action,and proposes the algorithm framework of multi-objective loose synchronous(MO-LS)search.By combining A*and M*,MO-LS-A*and MO-LS-M*algorithms are respectively proposed.The completeness and optimality of the algorithm are proved,and a series of comparative experiments are designed to analyze the factors affecting the performance of the algorithm,verifying that the proposed MO-LS-M*algorithm has certain advantages.
基金Supported by National Science and Technology Major Project,No.2024ZD0521002The Innovation Team Project of Traditional Chinese Medicine of Liaoning Province,No.LNZYYCXTD-CCCX-003+1 种基金General Program of the National Natural Science Foundation of China,No.82074296Construction Project of Inheritance Studios of Famous Chinese Medicine Experts in China,No.[2022]No.75.
文摘This article focuses on the clinical efficacy and mechanism of action of heatclearing and detoxifying traditional Chinese medicines(TCMs)in the treatment of erosive gastritis,providing a reference for the treatment of this disease.In the clinical treatment of erosive gastritis,TCM combinations such as Qing Gastric San,Semixia Diarrheal Heart Soup,and single-flavored heat-clearing and detoxifying drugs such as dandelion and Huanglian have specific efficacies and effectively improve the patient's symptoms,including killing or inhibiting Helicobacter pylori,reducing inflammatory reactions,protecting the gastric mucosa,inhibiting gastric acid secretion,regulating gastrointestinal hormones,and regulating immune function,playing therapeutic roles through multi-level and multi-target mechanisms.Thus,heat-clearing and detoxifying TCMs have broad application prospects in clinical practice for erosive gastritis.
文摘It is shown that time asymmetry is essential for deriving thermodynamic law and arises from the turnover of energy while reducing its information content and driving entropy increase. A dynamically interpreted principle of least action enables time asymmetry and time flow as a generation of action and redefines useful energy as an information system which implements a form of acting information. This is demonstrated using a basic formula, originally applied for time symmetry/energy conservation considerations, relating time asymmetry (which is conventionally denied but here expressly allowed), to energy behaviour. The results derived then explained that a dynamic energy is driving time asymmetry. It is doing it by decreasing the information content of useful energy, thus generating action and entropy increase, explaining action-time as an information phenomenon. Thermodynamic laws follow directly. The formalism derived readily explains what energy is, why it is conserved (1st law of thermodynamics), why entropy increases (2nd law) and that maximum entropy production within the restraints of the system controls self-organized processes of non-linear irreversible thermodynamics. The general significance of the principle of least action arises from its role of controlling the action generating oriented time of nature. These results contrast with present understanding of time neutrality and clock-time, which are here considered a source of paradoxes, intellectual contradictions and dead-end roads in models explaining nature and the universe.
文摘Objective: To explore the effect of Health Action Process Approach (HAPA) theory in patients with type D personality psoriasis. Methods: A total of 66 patients with type D personality psoriasis admitted to the dermatology department of a top-three hospital in Jingzhou City from November 2022 to July 2023 were selected and divided into control group and test group with 33 cases in each group by random number table method. The control group received routine health education, and the experimental group received health education based on the HAPA theory. Chronic disease self-efficacy scale, hospital anxiety and depression scale and skin disease quality of life scale were used to evaluate the effect of intervention. Results: After 3 months of intervention, the scores of self-efficacy in experimental group were higher than those in control group (P P Conclusion: Health education based on the theory of HAPA can enhance the self-efficacy of patients with type D personality psoriasis, relieve negative emotions and improve their quality of life.
基金supported by the National Natural Science Foundation of China under Grant 62107034the Major Science and Technology Project of Yunnan Province(202402AD080002)Yunnan International Joint R&D Center of China-Laos-Thailand Educational Digitalization(202203AP140006).
文摘The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos,providing a foundation for realizing intelligent and accurate teaching.However,the complex nature of the classroom environment has added challenges and difficulties in the process of student action recognition.In this research article,with regard to the circumstances where students are prone to be occluded and classroom computing resources are restricted in real classroom scenarios,a lightweight multi-modal fusion action recognition approach is put forward.This proposed method is capable of enhancing the accuracy of student action recognition while concurrently diminishing the number of parameters of the model and the Computation Amount,thereby achieving a more efficient and accurate recognition performance.In the feature extraction stage,this method fuses the keypoint heatmap with the RGB(Red-Green-Blue color model)image.In order to fully utilize the unique information of different modalities for feature complementarity,a Feature Fusion Module(FFE)is introduced.The FFE encodes and fuses the unique features of the two modalities during the feature extraction process.This fusion strategy not only achieves fusion and complementarity between modalities,but also improves the overall model performance.Furthermore,to reduce the computational load and parameter scale of the model,we use keypoint information to crop RGB images.At the same time,the first three networks of the lightweight feature extraction network X3D are used to extract dual-branch features.These methods significantly reduce the computational load and parameter scale.The number of parameters of the model is 1.40 million,and the computation amount is 5.04 billion floating-point operations per second(GFLOPs),achieving an efficient lightweight design.In the Student Classroom Action Dataset(SCAD),the accuracy of the model is 88.36%.In NTU 60(Nanyang Technological University Red-Green-Blue-Depth RGB+Ddataset with 60 categories),the accuracies on X-Sub(The people in the training set are different from those in the test set)and X-View(The perspectives of the training set and the test set are different)are 95.76%and 98.82%,respectively.On the NTU 120 dataset(Nanyang Technological University Red-Green-Blue-Depth dataset with 120 categories),RGB+Dthe accuracies on X-Sub and X-Set(the perspectives of the training set and the test set are different)are 91.97%and 93.45%,respectively.The model has achieved a balance in terms of accuracy,computation amount,and the number of parameters.
文摘ln order to improve the level of investment promotion and redouble effortsto enhance services,on February l9th,the 2025 Action Plan for StabilizingForeign lnvestment was released,proposing 20 measures in four aspects.Cur-rently,with increasing uncertainties in the external environment,China facesmultple difficulties and challenges in attracting foreign investment.
基金The Fundamental Research Funds for the Central Universities provided financial support for this research.
文摘Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous studies.However,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion features.The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms.In this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action recognition.Firstly,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature extraction.Secondly,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature extraction.Finally,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition performance.We evaluate the model using top-1 and top-5 classification accuracy.On the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,respectively.On the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(No.LQ23F030001)the National Natural Science Foundation of China(No.62406280)+5 种基金the Autism Research Special Fund of Zhejiang Foundation for Disabled Persons(No.2023008)the Liaoning Province Higher Education Innovative Talents Program Support Project(No.LR2019058)the Liaoning Province Joint Open Fund for Key Scientific and Technological Innovation Bases(No.2021-KF-12-05)the Central Guidance on Local Science and Technology Development Fund of Liaoning Province(No.2023JH6/100100066)the Key Laboratory for Biomedical Engineering of Ministry of Education,Zhejiang University,Chinain part by the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning.
文摘Video action recognition(VAR)aims to analyze dynamic behaviors in videos and achieve semantic understanding.VAR faces challenges such as temporal dynamics,action-scene coupling,and the complexity of human interactions.Existing methods can be categorized into motion-level,event-level,and story-level ones based on spatiotemporal granularity.However,single-modal approaches struggle to capture complex behavioral semantics and human factors.Therefore,in recent years,vision-language models(VLMs)have been introduced into this field,providing new research perspectives for VAR.In this paper,we systematically review spatiotemporal hierarchical methods in VAR and explore how the introduction of large models has advanced the field.Additionally,we propose the concept of“Factor”to identify and integrate key information from both visual and textual modalities,enhancing multimodal alignment.We also summarize various multimodal alignment methods and provide in-depth analysis and insights into future research directions.
基金supported by the National Natural Science Foundation of China(No.41977283)the Qing Lan Project of Jiangsu Province of China.
文摘Arsenic(As)pollution in coastal wetlands has been receiving growing attention.However,the exact mechanism of As mobility driven by tidal action is still not completely understood.The results reveal that lower total As concentrations in solution were observed in the flood-ebb treatment(FE),with the highest concentration being 7.1μg/L,and As(V)was the predominant species.However,elevated levels of total As in solution were found in the flooded treatment(FL),with a maximum value of 14.5μg/L after 30 days,and As(III)was the predominant form.The results of dissolved organicmatter(DOM)suggest that in the early to mid-stages of the incubation,fulvic acid-like substances might be utilized by microorganisms as electron donors or shuttle bodies,facilitating the reductive release of As/Fe from sediments.Both flood-ebb and flooded treatments promoted the transformation of crystalline iron hydrous oxides-bound As into residual forms.However,prolonged flooded conditions more readily facilitated the formation of specific adsorption forms of As and the reduction of crystalline iron hydrous oxides-bound As,increasing As mobility.In addition,the flood-ebb tides have been found to increase the diversity ofmicrobial populations.The main microbial genera in the flood-ebb treatment included Salinimicrobium,Erythrobacter,Yangia,Sulfitobacter,and Marinobacter.Bacillus,Psychrobacter,and Yangia showed a significant correlation with As(V).In flooded treatment,Bacillus,Pseudomonas,and Geothermobacter played a major role in the reduction and release of As.This study significantly contributes to the current understanding of how As behaves in diverse natural environments.
基金National Natural Science Foundation of China (No. 22208273)Tianchi Talent Plan of Xinjiang Uygur Autonomous RegionPostdoctoral Fellowship Program of CPSF under Grant Number GZC20240428。
文摘Coal direct liquefaction technology is a crucial contemporary coal chemical technology for efficient and clean use of coal resources. The development of direct coal liquefaction technology and the promotion of alternative energy sources are important measures to guarantee energy security and economic security. However, several challenges need to be addressed, including low conversion rate, inadequate oil yield, significant coking, demanding reaction conditions, and high energy consumption. Extensive research has been conducted on these issues, but further exploration is required in certain aspects such as pyrolysis of macromolecules during the liquefaction process, hydrogen activation, catalysts' performance and stability, solvent hydrogenation, as well as interactions between free radicals to understand their mechanisms better. This paper presents a comprehensive analysis of the design strategy for efficient catalysts in coal liquefaction, encompassing the mechanism of coal liquefaction, catalyst construction,and enhancement of catalytic conversion efficiency. It serves as a comprehensive guide for further research endeavors. Firstly, it systematically summarizes the conversion mechanism of direct coal liquefaction, provides detailed descriptions of various catalyst design strategies, and especially outlines the catalytic mechanism. Furthermore, it addresses the challenges and prospects associated with constructing efficient catalysts for direct coal liquefaction based on an understanding of their action mechanisms.
基金Shanghai Municipal Commission of Economy and Information Technology,China (No.202301054)。
文摘Action recognition,a fundamental task in the field of video understanding,has been extensively researched and applied.In contrast to an image,a video introduces an extra temporal dimension.However,many existing action recognition networks either perform simple temporal fusion through averaging or rely on pre-trained models from image recognition,resulting in limited temporal information extraction capabilities.This work proposes a highly efficient temporal decoding module that can be seamlessly integrated into any action recognition backbone network to enhance the focus on temporal relationships between video frames.Firstly,the decoder initializes a set of learnable queries,termed video-level action category prediction queries.Then,they are combined with the video frame features extracted by the backbone network after self-attention learning to extract video context information.Finally,these prediction queries with rich temporal features are used for category prediction.Experimental results on HMDB51,MSRDailyAct3D,Diving48 and Breakfast datasets show that using TokShift-Transformer and VideoMAE as encoders results in a significant improvement in Top-1 accuracy compared to the original models(TokShift-Transformer and VideoMAE),after introducing the proposed temporal decoder.The introduction of the temporal decoder results in an average performance increase exceeding 11%for TokShift-Transformer and nearly 5%for VideoMAE across the four datasets.Furthermore,the work explores the combination of the decoder with various action recognition networks,including Timesformer,as encoders.This results in an average accuracy improvement of more than 3.5%on the HMDB51 dataset.The code is available at https://github.com/huangturbo/TempDecoder.
基金by grants from the Science and Technology Development Fund,Macao SAR(0005/2024/AKP,0075/2022/A,and 028/2022/ITP)the Zhuhai Science and Technology Plan Project in the Social Development Field(2220004000117)the University of Macao(MYRG-GRG2023-00082-ICMSUMDF,MYRG-GRG2024-00150-ICMS-UMDF and CPG2025-00030-ICMS).
文摘Water decoction is the main form of traditional Chinese medicine(TCM)administered in clinics.Polysaccharides are major components of decoction.Recent studies reported that polysaccharides possess multiple pharmacological activities.However,the mechanism by which oral Chinese herbal polysaccharides play vital roles in the body remains uncertain.This review discussed the polysaccharides in Chinese herbal decoctions and their effects,direct and indirect.The direct impact of polysaccharides includes being absorbed into the body immunity regulation through Peyer’s patches;electrostatic adsorption,hydrophobic interaction,and glycoprotein receptors-induced antibacterial effects;prebiotic functions;gut microbiota structural regulation;and increasing the relative abundance of beneficial bacteria.The indirect effects of the polysaccharides in Chinese herbal decoctions include phytochemical toxicity reduction and activity enhancement.Finally,their clinical and research significance is summarized and future research directions are discussed.
文摘Representation learning from unlabeled skeleton data is a challenging task.Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations.However,the structural information of the skeleton data,which also plays a critical role in action recognition,is rarely explored in existing unsupervised methods.To deal with this limitation,we propose a novel twostream autoencoder network to combine the topological information with temporal information of skeleton data.Specifically,we encode the graph structure by graph convolutional network(GCN)and integrate the extracted GCN-based representations into the gate recurrent unit stream.Then we design a transfer module to merge the representations of the two streams adaptively.According to the characteristics of the two-stream autoencoder,a unified loss function composed of multiple tasks is proposed to update the learnable parameters of our model.Comprehensive experiments on NW-UCLA,UWA3D,and NTU-RGBD 60 datasets demonstrate that our proposed method can achieve an excellent performance among the unsupervised skeleton-based methods and even perform a similar or superior performance over numerous supervised skeleton-based methods.
