Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized byclinical symptoms of diarrhea and mucopurulent bloody stools, and its incidenceis increasing globally. The etiology and pathogenesis of U...Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized byclinical symptoms of diarrhea and mucopurulent bloody stools, and its incidenceis increasing globally. The etiology and pathogenesis of UC remain elusive. Currenttherapeutic approaches, including anti-inflammatory, immunosuppressiveand immunomodulating agents, are often limited in efficacy and frequently associatedwith adverse drug reactions. Therefore, there is an urgent need to developsafer and more effective treatment strategies to address the limitations of existingtherapies. Scutellaria baicalensis Georgi (HQ), a traditional Chinese medicinal herb,has been employed in the treatment of UC for over 2000 years. Recent studieshave demonstrated that HQ contains multiple active components capable oftreating UC through anti-inflammation, immune modulation, intestinal barrierprotection, antioxidant activity, and regulation of the gut microbiota. This paperreviews recent studies on the mechanism of action and clinical trials of HQ intreating UC based on relevant literature, with the aim of providing valuable insightsinto future treatment approaches.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
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
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
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
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
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
The proposed paper deals with a numerical approach that could better assist the archaeologist in the archaeological reconstruction projects.The goal of our research is to explore and study the use of computerized tool...The proposed paper deals with a numerical approach that could better assist the archaeologist in the archaeological reconstruction projects.The goal of our research is to explore and study the use of computerized tools in archaeological reconstruction projects of monumental architecture in order to propose new ways in which such technology can be used.展开更多
Skeleton-based human action recognition focuses on identifying actions from dynamic skeletal data,which contains both temporal and spatial characteristics.However,this approach faces chal-lenges such as viewpoint vari...Skeleton-based human action recognition focuses on identifying actions from dynamic skeletal data,which contains both temporal and spatial characteristics.However,this approach faces chal-lenges such as viewpoint variations,low recognition accuracy,and high model complexity.Skeleton-based graph convolutional network(GCN)generally outperform other deep learning methods in rec-ognition accuracy.However,they often underutilize temporal features and suffer from high model complexity,leading to increased training and validation costs,especially on large-scale datasets.This paper proposes a dual-channel graph convolutional network with multi-order information fusion(DM-AGCN)for human action recognition.The network integrates high frame rate skeleton chan-nels to capture action dynamics and low frame rate channels to preserve static semantic information,effectively balancing temporal and spatial features.This dual-channel architecture allows for separate processing of temporal and spatial information.Additionally,DM-AGCN extracts joint keypoints and bidirectional bone vectors from skeleton sequences,and employs a three-stream graph convolu-tional structure to extract features that describe human movement.Experimental results on the NTU-RGB+D dataset demonstrate that DM-AGCN achieves an accuracy of 89.4%on the X-Sub and 95.8%on the X-View,while reducing model complexity to 3.68 GFLOPs(Giga Floating-point Oper-ations Per Second).On the Kinetics-Skeleton dataset,the model achieves a Top-1 accuracy of 37.2%and a Top-5 accuracy of 60.3%,further validating its effectiveness across different benchmarks.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
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
Reliable human action recognition(HAR)in video sequences is critical for a wide range of applications,such as security surveillance,healthcare monitoring,and human-computer interaction.Several automated systems have b...Reliable human action recognition(HAR)in video sequences is critical for a wide range of applications,such as security surveillance,healthcare monitoring,and human-computer interaction.Several automated systems have been designed for this purpose;however,existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks(CNNs),which limits their accuracy in discriminating numerous human actions.Therefore,this study introduces a novel deeplearning framework called theARNet,designed for robustHAR.ARNet consists of two mainmodules,namely,a refined InceptionResNet-V2-based CNN and a Bi-LSTM(Long Short-Term Memory)network.The refined InceptionResNet-V2 employs a parametric rectified linear unit(PReLU)activation strategy within convolutional layers to enhance spatial feature extraction fromindividual video frames.The inclusion of the PReLUmethod improves the spatial informationcapturing ability of the approach as it uses learnable parameters to adaptively control the slope of the negative part of the activation function,allowing richer gradient flow during backpropagation and resulting in robust information capturing and stable model training.These spatial features holding essential pixel characteristics are then processed by the Bi-LSTMmodule for temporal analysis,which assists the ARNet in understanding the dynamic behavior of actions over time.The ARNet integrates three additional dense layers after the Bi-LSTM module to ensure a comprehensive computation of both spatial and temporal patterns and further boost the feature representation.The experimental validation of the model is conducted on 3 benchmark datasets named HMDB51,KTH,and UCF Sports and reports accuracies of 93.82%,99%,and 99.16%,respectively.The Precision results of HMDB51,KTH,and UCF Sports datasets are 97.41%,99.54%,and 99.01%;the Recall values are 98.87%,98.60%,99.08%,and the F1-Score is 98.13%,99.07%,99.04%,respectively.These results highlight the robustness of the ARNet approach and its potential as a versatile tool for accurate HAR across various real-world applications.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
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
When the G20 was created in 1999 in the wake of the Asian financial crisis,few imagined it would one day become the nerve centre of global governance.Twenty-six years later,the G20 members,which represent 85 percent o...When the G20 was created in 1999 in the wake of the Asian financial crisis,few imagined it would one day become the nerve centre of global governance.Twenty-six years later,the G20 members,which represent 85 percent of the global GDP and two-thirds of the world population,are once again navigating a turbulent era marked by geopolitical rivalry,economic fragmentation and widening inequality.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
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
Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in hum...Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.展开更多
基金Supported by National Natural Science Foundation of China,No.82374200Construction of Traditional Chinese Medicine Inheritance and Innovation Development Demonstration Pilot Projects in Pudong New Area-High-Level Research-Oriented Traditional Chinese Medicine Hospital Construction,No.YC-2023-0901.
文摘Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized byclinical symptoms of diarrhea and mucopurulent bloody stools, and its incidenceis increasing globally. The etiology and pathogenesis of UC remain elusive. Currenttherapeutic approaches, including anti-inflammatory, immunosuppressiveand immunomodulating agents, are often limited in efficacy and frequently associatedwith adverse drug reactions. Therefore, there is an urgent need to developsafer and more effective treatment strategies to address the limitations of existingtherapies. Scutellaria baicalensis Georgi (HQ), a traditional Chinese medicinal herb,has been employed in the treatment of UC for over 2000 years. Recent studieshave demonstrated that HQ contains multiple active components capable oftreating UC through anti-inflammation, immune modulation, intestinal barrierprotection, antioxidant activity, and regulation of the gut microbiota. This paperreviews recent studies on the mechanism of action and clinical trials of HQ intreating UC based on relevant literature, with the aim of providing valuable insightsinto future treatment approaches.
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.
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.
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.
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.
文摘The proposed paper deals with a numerical approach that could better assist the archaeologist in the archaeological reconstruction projects.The goal of our research is to explore and study the use of computerized tools in archaeological reconstruction projects of monumental architecture in order to propose new ways in which such technology can be used.
基金Supported by the National Natural Science Foundation of China(No.62303163)the Science and Technology Key Project of Science and Technology Department of Henan Province(No.252102211041).
文摘Skeleton-based human action recognition focuses on identifying actions from dynamic skeletal data,which contains both temporal and spatial characteristics.However,this approach faces chal-lenges such as viewpoint variations,low recognition accuracy,and high model complexity.Skeleton-based graph convolutional network(GCN)generally outperform other deep learning methods in rec-ognition accuracy.However,they often underutilize temporal features and suffer from high model complexity,leading to increased training and validation costs,especially on large-scale datasets.This paper proposes a dual-channel graph convolutional network with multi-order information fusion(DM-AGCN)for human action recognition.The network integrates high frame rate skeleton chan-nels to capture action dynamics and low frame rate channels to preserve static semantic information,effectively balancing temporal and spatial features.This dual-channel architecture allows for separate processing of temporal and spatial information.Additionally,DM-AGCN extracts joint keypoints and bidirectional bone vectors from skeleton sequences,and employs a three-stream graph convolu-tional structure to extract features that describe human movement.Experimental results on the NTU-RGB+D dataset demonstrate that DM-AGCN achieves an accuracy of 89.4%on the X-Sub and 95.8%on the X-View,while reducing model complexity to 3.68 GFLOPs(Giga Floating-point Oper-ations Per Second).On the Kinetics-Skeleton dataset,the model achieves a Top-1 accuracy of 37.2%and a Top-5 accuracy of 60.3%,further validating its effectiveness across different benchmarks.
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(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supported and funded by theDeanship of Scientific Research at ImamMohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Reliable human action recognition(HAR)in video sequences is critical for a wide range of applications,such as security surveillance,healthcare monitoring,and human-computer interaction.Several automated systems have been designed for this purpose;however,existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks(CNNs),which limits their accuracy in discriminating numerous human actions.Therefore,this study introduces a novel deeplearning framework called theARNet,designed for robustHAR.ARNet consists of two mainmodules,namely,a refined InceptionResNet-V2-based CNN and a Bi-LSTM(Long Short-Term Memory)network.The refined InceptionResNet-V2 employs a parametric rectified linear unit(PReLU)activation strategy within convolutional layers to enhance spatial feature extraction fromindividual video frames.The inclusion of the PReLUmethod improves the spatial informationcapturing ability of the approach as it uses learnable parameters to adaptively control the slope of the negative part of the activation function,allowing richer gradient flow during backpropagation and resulting in robust information capturing and stable model training.These spatial features holding essential pixel characteristics are then processed by the Bi-LSTMmodule for temporal analysis,which assists the ARNet in understanding the dynamic behavior of actions over time.The ARNet integrates three additional dense layers after the Bi-LSTM module to ensure a comprehensive computation of both spatial and temporal patterns and further boost the feature representation.The experimental validation of the model is conducted on 3 benchmark datasets named HMDB51,KTH,and UCF Sports and reports accuracies of 93.82%,99%,and 99.16%,respectively.The Precision results of HMDB51,KTH,and UCF Sports datasets are 97.41%,99.54%,and 99.01%;the Recall values are 98.87%,98.60%,99.08%,and the F1-Score is 98.13%,99.07%,99.04%,respectively.These results highlight the robustness of the ARNet approach and its potential as a versatile tool for accurate HAR across various real-world applications.
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.
文摘When the G20 was created in 1999 in the wake of the Asian financial crisis,few imagined it would one day become the nerve centre of global governance.Twenty-six years later,the G20 members,which represent 85 percent of the global GDP and two-thirds of the world population,are once again navigating a turbulent era marked by geopolitical rivalry,economic fragmentation and widening inequality.
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.
基金Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)the Competitive Research Fund of The University of Aizu,Japan.
文摘Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.
