Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
The combination of Daphnes Cortex(DC)and Liquorice Root(LR),two traditional Chinese medicinal herbs,has shown significant therapeutic effects on rheumatoid arthritis(RA),but its synergistic mechanism of action remains...The combination of Daphnes Cortex(DC)and Liquorice Root(LR),two traditional Chinese medicinal herbs,has shown significant therapeutic effects on rheumatoid arthritis(RA),but its synergistic mechanism of action remains to be elucidated.Employing a network pharmacology and molecular docking approach,this study systematically investigated the synergistic mechanism of the herb pair DC and LR in RA treatment.Active components and their corresponding targets were retrieved from the TCMSP database and relevant literature,and RA-related targets were collected from established disease databases.A total of 73 overlapping targets between DC-LR and RA were identified,among which core targets such as AKT1,TNF,and CASP3 were highlighted.GO and KEGG enrichment analyses revealed that these targets are involved in biological processes such as oxidative stress response and cell migration,and are significantly enriched in key pathways including HIF-1,TNF,and PI3K-Akt signaling pathways.Compatibility analysis further revealed that the combination of DC and LR may enhance therapeutic effects through synergistic regulation of shared targets and complementary modulation of upstream and downstream pathway components.Molecular docking confirmed strong binding affinities between core active components and key targets.This study provides a multi-dimensional“component-target-pathway”perspective on the potential synergistic anti-RA mechanism of the DC-LR herb pair,offering a theoretical basis for further experimental validation and clinical application.展开更多
End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods th...End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.展开更多
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
Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the clou...Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.展开更多
Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available....Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.展开更多
Offshore structures are constantly subjected to the complex forces of the marine environment,including wind,sea waves,currents,and seismic loadings.Among these,wind and sea wave forces persist throughout the structure...Offshore structures are constantly subjected to the complex forces of the marine environment,including wind,sea waves,currents,and seismic loadings.Among these,wind and sea wave forces persist throughout the structure’s lifetime.This study proposes a dynamic analysis approach that incorporates both time and frequency domain methods to investigate the structural responses of offshore structures under the combined effects of wind and wave forces.A wind wave-pier coupling dynamic model is first developed using a small-scale single pier,with corresponding dynamic equilibrium equations established.Fluctuating wind and sea waves are simulated using the weighted amplitude wave superposition(WAWS)method and linear superposition,respectively.Wind and wave load histories are then derived via Fourier transforms.The structural dynamic responses under different loading scenarios(wind only,wave only,and combined wind and wave)are analyzed using the Newmarkβmethod.Additionally,the effects of varying wind and wave parameters on structural responses are evaluated.The simulation results demonstrate that the structural responses to wind-wave coupling are smaller than the superimposed effects of wind and wave forces acting independently.When wind speeds are relatively low,wave forces dominate structural displacement and serve as the primary source of vibration.展开更多
Grifola frondosa(Maitake)is traditionally valued for its health benefits,with polysaccharides being key bioactive components.This paper investigates a specific subfraction,Fraction D(GFP-D),evaluating its clinical eff...Grifola frondosa(Maitake)is traditionally valued for its health benefits,with polysaccharides being key bioactive components.This paper investigates a specific subfraction,Fraction D(GFP-D),evaluating its clinical effects and mechanisms in immune enhancement,adjunctive anti-tumor activity,and regulation of glucose/lipid metabolism.Three clinical trials were conducted.In an immune study,120 healthy volunteers(CD4+T cell count 500–1000 cells/μL)received 150 mg/day GFP-D for 8 weeks,resulting in significant increases in CD4+T cells(from 632±95 to 812±108 cells/μL,28.5%increase,within the physiological activation range),CD4+/CD8+ratio,NK cell activity,IL-2,and IFN-γ(all P<0.001 vs.placebo).An anti-tumor study with 80 advanced cancer patients(stratified by age,tumor stage,and histotype)showed that adding 1000 mg/day GFP-D to chemotherapy improved objective response rate(52.5%vs.30.0%,P=0.036,95%CI:1.02–3.87),one-year progression-free survival(55.8%vs.33.3%,P=0.022),and preserved immune parameters versus chemotherapy alone.A metabolic study in 90 type 2 diabetes patients found that 400 mg/day GFP-D for 12 weeks significantly lowered fasting glucose,HbA1c,total cholesterol,triglycerides,and LDL-C,while raising HDL-C(from 1.0±0.2 to 1.2±0.2 mmol/L,20%increase,supported by increased AMPK phosphorylation).Mechanistically,immune enhancement involves macrophage/dendritic cell activation via Dectin-1/TLR4 receptors(confirmed by increased receptor expression and downstream signaling molecules),promoting cytokine-driven T/NK cell responses.Anti-tumor effects stem from immunomodulation,direct induction of cancer cell apoptosis(via mitochondrial/caspase pathways,verified by increased Bax/Bcl-2 ratio and caspase-3 activation),and angiogenesis inhibition by downregulating VEGF.Metabolic benefits are linked to AMPK pathway activation in liver/muscle(confirmed by increased p-AMPK/AMPK ratio),enhancing glucose uptake and inhibiting gluconeogenesis/lipogenesis,alongside modulation of gut microbiota(increased Bifidobacterium and Lactobacillus abundance).All trials reported no severe adverse events related to GFP-D;liver/kidney function parameters(ALT,AST,creatinine,urea nitrogen)remained within normal ranges throughout the intervention.Collectively,GFP-D emerges as a multi-functional bioactive agent with substantial therapeutic potential.展开更多
The supervisory control problem for discrete event system(DES) under control involves identifying the supervisor, if one exists, which, when synchronously composed with the DES,results in a system that conforms to the...The supervisory control problem for discrete event system(DES) under control involves identifying the supervisor, if one exists, which, when synchronously composed with the DES,results in a system that conforms to the control specification. In this context, we consider a non-deterministic DES under complete observation and control specification expressed in action-based propositional μ-calculus. The key to our solution is the process of quotienting the control specification against the plan resulting in a new μ-calculus formula such that a model for the formula is the supervisor. Thus the task of control synthesis is reduced a problem of μ-calculus satisfiability. In contrast to the existing μ-calculus quotienting-based techniques that are developed in deterministic setting, our quotienting rules can handle nondeterminism in the plant models. Another distinguishing feature of our technique is that while existing techniques use a separate μ-calculus formula to describe the controllability constraint(that uncontrollable events of plants are never disabled by a supervisor), we absorb this constraint as part of quotienting which allows us to directly capture more general state-dependent controllability constraints. Finally, we develop a tableau-based technique for verifying satisfiability of quotiented formula and model generation. The runtime for the technique is exponential in terms of the size of the plan and the control specification. A better complexity result that is polynomial to plant size and exponential to specification size is obtained when the controllability property is state-independent. A prototype implementation in a tabled logic programming language as well as some experimental results are presented.展开更多
The spectrum allocation for cognitive radio networks(CRNs) has received considerable studies under the assumption that the bandwidth of spectrum holes is static. However, in practice, the bandwidth of spectrum holes i...The spectrum allocation for cognitive radio networks(CRNs) has received considerable studies under the assumption that the bandwidth of spectrum holes is static. However, in practice, the bandwidth of spectrum holes is time-varied due to primary user/secondary user(PU/SU) activity and mobility, which result in non-determinacy. This paper studies the spectrum allocation for CRNs with non-deterministic bandwidth of spectrum holes. We present a novel probability density function(PDF) through order statistics as well as its simplified form to describe the statistical properties of spectrum holes, with which a statistical spectrum allocation model based on stochastic multiple knapsack problem(MKP) is formulated for spectrum allocation with non-deterministic bandwidth of spectrum holes. To reduce the computational complexity, we transform this stochastic programming problem into a constant MKP through exploiting the properties of cumulative distribution function(CDF), which can be solved via MTHG algorithm by using auxiliary variables. Simulation results illustrate that the proposed statistical spectrum allocation algorithm can achieve better performance compared with the existing algorithms when the bandwidth of spectrum holes is time-varied.展开更多
Watson Crick automata are finite automata working on double strands. Extensive research work has already been done on non deterministic Watson Crick automata and on deterministic Watson Crick automata. Parallel Commun...Watson Crick automata are finite automata working on double strands. Extensive research work has already been done on non deterministic Watson Crick automata and on deterministic Watson Crick automata. Parallel Communicating Watson Crick automata systems have been introduced by E. Czeziler et al. In this paper we discuss about a variant of Watson Crick automata known as the two-way Watson Crick automata which are more powerful than non-deterministic Watson Crick automata. We also establish the equivalence of different subclasses of two-way Watson crick automata. We further show that recursively enumerable (RE) languages can be realized by an image of generalized sequential machine (gsm) mapping of two-way Watson-Crick automata.展开更多
Modeling and forecasting of the groundwater water table are a major component of effective planning and management of water resources. One way to predict the groundwater level is analysis using a non-deterministic mod...Modeling and forecasting of the groundwater water table are a major component of effective planning and management of water resources. One way to predict the groundwater level is analysis using a non-deterministic model. This study assessed the performance of such models in predicting the groundwater level at Kashan aquifer. Data from 36 piezometer wells in Kashan aquifer for 1999 to 2010 were used. The desired statistical interval was divided into two parts and statistics for 1990 to 2004 were used for modeling and statistics from 2005 to 2010 were used for valediction of the model. The Akaike criterion and correlation coefficients were used to determine the accuracy of the prediction models. The results indicated that the AR(2) model more accurately predicted ground water level in the plains;using this model, the groundwater water table was predicted for up to 60 mo.展开更多
In this paper, we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur. In particular, we focus on the problem of liveness enforcement and consider a sensor-reading m...In this paper, we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur. In particular, we focus on the problem of liveness enforcement and consider a sensor-reading modification attack(SM-attack) that may disguise the occurrence of an event as that of another event by intruding sensor communication channels. To solve the problem, we introduce non-deterministic supervisors in the paper, which associate to every observed sequence a set of possible control actions offline and choose a control action from the set randomly online to control the system. Specifically, given a bounded Petri net(PN) as the reference formalism and an SMattack, an algorithm that synthesizes a liveness-enforcing nondeterministic supervisor tolerant to the SM-attack is proposed for the first time.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
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
The recently experienced hype concerning the so-called “4<sup>th</sup> Industrial Revolution” of production systems has prompted several papers of various subtopics regarding Cyber-Phdysical Production S...The recently experienced hype concerning the so-called “4<sup>th</sup> Industrial Revolution” of production systems has prompted several papers of various subtopics regarding Cyber-Phdysical Production Systems (CPPS). However, important aspects such as the modelling of CPPS to understand the theory regarding the performance of highly non-ergodic and non-deterministic flexible manufacturing systems in terms of Exit Rate (ER), Manufacturing Lead Time (MLT), and On-Time Delivery (OTD) have not yet been examined systematically and even less modeled analytically. To develop the topic, in this paper, the prerequisites for modelling such systems are defined in order to be able to derive an explicit and dedicated production mathematics-based understanding of CPPS and its dynamics: switching from explorative simulation to rational modelling of the manufacturing “physics” led to an own and specific manufacturing theory. The findings have led to enouncing, among others, the Theorem of Non-Ergodicity as well as the Batch Cycle Time Deviation Function giving important insights to model digital twin-based CPPS for complying with the mandatory OTD.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
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金supported by National Training Program of Innovation and Entrepreneurship for Undergraduates(202510163044).
文摘The combination of Daphnes Cortex(DC)and Liquorice Root(LR),two traditional Chinese medicinal herbs,has shown significant therapeutic effects on rheumatoid arthritis(RA),but its synergistic mechanism of action remains to be elucidated.Employing a network pharmacology and molecular docking approach,this study systematically investigated the synergistic mechanism of the herb pair DC and LR in RA treatment.Active components and their corresponding targets were retrieved from the TCMSP database and relevant literature,and RA-related targets were collected from established disease databases.A total of 73 overlapping targets between DC-LR and RA were identified,among which core targets such as AKT1,TNF,and CASP3 were highlighted.GO and KEGG enrichment analyses revealed that these targets are involved in biological processes such as oxidative stress response and cell migration,and are significantly enriched in key pathways including HIF-1,TNF,and PI3K-Akt signaling pathways.Compatibility analysis further revealed that the combination of DC and LR may enhance therapeutic effects through synergistic regulation of shared targets and complementary modulation of upstream and downstream pathway components.Molecular docking confirmed strong binding affinities between core active components and key targets.This study provides a multi-dimensional“component-target-pathway”perspective on the potential synergistic anti-RA mechanism of the DC-LR herb pair,offering a theoretical basis for further experimental validation and clinical application.
基金supported by the National Natural Science Foundation of China(Grant No.62266054)the Major Science and Technology Project of Yunnan Province(Grant No.202402AD080002)the Scientific Research Fund of the Yunnan Provincial Department of Education(Grant No.2025Y0302).
文摘End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.
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 Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R896).
文摘Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.
文摘Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.
基金Project(2022YFB2302700)supported by the National Key Research and Development Program of China。
文摘Offshore structures are constantly subjected to the complex forces of the marine environment,including wind,sea waves,currents,and seismic loadings.Among these,wind and sea wave forces persist throughout the structure’s lifetime.This study proposes a dynamic analysis approach that incorporates both time and frequency domain methods to investigate the structural responses of offshore structures under the combined effects of wind and wave forces.A wind wave-pier coupling dynamic model is first developed using a small-scale single pier,with corresponding dynamic equilibrium equations established.Fluctuating wind and sea waves are simulated using the weighted amplitude wave superposition(WAWS)method and linear superposition,respectively.Wind and wave load histories are then derived via Fourier transforms.The structural dynamic responses under different loading scenarios(wind only,wave only,and combined wind and wave)are analyzed using the Newmarkβmethod.Additionally,the effects of varying wind and wave parameters on structural responses are evaluated.The simulation results demonstrate that the structural responses to wind-wave coupling are smaller than the superimposed effects of wind and wave forces acting independently.When wind speeds are relatively low,wave forces dominate structural displacement and serve as the primary source of vibration.
文摘Grifola frondosa(Maitake)is traditionally valued for its health benefits,with polysaccharides being key bioactive components.This paper investigates a specific subfraction,Fraction D(GFP-D),evaluating its clinical effects and mechanisms in immune enhancement,adjunctive anti-tumor activity,and regulation of glucose/lipid metabolism.Three clinical trials were conducted.In an immune study,120 healthy volunteers(CD4+T cell count 500–1000 cells/μL)received 150 mg/day GFP-D for 8 weeks,resulting in significant increases in CD4+T cells(from 632±95 to 812±108 cells/μL,28.5%increase,within the physiological activation range),CD4+/CD8+ratio,NK cell activity,IL-2,and IFN-γ(all P<0.001 vs.placebo).An anti-tumor study with 80 advanced cancer patients(stratified by age,tumor stage,and histotype)showed that adding 1000 mg/day GFP-D to chemotherapy improved objective response rate(52.5%vs.30.0%,P=0.036,95%CI:1.02–3.87),one-year progression-free survival(55.8%vs.33.3%,P=0.022),and preserved immune parameters versus chemotherapy alone.A metabolic study in 90 type 2 diabetes patients found that 400 mg/day GFP-D for 12 weeks significantly lowered fasting glucose,HbA1c,total cholesterol,triglycerides,and LDL-C,while raising HDL-C(from 1.0±0.2 to 1.2±0.2 mmol/L,20%increase,supported by increased AMPK phosphorylation).Mechanistically,immune enhancement involves macrophage/dendritic cell activation via Dectin-1/TLR4 receptors(confirmed by increased receptor expression and downstream signaling molecules),promoting cytokine-driven T/NK cell responses.Anti-tumor effects stem from immunomodulation,direct induction of cancer cell apoptosis(via mitochondrial/caspase pathways,verified by increased Bax/Bcl-2 ratio and caspase-3 activation),and angiogenesis inhibition by downregulating VEGF.Metabolic benefits are linked to AMPK pathway activation in liver/muscle(confirmed by increased p-AMPK/AMPK ratio),enhancing glucose uptake and inhibiting gluconeogenesis/lipogenesis,alongside modulation of gut microbiota(increased Bifidobacterium and Lactobacillus abundance).All trials reported no severe adverse events related to GFP-D;liver/kidney function parameters(ALT,AST,creatinine,urea nitrogen)remained within normal ranges throughout the intervention.Collectively,GFP-D emerges as a multi-functional bioactive agent with substantial therapeutic potential.
基金supported in part by the National Sci-ence Foundation (NSF-ECCS-1509420, NSF-CSSI-2004766)。
文摘The supervisory control problem for discrete event system(DES) under control involves identifying the supervisor, if one exists, which, when synchronously composed with the DES,results in a system that conforms to the control specification. In this context, we consider a non-deterministic DES under complete observation and control specification expressed in action-based propositional μ-calculus. The key to our solution is the process of quotienting the control specification against the plan resulting in a new μ-calculus formula such that a model for the formula is the supervisor. Thus the task of control synthesis is reduced a problem of μ-calculus satisfiability. In contrast to the existing μ-calculus quotienting-based techniques that are developed in deterministic setting, our quotienting rules can handle nondeterminism in the plant models. Another distinguishing feature of our technique is that while existing techniques use a separate μ-calculus formula to describe the controllability constraint(that uncontrollable events of plants are never disabled by a supervisor), we absorb this constraint as part of quotienting which allows us to directly capture more general state-dependent controllability constraints. Finally, we develop a tableau-based technique for verifying satisfiability of quotiented formula and model generation. The runtime for the technique is exponential in terms of the size of the plan and the control specification. A better complexity result that is polynomial to plant size and exponential to specification size is obtained when the controllability property is state-independent. A prototype implementation in a tabled logic programming language as well as some experimental results are presented.
基金supported by the National Natural Science Foundation of China (No.61501065, 91438104,No.61571069 and No.61601067)the Fundamental Research Funds for the Central Universities (No.106112015CDJXY160002,No.106112016CDJXY160001)the Chongqing Research Program of Basic Research and Frontier Technology (No.CSTC2016JCYJA0021)
文摘The spectrum allocation for cognitive radio networks(CRNs) has received considerable studies under the assumption that the bandwidth of spectrum holes is static. However, in practice, the bandwidth of spectrum holes is time-varied due to primary user/secondary user(PU/SU) activity and mobility, which result in non-determinacy. This paper studies the spectrum allocation for CRNs with non-deterministic bandwidth of spectrum holes. We present a novel probability density function(PDF) through order statistics as well as its simplified form to describe the statistical properties of spectrum holes, with which a statistical spectrum allocation model based on stochastic multiple knapsack problem(MKP) is formulated for spectrum allocation with non-deterministic bandwidth of spectrum holes. To reduce the computational complexity, we transform this stochastic programming problem into a constant MKP through exploiting the properties of cumulative distribution function(CDF), which can be solved via MTHG algorithm by using auxiliary variables. Simulation results illustrate that the proposed statistical spectrum allocation algorithm can achieve better performance compared with the existing algorithms when the bandwidth of spectrum holes is time-varied.
文摘Watson Crick automata are finite automata working on double strands. Extensive research work has already been done on non deterministic Watson Crick automata and on deterministic Watson Crick automata. Parallel Communicating Watson Crick automata systems have been introduced by E. Czeziler et al. In this paper we discuss about a variant of Watson Crick automata known as the two-way Watson Crick automata which are more powerful than non-deterministic Watson Crick automata. We also establish the equivalence of different subclasses of two-way Watson crick automata. We further show that recursively enumerable (RE) languages can be realized by an image of generalized sequential machine (gsm) mapping of two-way Watson-Crick automata.
文摘Modeling and forecasting of the groundwater water table are a major component of effective planning and management of water resources. One way to predict the groundwater level is analysis using a non-deterministic model. This study assessed the performance of such models in predicting the groundwater level at Kashan aquifer. Data from 36 piezometer wells in Kashan aquifer for 1999 to 2010 were used. The desired statistical interval was divided into two parts and statistics for 1990 to 2004 were used for modeling and statistics from 2005 to 2010 were used for valediction of the model. The Akaike criterion and correlation coefficients were used to determine the accuracy of the prediction models. The results indicated that the AR(2) model more accurately predicted ground water level in the plains;using this model, the groundwater water table was predicted for up to 60 mo.
基金supported in part by the Public Technology Research Plan of Zhejiang Province (LGJ21F030001)the National Natural Science Foundation of China (62302448)the Zhejiang Provincial Key Laboratory of New Network Standards and Technologies (2013E10012)。
文摘In this paper, we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur. In particular, we focus on the problem of liveness enforcement and consider a sensor-reading modification attack(SM-attack) that may disguise the occurrence of an event as that of another event by intruding sensor communication channels. To solve the problem, we introduce non-deterministic supervisors in the paper, which associate to every observed sequence a set of possible control actions offline and choose a control action from the set randomly online to control the system. Specifically, given a bounded Petri net(PN) as the reference formalism and an SMattack, an algorithm that synthesizes a liveness-enforcing nondeterministic supervisor tolerant to the SM-attack is proposed for the first time.
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The recently experienced hype concerning the so-called “4<sup>th</sup> Industrial Revolution” of production systems has prompted several papers of various subtopics regarding Cyber-Phdysical Production Systems (CPPS). However, important aspects such as the modelling of CPPS to understand the theory regarding the performance of highly non-ergodic and non-deterministic flexible manufacturing systems in terms of Exit Rate (ER), Manufacturing Lead Time (MLT), and On-Time Delivery (OTD) have not yet been examined systematically and even less modeled analytically. To develop the topic, in this paper, the prerequisites for modelling such systems are defined in order to be able to derive an explicit and dedicated production mathematics-based understanding of CPPS and its dynamics: switching from explorative simulation to rational modelling of the manufacturing “physics” led to an own and specific manufacturing theory. The findings have led to enouncing, among others, the Theorem of Non-Ergodicity as well as the Batch Cycle Time Deviation Function giving important insights to model digital twin-based CPPS for complying with the mandatory OTD.
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.
