BACKGROUND Skin wounds are common injuries that affect quality of life and incur high costs.A considerable portion of healthcare resources in Western countries is allocated to wound treatment,mainly using mechanical,b...BACKGROUND Skin wounds are common injuries that affect quality of life and incur high costs.A considerable portion of healthcare resources in Western countries is allocated to wound treatment,mainly using mechanical,biological,or artificial dressings.Biological and artificial dressings,such as hydrogels,are preferred for their biocompatibility.Platelet concentrates,such as platelet-rich plasma(PRP)and platelet-rich fibrin(PRF),stand out for accelerating tissue repair and minimizing risks of allergies and rejection.This study developed PRF and PRP-based dressings to treat skin wounds in an animal model,evaluating their functionality and efficiency in accelerating the tissue repair process.AIM To develop wound dressings based on platelet concentrates and evaluating their efficiency in treating skin wounds in Wistar rats.METHODS Wistar rats,both male and female,were subjected to the creation of a skin wound,distributed into groups(n=64/group),and treated with Carbopol(negative control);PRP+Carbopol;PRF+Carbopol;or PRF+CaCl_(2)+Carbopol,on days zero(D0),D3,D7,D14,and D21.PRP and PRF were obtained only from male rats.On D3,D7,D14,and D21,the wounds were analyzed for area,contraction rate,and histopathology of the tissue repair process.RESULTS The PRF-based dressing was more effective in accelerating wound closure early in the tissue repair process(up to D7),while PRF+CaCl_(2) seemed to delay the process,as wound closure was not complete by D21.Regarding macroscopic parameters,animals treated with PRF+CaCl_(2) showed significantly more crusting(necrosis)early in the repair process(D3).In terms of histopathological parameters,the PRF group exhibited significant collagenization at the later stages of the repair process(D14 and D21).By D21,fibroblast proliferation and inflammatory infiltration were higher in the PRP group.Animals treated with PRF+CaCl_(2) experienced a more pronounced inflammatory response up to D7,which diminished from D14 onwards.CONCLUSION The PRF-based dressing was effective in accelerating the closure of cutaneous wounds in Wistar rats early in the process and in aiding tissue repair at the later stages.展开更多
A kinetic moment-closed model(KMCM), derived from the Vlasov–Fokker–Planck(VFP) equation with spherically symmetric velocity space, is introduced as a general relaxation model for homogeneous plasmas. The closed for...A kinetic moment-closed model(KMCM), derived from the Vlasov–Fokker–Planck(VFP) equation with spherically symmetric velocity space, is introduced as a general relaxation model for homogeneous plasmas. The closed form of this model is presented by introducing a set of new functions called R function and R integration. This nonlinear model, based on the finitely distinguishable independent features(FDIF) hypothesis, enables the capture of the nature of the equilibrium state and non-equilibrium state. From this relaxation model, a general temperature relaxation model is derived when the velocity space exhibits spherical symmetry, and the general characteristic frequency of temperature relaxation is presented.展开更多
Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on se...Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.展开更多
A prior observational study indicated an asymmetric link between sea surface temperature(SST)in the Tasman Sea and ENSO during austral summer.Specifically,El Niño is associated with a dipolar SST anomaly pattern,...A prior observational study indicated an asymmetric link between sea surface temperature(SST)in the Tasman Sea and ENSO during austral summer.Specifically,El Niño is associated with a dipolar SST anomaly pattern,featuring warming in the northwest and cooling in the southeast,whereas La Niña corresponds to basin-scale warming.This study employs the experiments of coupled models from the sixth phase of the Coupled Model Intercomparison Project(CMIP6)to assess ENSO’s impact on Tasman Sea SST.While all 15 models capture the observed dipolar SST anomalies(SSTAs)in the Tasman Sea during El Niño years,only 7 models capture the basin-scale warmth in the Tasman Sea during La Niña years.Consequently,the models are bifurcated into two groups:group-one models yield one physically reasonable asymmetric connection as observed,including the asymmetry of oceanic heat transport,especially the Ekman meridional transport anomalies induced by zonal wind stress driven by the asymmetric atmospheric circulation over the Tasman Sea.However,due to abnormal responses to ENSO and systematic biases in model simulations,including jet and storm tracks,oceanic heat fluxes,ocean currents,and SST,the group-two models fail to reproduce the asymmetric connection between the Tasman Sea and ENSO.This study not only validates the observational asymmetric connection of SSTAs in the Tasman Sea with respect to the two opposite ENSO phases,but also provides evidence and clues to reduce the bias in group-two models.展开更多
We derive the transport equations from the Vlasov–Fokker–Planck equation when the velocity space is spherically symmetric.The Shkarofsky's form of Fokker–Planck–Rosenbluth collision operator is employed in the...We derive the transport equations from the Vlasov–Fokker–Planck equation when the velocity space is spherically symmetric.The Shkarofsky's form of Fokker–Planck–Rosenbluth collision operator is employed in the Vlasov–Fokker–Planck equation.A closed-form relaxation model for homogeneous plasmas could be presented in terms of Gauss hypergeometric2F1functions.This has been accomplished based on the Maxwellian mixture model.Furthermore,we demonstrate that classic models such as two-temperature thermal equilibrium model and thermodynamic equilibrium model are special cases of our relaxation model and the zeroth-order Braginskii heat transfer model can also be derived.The present relaxation model is a nonequilibrium model based on the hypothesis that the plasmas system possesses finitely distinguishable independent features,without relying on the conventional near-equilibrium assumption.展开更多
Background:Nonalcoholic fatty liver disease(NAFLD)is a global health concern with the acid sphingomyelinase(ASM)/ceramide(CE)pathway and the NOD-like receptor family,pyrin domain-containing protein 3(NLRP3)inflammasom...Background:Nonalcoholic fatty liver disease(NAFLD)is a global health concern with the acid sphingomyelinase(ASM)/ceramide(CE)pathway and the NOD-like receptor family,pyrin domain-containing protein 3(NLRP3)inflammasome identified as pivotal players in lipid disorders and inflammation.This study explores the interaction mechanism between the ASM/CE pathway and NLRP3 in NAFLD cell models,aiming to understand the impact of amitriptyline(Ami),an ASM inhibitor,on lipid deposition and hepatocyte injury by regulating the ASM/CE-NLRP3 pathway.Methods:HepG2 and HL-7702 cells were exposed to free fatty acids(FFAs)to establish the NAFLD model.The cells were divided into 5 groups:control group,model group,Ami group,tumor necrosis factoralpha(TNF-α)group,and Ami+TNF-αgroup.Intracellular lipid droplets were visualized using Oil Red O staining,and Western blot analysis quantified ASM,NLRP3,and caspase 1 protein expression.Enzyme linked immunosorbent assay(ELISA)was measured CE and ASM levels,while qRT-PCR assessed mRNA expression.The apoptotic rate was evaluated by flow cytometry(FCM).Results:Following FFAs incubation,significant increases in ASM and CE levels were observed in HepG2 and HL-7702 cells,accompanied by elevated expression of NLRP3,and caspase 1,and IL-1β.TNF-αtreatment further amplified these indicators.Ami demonstrated a reduction in lipid deposition,suppressed ASM/CE pathway activation,downregulated NLRP3 and caspase 1 expression,and improved apoptosis.Additionally,MCC950,a selective inhibitor of the NLRP3,mitigated NLRP3,caspase 1,and IL-1βexpression,alleviating lipid deposition and apoptosis in the NAFLD cell model.Conclusion:The ASM/CE-NLRP3 pathway in NAFLD cells promotes hepatocyte steatosis,inflammation,and cell damage.Ami emerges as a promising therapeutic agent by inhibiting the ASM/CE-NLRP3 pathway,underscoring its potential as a key target for NAFLD treatment.展开更多
BACKGROUND Lymph node ratio(LNR)was demonstrated to play a crucial role in the prognosis of many tumors.However,research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm(NEN)pati...BACKGROUND Lymph node ratio(LNR)was demonstrated to play a crucial role in the prognosis of many tumors.However,research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm(NEN)patients was limited.AIM To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.METHODS A total of 286 patients from the Surveillance,Epidemiology,and End Results database were divided into the training set and validation set at a ratio of 8:2.92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set.Cox regression analysis was used to explore the relationship between LNR and disease-specific survival(DSS)of gastric NEN patients.Random survival forest(RSF)algorithm and Cox proportional hazards(CoxPH)analysis were applied to develop models to predict DSS respectively,and compared with the 8th edition American Joint Committee on Cancer(AJCC)tumornode-metastasis(TNM)staging.RESULTS Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death.The RSF model exhibited the best performance in predicting DSS,with the C-index in the test set being 0.769[95%confidence interval(CI):0.691-0.846]outperforming the CoxPH model(0.744,95%CI:0.665-0.822)and the 8th edition AJCC TNM staging(0.723,95%CI:0.613-0.833).The calibration curves and decision curve analysis(DCA)demonstrated the RSF model had good calibration and clinical benefits.Furthermore,the RSF model could perform risk stratification and individual prognosis prediction effectively.CONCLUSION A higher LNR indicated a lower DSS in postoperative gastric NEN patients.The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set,showing potential in clinical practice.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
With the analysis of experiment and theory on GaN HEMT devices under DC sweep,an improved model for kink effect based on advanced SPICE model for high electron mobility transistors(ASM-HEMT)is pro⁃posed,considering th...With the analysis of experiment and theory on GaN HEMT devices under DC sweep,an improved model for kink effect based on advanced SPICE model for high electron mobility transistors(ASM-HEMT)is pro⁃posed,considering the relationship between the drain/gate-source voltage and kink effect.The improved model can not only accurately describe the trend of the drain-source current with the current collapse and kink effect,but also precisely fit different values of drain-source voltages at which the kink effect occurs under different gatesource voltages.Furthermore,it well characterizes the DC characteristics of GaN devices in the full operating range,with the fitting error less than 3%.To further verify the accuracy and convergence of the improved model,a load-pull system is built in ADS.The simulated result shows that although both the original ASM-HEMT and the improved model predict the output power for the maximum power matching of GaN devices well,the im⁃proved model predicts the power-added efficiency for the maximum efficiency matching more accurately,with 4%improved.展开更多
Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendou...Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability.Herein,we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection(SISTIFD)to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques.It can fuse the spectra and plasma images in synchronization,derive the plasma parameters(total number density,plasma temperature,electron density,and other implicit factors),and provide accurate results.The experimental data demonstrate their excellent utility and capacity,with a reduction of 98%in evaluation indices(root mean square error,relative standard deviation,etc.)and an analysis frequency of 143 Hz(much faster than the mainstream detection frame rate of 1 Hz).In addition,as a completely end-to-end and self-supervised framework,the SISTIFD enables automatic detection without manual preprocessing or intervention.With these advantages,it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry,especially in the regions that require both capability and efficiency.This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput,cross-interference,various analyte complexity,and diverse applications.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
文摘BACKGROUND Skin wounds are common injuries that affect quality of life and incur high costs.A considerable portion of healthcare resources in Western countries is allocated to wound treatment,mainly using mechanical,biological,or artificial dressings.Biological and artificial dressings,such as hydrogels,are preferred for their biocompatibility.Platelet concentrates,such as platelet-rich plasma(PRP)and platelet-rich fibrin(PRF),stand out for accelerating tissue repair and minimizing risks of allergies and rejection.This study developed PRF and PRP-based dressings to treat skin wounds in an animal model,evaluating their functionality and efficiency in accelerating the tissue repair process.AIM To develop wound dressings based on platelet concentrates and evaluating their efficiency in treating skin wounds in Wistar rats.METHODS Wistar rats,both male and female,were subjected to the creation of a skin wound,distributed into groups(n=64/group),and treated with Carbopol(negative control);PRP+Carbopol;PRF+Carbopol;or PRF+CaCl_(2)+Carbopol,on days zero(D0),D3,D7,D14,and D21.PRP and PRF were obtained only from male rats.On D3,D7,D14,and D21,the wounds were analyzed for area,contraction rate,and histopathology of the tissue repair process.RESULTS The PRF-based dressing was more effective in accelerating wound closure early in the tissue repair process(up to D7),while PRF+CaCl_(2) seemed to delay the process,as wound closure was not complete by D21.Regarding macroscopic parameters,animals treated with PRF+CaCl_(2) showed significantly more crusting(necrosis)early in the repair process(D3).In terms of histopathological parameters,the PRF group exhibited significant collagenization at the later stages of the repair process(D14 and D21).By D21,fibroblast proliferation and inflammatory infiltration were higher in the PRP group.Animals treated with PRF+CaCl_(2) experienced a more pronounced inflammatory response up to D7,which diminished from D14 onwards.CONCLUSION The PRF-based dressing was effective in accelerating the closure of cutaneous wounds in Wistar rats early in the process and in aiding tissue repair at the later stages.
基金supported by the Shuangchuang Ph.D Award (from World Prestigious Universities) (Grant No. JSSCBS20211303)Lianyungang Postdoctoral Science Foundation (Grant No. LYG20220014)the National Natural Science Foundation of China (Grant No.120051410)。
文摘A kinetic moment-closed model(KMCM), derived from the Vlasov–Fokker–Planck(VFP) equation with spherically symmetric velocity space, is introduced as a general relaxation model for homogeneous plasmas. The closed form of this model is presented by introducing a set of new functions called R function and R integration. This nonlinear model, based on the finitely distinguishable independent features(FDIF) hypothesis, enables the capture of the nature of the equilibrium state and non-equilibrium state. From this relaxation model, a general temperature relaxation model is derived when the velocity space exhibits spherical symmetry, and the general characteristic frequency of temperature relaxation is presented.
基金funding partly by the National Natural Science Foundation of China under grant number 61701179.
文摘Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF0805101)the National Natural Science Founda-tion of China(Grant Nos.42376250 and 42405068).
文摘A prior observational study indicated an asymmetric link between sea surface temperature(SST)in the Tasman Sea and ENSO during austral summer.Specifically,El Niño is associated with a dipolar SST anomaly pattern,featuring warming in the northwest and cooling in the southeast,whereas La Niña corresponds to basin-scale warming.This study employs the experiments of coupled models from the sixth phase of the Coupled Model Intercomparison Project(CMIP6)to assess ENSO’s impact on Tasman Sea SST.While all 15 models capture the observed dipolar SST anomalies(SSTAs)in the Tasman Sea during El Niño years,only 7 models capture the basin-scale warmth in the Tasman Sea during La Niña years.Consequently,the models are bifurcated into two groups:group-one models yield one physically reasonable asymmetric connection as observed,including the asymmetry of oceanic heat transport,especially the Ekman meridional transport anomalies induced by zonal wind stress driven by the asymmetric atmospheric circulation over the Tasman Sea.However,due to abnormal responses to ENSO and systematic biases in model simulations,including jet and storm tracks,oceanic heat fluxes,ocean currents,and SST,the group-two models fail to reproduce the asymmetric connection between the Tasman Sea and ENSO.This study not only validates the observational asymmetric connection of SSTAs in the Tasman Sea with respect to the two opposite ENSO phases,but also provides evidence and clues to reduce the bias in group-two models.
基金Project supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDB0500302 and LSKJ202300305)。
文摘We derive the transport equations from the Vlasov–Fokker–Planck equation when the velocity space is spherically symmetric.The Shkarofsky's form of Fokker–Planck–Rosenbluth collision operator is employed in the Vlasov–Fokker–Planck equation.A closed-form relaxation model for homogeneous plasmas could be presented in terms of Gauss hypergeometric2F1functions.This has been accomplished based on the Maxwellian mixture model.Furthermore,we demonstrate that classic models such as two-temperature thermal equilibrium model and thermodynamic equilibrium model are special cases of our relaxation model and the zeroth-order Braginskii heat transfer model can also be derived.The present relaxation model is a nonequilibrium model based on the hypothesis that the plasmas system possesses finitely distinguishable independent features,without relying on the conventional near-equilibrium assumption.
基金supported by the Initial Scientific Research Fund of the Talents Introduced in Nanjing Lishui People’s Hospital(Project 2021YJ02).
文摘Background:Nonalcoholic fatty liver disease(NAFLD)is a global health concern with the acid sphingomyelinase(ASM)/ceramide(CE)pathway and the NOD-like receptor family,pyrin domain-containing protein 3(NLRP3)inflammasome identified as pivotal players in lipid disorders and inflammation.This study explores the interaction mechanism between the ASM/CE pathway and NLRP3 in NAFLD cell models,aiming to understand the impact of amitriptyline(Ami),an ASM inhibitor,on lipid deposition and hepatocyte injury by regulating the ASM/CE-NLRP3 pathway.Methods:HepG2 and HL-7702 cells were exposed to free fatty acids(FFAs)to establish the NAFLD model.The cells were divided into 5 groups:control group,model group,Ami group,tumor necrosis factoralpha(TNF-α)group,and Ami+TNF-αgroup.Intracellular lipid droplets were visualized using Oil Red O staining,and Western blot analysis quantified ASM,NLRP3,and caspase 1 protein expression.Enzyme linked immunosorbent assay(ELISA)was measured CE and ASM levels,while qRT-PCR assessed mRNA expression.The apoptotic rate was evaluated by flow cytometry(FCM).Results:Following FFAs incubation,significant increases in ASM and CE levels were observed in HepG2 and HL-7702 cells,accompanied by elevated expression of NLRP3,and caspase 1,and IL-1β.TNF-αtreatment further amplified these indicators.Ami demonstrated a reduction in lipid deposition,suppressed ASM/CE pathway activation,downregulated NLRP3 and caspase 1 expression,and improved apoptosis.Additionally,MCC950,a selective inhibitor of the NLRP3,mitigated NLRP3,caspase 1,and IL-1βexpression,alleviating lipid deposition and apoptosis in the NAFLD cell model.Conclusion:The ASM/CE-NLRP3 pathway in NAFLD cells promotes hepatocyte steatosis,inflammation,and cell damage.Ami emerges as a promising therapeutic agent by inhibiting the ASM/CE-NLRP3 pathway,underscoring its potential as a key target for NAFLD treatment.
基金Supported by the Science and Technology Plan of Suzhou City,No.SKY2021038.
文摘BACKGROUND Lymph node ratio(LNR)was demonstrated to play a crucial role in the prognosis of many tumors.However,research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm(NEN)patients was limited.AIM To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.METHODS A total of 286 patients from the Surveillance,Epidemiology,and End Results database were divided into the training set and validation set at a ratio of 8:2.92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set.Cox regression analysis was used to explore the relationship between LNR and disease-specific survival(DSS)of gastric NEN patients.Random survival forest(RSF)algorithm and Cox proportional hazards(CoxPH)analysis were applied to develop models to predict DSS respectively,and compared with the 8th edition American Joint Committee on Cancer(AJCC)tumornode-metastasis(TNM)staging.RESULTS Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death.The RSF model exhibited the best performance in predicting DSS,with the C-index in the test set being 0.769[95%confidence interval(CI):0.691-0.846]outperforming the CoxPH model(0.744,95%CI:0.665-0.822)and the 8th edition AJCC TNM staging(0.723,95%CI:0.613-0.833).The calibration curves and decision curve analysis(DCA)demonstrated the RSF model had good calibration and clinical benefits.Furthermore,the RSF model could perform risk stratification and individual prognosis prediction effectively.CONCLUSION A higher LNR indicated a lower DSS in postoperative gastric NEN patients.The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set,showing potential in clinical practice.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金Supported by the National Key R&D Program of China(2022YFF0707800,2022YFF0707801)Primary Research&Development Plan of Jiangsu Province(BE2022070,BE2022070-2)。
文摘With the analysis of experiment and theory on GaN HEMT devices under DC sweep,an improved model for kink effect based on advanced SPICE model for high electron mobility transistors(ASM-HEMT)is pro⁃posed,considering the relationship between the drain/gate-source voltage and kink effect.The improved model can not only accurately describe the trend of the drain-source current with the current collapse and kink effect,but also precisely fit different values of drain-source voltages at which the kink effect occurs under different gatesource voltages.Furthermore,it well characterizes the DC characteristics of GaN devices in the full operating range,with the fitting error less than 3%.To further verify the accuracy and convergence of the improved model,a load-pull system is built in ADS.The simulated result shows that although both the original ASM-HEMT and the improved model predict the output power for the maximum power matching of GaN devices well,the im⁃proved model predicts the power-added efficiency for the maximum efficiency matching more accurately,with 4%improved.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0118700)the National Natural Science Foundation of China(Grant No.62375101)the Fundamental Research Funds for the Central Universities(Grant No.YCJJ20230216).
文摘Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability.Herein,we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection(SISTIFD)to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques.It can fuse the spectra and plasma images in synchronization,derive the plasma parameters(total number density,plasma temperature,electron density,and other implicit factors),and provide accurate results.The experimental data demonstrate their excellent utility and capacity,with a reduction of 98%in evaluation indices(root mean square error,relative standard deviation,etc.)and an analysis frequency of 143 Hz(much faster than the mainstream detection frame rate of 1 Hz).In addition,as a completely end-to-end and self-supervised framework,the SISTIFD enables automatic detection without manual preprocessing or intervention.With these advantages,it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry,especially in the regions that require both capability and efficiency.This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput,cross-interference,various analyte complexity,and diverse applications.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
