With the growing demand for offshore energy,deepwater drilling has become a vital technology in petroleum engineering.However,conventional drilling systems often face limitations such as delayed bottomhole pressure re...With the growing demand for offshore energy,deepwater drilling has become a vital technology in petroleum engineering.However,conventional drilling systems often face limitations such as delayed bottomhole pressure response and low control precision,particularly under narrow pressure window and complex formation conditions.To address these challenges,Dual-layer Pipe dual-gradient drilling(DGD)technology has been introduced,utilizing a dual-pipe structure and downhole lift pumps to extend the pressure control range.Despite these advantages,current DGD systems lack fast and precise bottomhole pressure control due to their reliance on indirect flow-based methods.This study proposes a bottomhole pressure control method based on backpressure regulation using a hybrid fuzzy-PID control strategy.A dynamic pressure calculation model is developed for the Dual-layer Pipe DGD system,incorporating coupling among choke valve opening,surface backpressure,and bottomhole pressure.The fuzzy-PID controller adjusts valve operation in real-time based on pressure deviation and its rate of change,improving response speed and control accuracy.Simulink-based simulations demonstrate that the proposed system achieves rapid pressure regulation with an overshoot below 5%and steady-state error under 0.12%.Compared to conventional PID control,the fuzzy-PID system shows superior adaptability to pressure variations.This research enhances the theoretical foundation of backpressure control in deepwater DGD operations and provides a practical approach for improving safety and efficiency in complex drilling environments.展开更多
BACKGROUND Accurate preoperative T staging is essential for determining optimal treatment strategies in colorectal cancer(CRC).Low-keV virtual monoenergetic images(VMIs)have been shown to enhance lesion conspicuity.Th...BACKGROUND Accurate preoperative T staging is essential for determining optimal treatment strategies in colorectal cancer(CRC).Low-keV virtual monoenergetic images(VMIs)have been shown to enhance lesion conspicuity.This study aimed to assess the diagnostic value of dual-layer spectral computed tomography(CT)-derived VMIs,in combination with multiplanar reformation(MPR)and evaluation of peritumoral fat stranding(PFS),for improving the accuracy of T staging in CRC.AIM To assess the diagnostic performance of dual-layer spectral CT(DLSCT)VMIs,particularly at low energy levels,and their integration with personalized MPR for preoperative T staging of CRC.METHODS In this retrospective study,157 patients with pathologically confirmed CRC(mean age:63.5±12.1 years)underwent DLSCT within 1 week before surgery.VMIs ranging from 40 keV to 70 keV(at 10 keV intervals)and conventional polyenergetic images(PEIs)were reconstructed.Objective image quality parameters,including image noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR),were quantified,alongside subjective image quality scores using a 5-point Likert scale.Interobserver agreement was evaluated usingκstatistics.Taking histopathology as the reference standard,the diagnostic accuracy of T staging(T1-2 vs T3-4)was compared across PEIs and VMIs,both with and without MPR and PFS.RESULTS Low-keV VMIs(40-70 keV)demonstrated significantly higher SNR and CNR than PEIs(all P<0.001).Notably,40-keV VMIs achieved noise levels comparable to PEIs(8.17±3.63 vs 8.53±2.90;P=0.673).Subjective image quality peaked at 40-50 keV VMIs(Likert scores 4.85-4.88 vs 3.97 for PEIs;P<0.001),supported by excellent interobserver agreement(κ=0.812-0.913).The combination of 40-50 keV VMIs with MPR yielded the highest T staging accuracy(94.27%)compared to axial PEIs(70.7%),with a sensitivity and specificity of 83.87%and 96.83%,respectively(Youden index=0.81;P<0.05).While PFS enhanced staging accuracy on PEIs(up to 77.07%with MPR),it provided no significant additional benefit for VMIs.CONCLUSION DLSCT VMIs at 40-50 keV significantly enhanced image quality and improved preoperative T staging accuracy of CRC when combined with MPR.These findings underscored the clinical value of low-keV spectral imaging in tailoring individualized treatment strategies.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
Lithium metal anode has become a favorable candidate for next-generation rechargeable batteries.However, the unstable interface between lithium metal and electrolyte leads to the growth of dendrites,resulting in the l...Lithium metal anode has become a favorable candidate for next-generation rechargeable batteries.However, the unstable interface between lithium metal and electrolyte leads to the growth of dendrites,resulting in the low Coulombic efficiency and even the safety concerns. Herein, a rigid-flexible dual-layer vermiculite nanosheet(VN) based organic-inorganic hybrid film on lithium metal anode is proposed to suppress dendrite growth and relieve volume fluctuations. The inner mechanically robust VN layer(3 μm thick) enhances the mechanical properties of the protective layer, while the outer polymer(4 μm thick) can enhance the flexibility of the hybrid layer. The Li | Li symmetric cell with protected lithium shows an extended life of over 670 h. The full cell with Li anode protected by dual-layer interface exhibits a better capacity retention of 80% after 174 cycles in comparison to bare Li anode with 94 cycles.This study provides a novel approach and a significant step towards prolonging lifespan of lithium metal batteries.展开更多
Compared to inorganic supports, polymeric supports can offer additional benefits, e.g., easier processing and cheaper. However, the organic surface has weak adhesion to the zeolitic imidazolate frameworks(ZIFs) membra...Compared to inorganic supports, polymeric supports can offer additional benefits, e.g., easier processing and cheaper. However, the organic surface has weak adhesion to the zeolitic imidazolate frameworks(ZIFs) membrane layer, which usually requires complex surface modification or seeding. Herein, we demonstrate that a dual-layer asymmetric polymer support prepared by a simple spinning process is a good candidate for the preparation of ZIF-8 membrane. The inner layer of the support is an organic hollow fiber(PES) with finger-like pores, and the outer layer is a ZnO-PES composite layer with finger-like pores also. The ZnO-PES composite layer is expected to contain uniform ZnO crystals in the polymer matrix, i.e., the ZnO particles in the skin layer of the support are not easy to fall off. Under the induction of ZnO particles in the outer layers, continuous ZIF-8 membranes can be prepared by single in-situ crystallization, showing good adhesion to the supports. The obtained ZIF-8 membranes show a H_(2) permeance of 8.7 × 10^(-8)mol·m^(-2)·s^(-1)·Pa^(-1) with a H_(2)/N_(2) ideal separation selectivity of 18.0. The design and preparation of this dual-layer polymer support is expected to promote the large-scale application of MOF membranes on polymer supports.展开更多
Ammonia allows storage and transport of hydrogen over long distances and is an attractive potential hydrogen carrier.Electrochemical decomposition has recently been used for the conversion of ammonia to hydrogen and i...Ammonia allows storage and transport of hydrogen over long distances and is an attractive potential hydrogen carrier.Electrochemical decomposition has recently been used for the conversion of ammonia to hydrogen and is regarded as a future technology for production of CO_(2)-free pure hydrogen.Herein,a heterostructural Pt-Ir dual-layer electrode is developed and shown to achieve successful long-term operation in an ammonia electrolyzer with an anion exchange membrane(AEM).This electrolyzer consisted of eight membra ne electrode assemblies(MEAs)with a total geometric area of 200 cm~2 on the anode side,which resulted in a hydrogen production rate of 25 L h~(-1).We observed the degradation in MEA performance attributed to changes in the anode catalyst layer during hydrogen production via ammonia electrolysis.Furthermore,we demonstrated the relationship between the ammonia oxidation reaction(AOR)and the oxygen evolution reaction(OER).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
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
基金the Sichuan Provincial Key R&D Program(Regional Innovation Coop-eration Project 2025YFHZ0306)Open Fund(PLN 2022-46)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University)Special Support for Sichuan Postdoctoral Research Projects.
文摘With the growing demand for offshore energy,deepwater drilling has become a vital technology in petroleum engineering.However,conventional drilling systems often face limitations such as delayed bottomhole pressure response and low control precision,particularly under narrow pressure window and complex formation conditions.To address these challenges,Dual-layer Pipe dual-gradient drilling(DGD)technology has been introduced,utilizing a dual-pipe structure and downhole lift pumps to extend the pressure control range.Despite these advantages,current DGD systems lack fast and precise bottomhole pressure control due to their reliance on indirect flow-based methods.This study proposes a bottomhole pressure control method based on backpressure regulation using a hybrid fuzzy-PID control strategy.A dynamic pressure calculation model is developed for the Dual-layer Pipe DGD system,incorporating coupling among choke valve opening,surface backpressure,and bottomhole pressure.The fuzzy-PID controller adjusts valve operation in real-time based on pressure deviation and its rate of change,improving response speed and control accuracy.Simulink-based simulations demonstrate that the proposed system achieves rapid pressure regulation with an overshoot below 5%and steady-state error under 0.12%.Compared to conventional PID control,the fuzzy-PID system shows superior adaptability to pressure variations.This research enhances the theoretical foundation of backpressure control in deepwater DGD operations and provides a practical approach for improving safety and efficiency in complex drilling environments.
基金Supported by Jiangsu Province 333 Talent Key Industry Field Talent Project,No.[2022]21Key Scientific Research Program of Jiangsu Provincial Health Committee,No.ZD2021059+2 种基金Nantong Key Laboratory Project,No.[2020]163The Project of Nantong City Health Committee,No.MS2023027Young Medical Talents Fund of Health and Family Planning Commission of Nantong,No.QA2019006 and No.QNZ2023027.
文摘BACKGROUND Accurate preoperative T staging is essential for determining optimal treatment strategies in colorectal cancer(CRC).Low-keV virtual monoenergetic images(VMIs)have been shown to enhance lesion conspicuity.This study aimed to assess the diagnostic value of dual-layer spectral computed tomography(CT)-derived VMIs,in combination with multiplanar reformation(MPR)and evaluation of peritumoral fat stranding(PFS),for improving the accuracy of T staging in CRC.AIM To assess the diagnostic performance of dual-layer spectral CT(DLSCT)VMIs,particularly at low energy levels,and their integration with personalized MPR for preoperative T staging of CRC.METHODS In this retrospective study,157 patients with pathologically confirmed CRC(mean age:63.5±12.1 years)underwent DLSCT within 1 week before surgery.VMIs ranging from 40 keV to 70 keV(at 10 keV intervals)and conventional polyenergetic images(PEIs)were reconstructed.Objective image quality parameters,including image noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR),were quantified,alongside subjective image quality scores using a 5-point Likert scale.Interobserver agreement was evaluated usingκstatistics.Taking histopathology as the reference standard,the diagnostic accuracy of T staging(T1-2 vs T3-4)was compared across PEIs and VMIs,both with and without MPR and PFS.RESULTS Low-keV VMIs(40-70 keV)demonstrated significantly higher SNR and CNR than PEIs(all P<0.001).Notably,40-keV VMIs achieved noise levels comparable to PEIs(8.17±3.63 vs 8.53±2.90;P=0.673).Subjective image quality peaked at 40-50 keV VMIs(Likert scores 4.85-4.88 vs 3.97 for PEIs;P<0.001),supported by excellent interobserver agreement(κ=0.812-0.913).The combination of 40-50 keV VMIs with MPR yielded the highest T staging accuracy(94.27%)compared to axial PEIs(70.7%),with a sensitivity and specificity of 83.87%and 96.83%,respectively(Youden index=0.81;P<0.05).While PFS enhanced staging accuracy on PEIs(up to 77.07%with MPR),it provided no significant additional benefit for VMIs.CONCLUSION DLSCT VMIs at 40-50 keV significantly enhanced image quality and improved preoperative T staging accuracy of CRC when combined with MPR.These findings underscored the clinical value of low-keV spectral imaging in tailoring individualized treatment strategies.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
基金supported by National Natural Science Foundation of China (22179070, U1932220)。
文摘Lithium metal anode has become a favorable candidate for next-generation rechargeable batteries.However, the unstable interface between lithium metal and electrolyte leads to the growth of dendrites,resulting in the low Coulombic efficiency and even the safety concerns. Herein, a rigid-flexible dual-layer vermiculite nanosheet(VN) based organic-inorganic hybrid film on lithium metal anode is proposed to suppress dendrite growth and relieve volume fluctuations. The inner mechanically robust VN layer(3 μm thick) enhances the mechanical properties of the protective layer, while the outer polymer(4 μm thick) can enhance the flexibility of the hybrid layer. The Li | Li symmetric cell with protected lithium shows an extended life of over 670 h. The full cell with Li anode protected by dual-layer interface exhibits a better capacity retention of 80% after 174 cycles in comparison to bare Li anode with 94 cycles.This study provides a novel approach and a significant step towards prolonging lifespan of lithium metal batteries.
基金supported by the National Natural Science Foundation of China (21978253)the Fundamental Research Funds for the Central Universities (226-2022-00020, 226-2022-00055)。
文摘Compared to inorganic supports, polymeric supports can offer additional benefits, e.g., easier processing and cheaper. However, the organic surface has weak adhesion to the zeolitic imidazolate frameworks(ZIFs) membrane layer, which usually requires complex surface modification or seeding. Herein, we demonstrate that a dual-layer asymmetric polymer support prepared by a simple spinning process is a good candidate for the preparation of ZIF-8 membrane. The inner layer of the support is an organic hollow fiber(PES) with finger-like pores, and the outer layer is a ZnO-PES composite layer with finger-like pores also. The ZnO-PES composite layer is expected to contain uniform ZnO crystals in the polymer matrix, i.e., the ZnO particles in the skin layer of the support are not easy to fall off. Under the induction of ZnO particles in the outer layers, continuous ZIF-8 membranes can be prepared by single in-situ crystallization, showing good adhesion to the supports. The obtained ZIF-8 membranes show a H_(2) permeance of 8.7 × 10^(-8)mol·m^(-2)·s^(-1)·Pa^(-1) with a H_(2)/N_(2) ideal separation selectivity of 18.0. The design and preparation of this dual-layer polymer support is expected to promote the large-scale application of MOF membranes on polymer supports.
基金supported by the research program funded by the TKG Huchemssupported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(20213030040590)supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(2021R1A5A1028138)。
文摘Ammonia allows storage and transport of hydrogen over long distances and is an attractive potential hydrogen carrier.Electrochemical decomposition has recently been used for the conversion of ammonia to hydrogen and is regarded as a future technology for production of CO_(2)-free pure hydrogen.Herein,a heterostructural Pt-Ir dual-layer electrode is developed and shown to achieve successful long-term operation in an ammonia electrolyzer with an anion exchange membrane(AEM).This electrolyzer consisted of eight membra ne electrode assemblies(MEAs)with a total geometric area of 200 cm~2 on the anode side,which resulted in a hydrogen production rate of 25 L h~(-1).We observed the degradation in MEA performance attributed to changes in the anode catalyst layer during hydrogen production via ammonia electrolysis.Furthermore,we demonstrated the relationship between the ammonia oxidation reaction(AOR)and the oxygen evolution reaction(OER).
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.
