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
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
Geological storage and utilization of CO_(2)involve complex interactions among Thermo-hydromechanical-chemical(THMC)coupling processes,which significantly affect storage integrity and efficiency.To address the challen...Geological storage and utilization of CO_(2)involve complex interactions among Thermo-hydromechanical-chemical(THMC)coupling processes,which significantly affect storage integrity and efficiency.To address the challenges in accurately simulating these coupled phenomena,this paper systematically reviews recent advances in the mathematical modeling and numerical solution of THMC coupling in CO_(2)geological storage.The study focuses on the derivation and structure of governing and constitutive equations,the classification and comparative performance of fully coupled,iteratively coupled,and explicitly coupled solution methods,and the modeling of dynamic changes in porosity,permeability,and fracture evolution induced by multi-field interactions.Furthermore,the paper evaluates the capabilities,application scenarios,and limitations of major simulation platforms,including TOUGH,CMG-GEM,and COMSOL.By establishing a comparative framework integrating model formulations and solver strategies,this work clarifies the strengths and gaps of current approaches and contributes to the development of robust,scalable,and mechanism-oriented numerical models for long-term prediction of CO_(2)behavior in geological formations.展开更多
To reduce the uncertainty associated with the traditional definition of tunnel boring machine(TBM)utilization(U)and achieve an effective indicator of TBM performance,a new performance indicator called rock mass-relate...To reduce the uncertainty associated with the traditional definition of tunnel boring machine(TBM)utilization(U)and achieve an effective indicator of TBM performance,a new performance indicator called rock mass-related utilization(U_(r))is introduced;this variable considers only rock mass-related factors rather than all potential factors.This work aims to predict U_(r)by adopting the rock mass rating(RMR)and the moisture-dependent Cerchar abrasivity index(CAI).Substantial U_(r),RMR and CAI data are acquired from a 31.57 km northwestern Chinese water conveyance tunnel via tunnelling field recordings,geological investigations and Cerchar abrasivity tests.The moisture dependence of the CAI is explored across four lithologies:quartz schists,granites,sandstones and metamorphic andesites.The potential influences of RMR and CAI on Ur are then investigated.As the RMR increases,U_(r)initially increases and then peaks at an RMR of 56 before declining.U_(r)appears to decline with CAI.An investigation-based relation among U_(r),RMR and moisture-dependent CAI is developed for estimating U_(r).The developed relation can accurately predict U_(r)using RMR and moisture-dependent CAI in the majority of the tunnelling cases examined.This work proposes a stable indicator of TBM performance and provided a fairly accurate prediction method for this indicator.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
The hybrid policy is a flexible policy tool that combines features of carbon trading and carbon taxation.Its economic and environmental effects under China's background are still not studied in detail.Given the ex...The hybrid policy is a flexible policy tool that combines features of carbon trading and carbon taxation.Its economic and environmental effects under China's background are still not studied in detail.Given the exogenous carbon reduction targets,carbon prices,and carbon tax-rates,by computable general equilibrium modeling methods and factor decomposition methods,this article investigates direct and cascaded effects of the hybrid policy on economic growth,energy utilization,and carbon emission on the national level and the sector level,with China's national input-output data-set.Stepwisely,policy scenarios with irrational estimated results are selectively excluded based on comprehensive evaluation among economic,carbon reduction and other policy targets.As a result,against national economic conditions in 2007,the hybrid policy,with a carbon reduction target of -10%,a carbon tax-rate of around $10,and a ceiling carbon price of $40,is highly recommended,because of its significant lower economic loss,lower energy utilization cost,and practical robustness against fluctuation of energy market and carbon market.Furthermore,by decomposition analysis,carbon reduction-related costs are decomposed into a direct part that includes carbon allowance price and carbon tax,and an indirect part as the energy price incremental induced by direct carbon costs.Gross carbon reduction may be decomposed into three parts such as energy intensity,economic scale,and technical progress.And,carbon taxation is the main policy tool that stimulates to improve the energy efficiency.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
Coal-based soild wastes(CBSWs)are industrial byproducts that can be harmful to the environment.The exploitation and utilization of CBsWs offer societal advantages such as resource conservation,pollution reduction,and ...Coal-based soild wastes(CBSWs)are industrial byproducts that can be harmful to the environment.The exploitation and utilization of CBsWs offer societal advantages such as resource conservation,pollution reduction,and cost-effective production.However,environmentally sustainable management remains a worldwide challenge due to the substantial production volume and limited disposal capacity of CBSWs.The physicochemical properties and utilization of CBSWs are summarized,including fly ash,coal gangue and coal gasification slag.It also presents the current global applications status of CBSWs resources and examines market supply and demand.Subsequently,the paper provides an overview of studies on ways to utilise CBSWs,highlighting the primary avenues of CBSWs resource utilization which are mainly from the fields of chemical materials,metallurgy and agriculture.Furthermore,a comparative evaluation of the various methods for CBSWs resource recovery is conducted,outlining their respective advantages and disadvantages.The future development of CBSWs recycling processes is also discussed.The review concludes that while there is a growing need for attention in CBSWs recycling,its utilization will involve a combination of both large-scale treatment and refinement processes.The paper aims to offer references and insights for the effective utilization and environmental protection of CBSWs.Future direction will focus on the collaborative utilization of CBSWs,emphasizing on the combination of large-scale and high-value utilization.In addition,there is a need to establish a comprehensive database based on on-site production practices,explore on-site solutions to reduce transportation costs,and improve physicochemical properties during the production process.展开更多
Objective: The purpose of this study was to examine the relationships between osteoporosis knowledge, beliefs and calcium intake among college students. This study also examined perceived susceptibility, severity, ben...Objective: The purpose of this study was to examine the relationships between osteoporosis knowledge, beliefs and calcium intake among college students. This study also examined perceived susceptibility, severity, benefits, barriers and self-efficacy related to osteoporosis prevention. Participants: Seven hundred and ninety two (n = 792) men and women ages 17 - 31 of all ethnicities at a mid-western regional university in the US participated in the study. Methods: The Osteoporosis Knowledge Test, Osteoporosis Health Belief Scale, and Osteoporosis Preventing Behaviors Survey were utilized. Each of these tools were previously validated and found reliable. Correlation and multiple regression analyses were completed. Results: Participants did not perceive themselves as susceptible to osteoporosis and perceived minimal barriers to calcium intake. Their knowledge was minimal concerning alternate sources of calcium. Conclusions: Prevention programs should aim to increase osteoporosis knowledge of risk factors and osteoprotective behaviors and to decrease high-risk behaviors during college years when behavior changes can have the strongest impact on bone health.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
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Background There is a growing focus on using various plant-derived agricultural by-products to increase the benefits of pig farming,but these feedstuffs are fibrous in nature.This study investigated the relationship b...Background There is a growing focus on using various plant-derived agricultural by-products to increase the benefits of pig farming,but these feedstuffs are fibrous in nature.This study investigated the relationship between dietary fiber physicochemical properties and feedstuff fermentation characteristics and their effects on nutrient utilization,energy metabolism,and gut microbiota in growing pigs.Methods Thirty-six growing barrows(47.2±1.5 kg)were randomly allotted to 6 dietary treatments with 2 apparent viscosity levels and 3β-glucan-to-arabinoxylan ratios.In the experiment,nutrient utilization,energy metabolism,fecal microbial community,and production and absorption of short-chain fatty acid(SCFA)of pigs were investigated.In vitro digestion and fermentation models were used to compare the fermentation characteristics of feedstuffs and ileal digesta in the pig’s hindgut.Results The production dynamics of SCFA and dry matter corrected gas production of different feedstuffs during in vitro fermentation were different and closely related to the physical properties and chemical structure of the fiber.In animal experiments,increasing the dietary apparent viscosity and theβ-glucan-to-arabinoxylan ratios both increased the apparent ileal digestibility(AID),apparent total tract digestibility(ATTD),and hindgut digestibility of fiber components while decreasing the AID and ATTD of dry matter and organic matter(P<0.05).In addition,increasing dietary apparent viscosity andβ-glucan-to-arabinoxylan ratios both increased gas exchange,heat production,and protein oxidation,and decreased energy deposition(P<0.05).The dietary apparent viscosity andβ-glucanto-arabinoxylan ratios had linear interaction effects on the digestible energy,metabolizable energy,retained energy(RE),and net energy(NE)of the diets(P<0.05).At the same time,the increase of dietary apparent viscosity andβ-glucan-to-arabinoxylan ratios both increased SCFA production and absorption(P<0.05).Increasing the dietary apparent viscosity andβ-glucan-to-arabinoxylan ratios increased the diversity and abundance of bacteria(P<0.05)and the relative abundance of beneficial bacteria.Furthermore,increasing the dietaryβ-glucan-to-arabinoxylan ratios led to a linear increase in SCFA production during the in vitro fermentation of ileal digesta(P<0.001).Finally,the prediction equations for RE and NE were established.Conclusion Dietary fiber physicochemical properties alter dietary fermentation patterns and regulate nutrient utilization,energy metabolism,and pig gut microbiota composition and metabolites.展开更多
Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Reg...Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.展开更多
Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experienci...Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experiencing a significant annual increase.Despite its prevalence and considerable impact on people,little is known about its pathogenesis.One major reason is the scarcity of reliable animal models due to the absence of consensus on the pathology and etiology of depression.Furthermore,the neural circuit mechanism of depression induced by various factors is particularly complex.Considering the variability in depressive behavior patterns and neurobiological mechanisms among different animal models of depression,a comparison between the neural circuits of depression induced by various factors is essential for its treatment.In this review,we mainly summarize the most widely used behavioral animal models and neural circuits under different triggers of depression,aiming to provide a theoretical basis for depression prevention.展开更多
CO_(2)-responsive gels,which swell upon contact with CO_(2),are widely used for profile control to plug high-permeability gas flow channels in carbon capture,utilization,and storage(CCUS)applications in oil reser-voir...CO_(2)-responsive gels,which swell upon contact with CO_(2),are widely used for profile control to plug high-permeability gas flow channels in carbon capture,utilization,and storage(CCUS)applications in oil reser-voirs.However,the use of these gels in high-temperature CCUS applications is limited due to their rever-sible swelling behavior at elevated temperatures.In this study,a novel dispersed particle gel(DPG)suspension is developed for high-temperature profile control in CCUS applications.First,we synthesize a double-network hydrogel consisting of a crosslinked polyacrylamide(PAAm)network and a crosslinked sodium alginate(SA)network.The hydrogel is then sheared in water to form a pre-prepared DPG suspen-sion.To enhance its performance,the gel particles are modified by introducing potassium methylsilan-etriolate(PMS)upon CO_(2) exposure.Comparing the particle size distributions of the modified and pre-prepared DPG suspension reveals a significant swelling of gel particles,over twice their original size.Moreover,subjecting the new DPG suspension to a 100℃ environment for 24 h demonstrates that its gel particle sizes do not decrease,confirming irreversible swelling,which is a significant advantage over the traditional CO_(2)-responsive gels.Thermogravimetric analysis further indicates improved thermal sta-bility compared to the pre-prepared DPG particles.Core flooding experiments show that the new DPG suspension achieves a high plugging efficiency of 95.3%in plugging an ultra-high permeability sandpack,whereas the pre-prepared DPG suspension achieves only 82.8%.With its high swelling ratio,irreversible swelling at high temperatures,enhanced thermal stability,and superior plugging performance,the newly developed DPG suspension in this work presents a highly promising solution for profile control in high-temperature CCUS applications.展开更多
Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,...Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.展开更多
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)].
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 China Postdoctoral Science Foundation(No.2024M752803)the National Natural Science Foundation of China(No.52179112)the Open Fund of National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University)(No.PLN2023-02)。
文摘Geological storage and utilization of CO_(2)involve complex interactions among Thermo-hydromechanical-chemical(THMC)coupling processes,which significantly affect storage integrity and efficiency.To address the challenges in accurately simulating these coupled phenomena,this paper systematically reviews recent advances in the mathematical modeling and numerical solution of THMC coupling in CO_(2)geological storage.The study focuses on the derivation and structure of governing and constitutive equations,the classification and comparative performance of fully coupled,iteratively coupled,and explicitly coupled solution methods,and the modeling of dynamic changes in porosity,permeability,and fracture evolution induced by multi-field interactions.Furthermore,the paper evaluates the capabilities,application scenarios,and limitations of major simulation platforms,including TOUGH,CMG-GEM,and COMSOL.By establishing a comparative framework integrating model formulations and solver strategies,this work clarifies the strengths and gaps of current approaches and contributes to the development of robust,scalable,and mechanism-oriented numerical models for long-term prediction of CO_(2)behavior in geological formations.
基金financially supported by the National Natural Science Foundation of China(Nos.41972270,52076198)the Key Research and Development Plan of Henan Province(No.182102210014)+2 种基金the Excellent Youth Foundation of Henan Scientific Committee(No.222300420078)the Youth Talent Promotion Project of Henan Province(No.2022HYTP019)the Open Foundation of State Key Laboratory of Shield Machine and Boring Technology(No.SKLST-2019-K06)。
文摘To reduce the uncertainty associated with the traditional definition of tunnel boring machine(TBM)utilization(U)and achieve an effective indicator of TBM performance,a new performance indicator called rock mass-related utilization(U_(r))is introduced;this variable considers only rock mass-related factors rather than all potential factors.This work aims to predict U_(r)by adopting the rock mass rating(RMR)and the moisture-dependent Cerchar abrasivity index(CAI).Substantial U_(r),RMR and CAI data are acquired from a 31.57 km northwestern Chinese water conveyance tunnel via tunnelling field recordings,geological investigations and Cerchar abrasivity tests.The moisture dependence of the CAI is explored across four lithologies:quartz schists,granites,sandstones and metamorphic andesites.The potential influences of RMR and CAI on Ur are then investigated.As the RMR increases,U_(r)initially increases and then peaks at an RMR of 56 before declining.U_(r)appears to decline with CAI.An investigation-based relation among U_(r),RMR and moisture-dependent CAI is developed for estimating U_(r).The developed relation can accurately predict U_(r)using RMR and moisture-dependent CAI in the majority of the tunnelling cases examined.This work proposes a stable indicator of TBM performance and provided a fairly accurate prediction method for this indicator.
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 the Fundamental Research Funds for the Central Universities[CDJSK10 00 68]NSFC Young Scientist Research Fund[0903080]
文摘The hybrid policy is a flexible policy tool that combines features of carbon trading and carbon taxation.Its economic and environmental effects under China's background are still not studied in detail.Given the exogenous carbon reduction targets,carbon prices,and carbon tax-rates,by computable general equilibrium modeling methods and factor decomposition methods,this article investigates direct and cascaded effects of the hybrid policy on economic growth,energy utilization,and carbon emission on the national level and the sector level,with China's national input-output data-set.Stepwisely,policy scenarios with irrational estimated results are selectively excluded based on comprehensive evaluation among economic,carbon reduction and other policy targets.As a result,against national economic conditions in 2007,the hybrid policy,with a carbon reduction target of -10%,a carbon tax-rate of around $10,and a ceiling carbon price of $40,is highly recommended,because of its significant lower economic loss,lower energy utilization cost,and practical robustness against fluctuation of energy market and carbon market.Furthermore,by decomposition analysis,carbon reduction-related costs are decomposed into a direct part that includes carbon allowance price and carbon tax,and an indirect part as the energy price incremental induced by direct carbon costs.Gross carbon reduction may be decomposed into three parts such as energy intensity,economic scale,and technical progress.And,carbon taxation is the main policy tool that stimulates to improve the energy efficiency.
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.
基金supported by the following:“National Natural Science Foundation of China”(22478231)“Natural Science Foundation of Henan”(242300421449)“Fundamental Research Program of Shanxi Province”(202403021221011).
文摘Coal-based soild wastes(CBSWs)are industrial byproducts that can be harmful to the environment.The exploitation and utilization of CBsWs offer societal advantages such as resource conservation,pollution reduction,and cost-effective production.However,environmentally sustainable management remains a worldwide challenge due to the substantial production volume and limited disposal capacity of CBSWs.The physicochemical properties and utilization of CBSWs are summarized,including fly ash,coal gangue and coal gasification slag.It also presents the current global applications status of CBSWs resources and examines market supply and demand.Subsequently,the paper provides an overview of studies on ways to utilise CBSWs,highlighting the primary avenues of CBSWs resource utilization which are mainly from the fields of chemical materials,metallurgy and agriculture.Furthermore,a comparative evaluation of the various methods for CBSWs resource recovery is conducted,outlining their respective advantages and disadvantages.The future development of CBSWs recycling processes is also discussed.The review concludes that while there is a growing need for attention in CBSWs recycling,its utilization will involve a combination of both large-scale treatment and refinement processes.The paper aims to offer references and insights for the effective utilization and environmental protection of CBSWs.Future direction will focus on the collaborative utilization of CBSWs,emphasizing on the combination of large-scale and high-value utilization.In addition,there is a need to establish a comprehensive database based on on-site production practices,explore on-site solutions to reduce transportation costs,and improve physicochemical properties during the production process.
文摘Objective: The purpose of this study was to examine the relationships between osteoporosis knowledge, beliefs and calcium intake among college students. This study also examined perceived susceptibility, severity, benefits, barriers and self-efficacy related to osteoporosis prevention. Participants: Seven hundred and ninety two (n = 792) men and women ages 17 - 31 of all ethnicities at a mid-western regional university in the US participated in the study. Methods: The Osteoporosis Knowledge Test, Osteoporosis Health Belief Scale, and Osteoporosis Preventing Behaviors Survey were utilized. Each of these tools were previously validated and found reliable. Correlation and multiple regression analyses were completed. Results: Participants did not perceive themselves as susceptible to osteoporosis and perceived minimal barriers to calcium intake. Their knowledge was minimal concerning alternate sources of calcium. Conclusions: Prevention programs should aim to increase osteoporosis knowledge of risk factors and osteoprotective behaviors and to decrease high-risk behaviors during college years when behavior changes can have the strongest impact on bone health.
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.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金supported by the National Key Research and Development Program(No.2021YFD1300201)Jilin Provincial Department of Science and Technology Innovation Platform and Talent Special Project(No.20230508090RC).
文摘Background There is a growing focus on using various plant-derived agricultural by-products to increase the benefits of pig farming,but these feedstuffs are fibrous in nature.This study investigated the relationship between dietary fiber physicochemical properties and feedstuff fermentation characteristics and their effects on nutrient utilization,energy metabolism,and gut microbiota in growing pigs.Methods Thirty-six growing barrows(47.2±1.5 kg)were randomly allotted to 6 dietary treatments with 2 apparent viscosity levels and 3β-glucan-to-arabinoxylan ratios.In the experiment,nutrient utilization,energy metabolism,fecal microbial community,and production and absorption of short-chain fatty acid(SCFA)of pigs were investigated.In vitro digestion and fermentation models were used to compare the fermentation characteristics of feedstuffs and ileal digesta in the pig’s hindgut.Results The production dynamics of SCFA and dry matter corrected gas production of different feedstuffs during in vitro fermentation were different and closely related to the physical properties and chemical structure of the fiber.In animal experiments,increasing the dietary apparent viscosity and theβ-glucan-to-arabinoxylan ratios both increased the apparent ileal digestibility(AID),apparent total tract digestibility(ATTD),and hindgut digestibility of fiber components while decreasing the AID and ATTD of dry matter and organic matter(P<0.05).In addition,increasing dietary apparent viscosity andβ-glucan-to-arabinoxylan ratios both increased gas exchange,heat production,and protein oxidation,and decreased energy deposition(P<0.05).The dietary apparent viscosity andβ-glucanto-arabinoxylan ratios had linear interaction effects on the digestible energy,metabolizable energy,retained energy(RE),and net energy(NE)of the diets(P<0.05).At the same time,the increase of dietary apparent viscosity andβ-glucan-to-arabinoxylan ratios both increased SCFA production and absorption(P<0.05).Increasing the dietary apparent viscosity andβ-glucan-to-arabinoxylan ratios increased the diversity and abundance of bacteria(P<0.05)and the relative abundance of beneficial bacteria.Furthermore,increasing the dietaryβ-glucan-to-arabinoxylan ratios led to a linear increase in SCFA production during the in vitro fermentation of ileal digesta(P<0.001).Finally,the prediction equations for RE and NE were established.Conclusion Dietary fiber physicochemical properties alter dietary fermentation patterns and regulate nutrient utilization,energy metabolism,and pig gut microbiota composition and metabolites.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.
基金supported by the Brain&Behavior Research Foundation(30233).
文摘Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experiencing a significant annual increase.Despite its prevalence and considerable impact on people,little is known about its pathogenesis.One major reason is the scarcity of reliable animal models due to the absence of consensus on the pathology and etiology of depression.Furthermore,the neural circuit mechanism of depression induced by various factors is particularly complex.Considering the variability in depressive behavior patterns and neurobiological mechanisms among different animal models of depression,a comparison between the neural circuits of depression induced by various factors is essential for its treatment.In this review,we mainly summarize the most widely used behavioral animal models and neural circuits under different triggers of depression,aiming to provide a theoretical basis for depression prevention.
基金Lin Du acknowledges the financial support provided by China Scholarship Council(CSC)via a Ph.D.Scholarship(202008510128)supported by Core Technology Project of China National Petroleum Corporation(CNPC)"Research on Thermal Miscible Flooding Technology"(2023ZG18)。
文摘CO_(2)-responsive gels,which swell upon contact with CO_(2),are widely used for profile control to plug high-permeability gas flow channels in carbon capture,utilization,and storage(CCUS)applications in oil reser-voirs.However,the use of these gels in high-temperature CCUS applications is limited due to their rever-sible swelling behavior at elevated temperatures.In this study,a novel dispersed particle gel(DPG)suspension is developed for high-temperature profile control in CCUS applications.First,we synthesize a double-network hydrogel consisting of a crosslinked polyacrylamide(PAAm)network and a crosslinked sodium alginate(SA)network.The hydrogel is then sheared in water to form a pre-prepared DPG suspen-sion.To enhance its performance,the gel particles are modified by introducing potassium methylsilan-etriolate(PMS)upon CO_(2) exposure.Comparing the particle size distributions of the modified and pre-prepared DPG suspension reveals a significant swelling of gel particles,over twice their original size.Moreover,subjecting the new DPG suspension to a 100℃ environment for 24 h demonstrates that its gel particle sizes do not decrease,confirming irreversible swelling,which is a significant advantage over the traditional CO_(2)-responsive gels.Thermogravimetric analysis further indicates improved thermal sta-bility compared to the pre-prepared DPG particles.Core flooding experiments show that the new DPG suspension achieves a high plugging efficiency of 95.3%in plugging an ultra-high permeability sandpack,whereas the pre-prepared DPG suspension achieves only 82.8%.With its high swelling ratio,irreversible swelling at high temperatures,enhanced thermal stability,and superior plugging performance,the newly developed DPG suspension in this work presents a highly promising solution for profile control in high-temperature CCUS applications.
基金supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(grant No.YSBR-018)the National Natural Science Foundation of China(grant Nos.42188101,42130204)+4 种基金the B-type Strategic Priority Program of CAS(grant no.XDB41000000)the National Natural Science Foundation of China(NSFC)Distinguished Overseas Young Talents Program,Innovation Program for Quantum Science and Technology(2021ZD0300301)the Open Research Project of Large Research Infrastructures of CAS-“Study on the interaction between low/mid-latitude atmosphere and ionosphere based on the Chinese Meridian Project”.The project was supported also by the National Key Laboratory of Deep Space Exploration(Grant No.NKLDSE2023A002)the Open Fund of Anhui Provincial Key Laboratory of Intelligent Underground Detection(Grant No.APKLIUD23KF01)the China National Space Administration(CNSA)pre-research Project on Civil Aerospace Technologies No.D010305,D010301.
文摘Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.
