The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpec...The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.展开更多
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.展开更多
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable...In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable-driven manipulator.In this paper,the kinematic analysis of a type of cable-driven manipulator is performed,and a motion planning scheme is conducted to actuate this manipulator.Moreover,a flexible multi-body dynamic model of a cable-driven manipulator considering the frictional contact between the cables and pulleys is established.To describe properties such as flexibility,vibration,and variable length of the cable,this paper utilizes reducedorder beam elements of the Absolute Nodal Coordinates Formulation(ANCF)in Arbitrary Lagrangian Eulerian(ALE)framework.Additionally,a virtual element is introduced to model the contact segment in the cable-pulley system.A tension decay factor is employed to account for the friction in the contact segment.To validate the proposed method,a semi-analytical model based on D'Alembert's principle is established.Cross-verification is performed to validate the accuracy of both models.The model is further applied to simulate the rotation of the cable-driven manipulator with different structural parameters and frictional factors.The results from the analyses provide valuable guidance for the design and motion control of the in-space cable-driven manipulator.Finally,a prototype of a single module is manufactured and tested.Ground experiments are carried out to verify the kinematic and dynamic models.展开更多
Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury...Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury in humans are severely limited by the inaccessibility of living brain tissue,creating a need for in vitro model systems to study cellular mechanisms of degeneration and regeneration following injury.Here we describe methods to establish a 3D human brain tissue model,consisting of a silk-collagen composite scaffold seeded with human neurons,astrocytes,and microglia,to study neuro-regeneration after traumatic brain injury.Step-by-step fabrication,injury,and analytical assessments of the 3D“triculture”system are described.Using this tissue model system,we demonstrate that glial cells promote regeneration of neuronal networks within the injury site over several weeks post-injury.Further,we found that regenerating networks in the 3D triculture tissues did not secrete early markers of neurodegenerative disease,but displayed signs of excitatory/inhibitory imbalance,suggesting that pro-regenerative treatments for traumatic brain injury in the future may need to direct cell differentiation to promote proper function.The mechanical stability of this model system enables physiologically relevant impact injury and long-term culture capability,while its modular design enables modification of cell contents,extracellular matrix composition,and scaffold properties.This adaptability could allow the integration of patient-derived cells and genetic modifications to bridge research and clinical applications focused on personalized targeted therapies.This in vitro system provides a valuable platform for accelerating therapeutic advancements in traumatic brain injury and neurodegenerative disorders,ultimately improving patient outcomes.展开更多
The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-ti...The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-time scenarios.This review begins with a concise overview of traditional tight-binding(TB)models,including both(semi-)empirical and first-principles approaches,establishing the foundation for understanding MLTB developments.We then present a systematic classification of existing MLTB methodologies,grouped into two major categories:direct prediction of TB Hamiltonian elements and inference of empirical parameters.A comparative analysis with other ML-based electronic structure models is also provided,highlighting the advancement of MLTB approaches.Finally,we explore the emerging MLTB application ecosystem,highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains.The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena.展开更多
The assessment of landslide susceptibility triggered by earthquakes serves as a fundamental basis for effective emergency response and post-disaster reconstruction efforts.However,current predictive models often face ...The assessment of landslide susceptibility triggered by earthquakes serves as a fundamental basis for effective emergency response and post-disaster reconstruction efforts.However,current predictive models often face limitations in accuracy,with the prediction rates of most models ranging from 80%to 90%.This study introduces a new hybrid machine learning framework,termed the Subtractive Clustering Method-based Adaptive Neural Network Fuzzy Inference System(SCM-ANFIS),and evaluates its performance in the Wenchuan earthquake region.This region features distinctive geology(e.g.,Longmenshan Fault-governed complex tectonics)and abundant fundamental data;additionally,the 2008 Wenchuan Earthquake provides a pertinent case for earthquakeinduced landslide model evaluation.Based on a literature review and correlation analysis,this study systematically identified 12 key influencing factors that collectively characterize the region's high landslide susceptibility,shaped by intense seismic activity,complex terrain,and fragmented rock masses.Positive and negative samples were extracted as target variables through buffer sampling to calculate earthquake-induced landslide susceptibility.The susceptibility zoning map was then calibrated and generated by incorporating the regional landslide area percentage.The study concludes the following:(1)Compared to traditional machine learning approaches,the model demonstrates strong performance and stability,achieving a prediction accuracy of 98.5%.Approximately 97.89%of historically documented landslides in the Wenchuan region were located within areas identified as having high susceptibility,which aligns well with observed spatial distributions.(2)Increase in the buffer distance contributes to enhance prediction accuracy while a larger sample size improves model stability.(3)The model exhibits superior performance and possesses scalability for application in other regions,such as Jiuzhaigou and Luding.(4)Nonetheless,limitations remain regarding uncertainty,sample composition,algorithmic design,and practical implementation.Future research should focus on improving data precision and optimizing algorithmic frameworks.Overall,this study provides valuable support for landslide susceptibility assessments and contributes essential data for disaster risk mitigation efforts.展开更多
Background:Skeletal tuberculosis(TB)remains a persistent clinical and research chal-lenge due to its chronic course,osteolytic destruction,and the limitations of existing animal models,which often require high-level b...Background:Skeletal tuberculosis(TB)remains a persistent clinical and research chal-lenge due to its chronic course,osteolytic destruction,and the limitations of existing animal models,which often require high-level biosafety containment or fail to repli-cate human skeletal pathology.Methods:This study developed a biosafe,accessible,and versatile murine model of skeletal TB using Mycobacterium smegmatis,a fast-growing,nonpathogenic myco-bacterial species with high genomic homology to Mycobacterium tuberculosis.Three infection routes-subperiosteal calvarial injection,intratibial injection,and intra-cardiac inoculation-were systematically evaluated for their ability to induce lo-calized versus disseminated bone infection under standard biosafety level(BSL)-1 conditions.Results:Subperiosteal calvarial and intratibial injection of M.smegmatis induced local-ized bone lesions characterized by osteolysis,sequestrum formation,granulomatous inflammation,and increased osteoclast activity.Intratibial infection additionally trig-gered compartment-specific immune responses,including neutrophil and macrophage expansion,transient B-cell depletion,and activation of interferon-γ^(+)(IFN-γ^(+))T cells,reflecting active immune remodeling at the infection site.Systemic dissemination via intracardiac injection reproducibly generated progressive vertebral and tibial bone destruction with organized granuloma formation and immune cell infiltration but without prominent sequestrum formation.Compared to intratibial infection,intracar-diac delivery exhibited lower intragroup variability and more closely recapitulated the diffuse progression of extrapulmonary skeletal tuberculosis.Conclusions:This M.smegmatis-based murine model provides a straightforward,reliable,and immunopathologically relevant platform for exploring host-pathogen dynamics,immune-driven bone destruction,and early-stage therapeutic testing in skeletal TB,all within standard BSL-1 laboratories.This model fills a critical gap by enabling BSL-1 research into skeletal TB mechanisms and drug development.展开更多
Background:In preclinical research,tumor growth inhibition in subcutaneous models is frequently employed to evaluate therapeutic efficacy;however,such models often lack clinical translatability.Methods:To better appro...Background:In preclinical research,tumor growth inhibition in subcutaneous models is frequently employed to evaluate therapeutic efficacy;however,such models often lack clinical translatability.Methods:To better approximate clinical reality,taking the case of doxorubicin treatment,we utilized an orthotopic transplant and resection(OtR)strategy to systematically assess the effects of neoadjuvant chemotherapy,adjuvant chem-otherapy,and their combination on tumor growth,recurrence,and malignant progression.Results:Surprisingly,none of the treatments improved mouse survival,with adjuvant therapy even shortening it.Although neoadjuvant chemotherapy delayed preopera-tive tumor growth,and all regimens reduced recurrence rates,none effectively pre-vented metastasis.Furthermore,all treatment groups exhibited weight loss,indicative of chemotherapy-induced cachexia.Conclusions:Collectively,these findings demonstrate that reduced tumor growth in preclinical mouse models does not necessarily translate into overall survival benefit.Our results emphasize the critical importance of prioritizing metastasis prevention over tumor growth inhibition as a key efficacy endpoint in antitumor drug evaluation.展开更多
Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability.The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of win...Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability.The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of wind speed and direction in the wind field,leading to an increased risk of load overload,severely affecting operational lifespan and reducing power generation efficiency.This impact is even more pronounced during the passage of a cold front.To address this issue,this paper proposes an independent variable-pitch control method that optimizes predictions by utilizing the spatiotemporal relationship between pre-observed cold front patterns and their dynamic propagation.First,a cold front and cold front propagation model suitable for engineering applications was derived.And a non-uniform inflow load model of turbine is established,which,combined with tower vibration response and rotor dynamic loads,accurately simulates the force distribution under complex inflow conditions.Subsequently,a pre-observation-based active cyclic pitch control method is presented,dynamically computing optimal pitch angle sequences by predicting wind field trends.This method eliminates the need for iterative optimization algorithms and reduces control latency to achieve proactive load management.Simulation verification shows that the proposed control strategy can effectively reduce key structural loads and increase power generation without relying on complex optimization algorithms.This method provides a practical solution for improving the economic benefits and operational reliability of wind farms under special wind conditions.展开更多
In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statist...In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.展开更多
Interaction between the converter and the grid may lead to harmonic oscillations.The impedance-based method is an effective way to deal with the stability issue.In this study,the impedance-based method is used to inve...Interaction between the converter and the grid may lead to harmonic oscillations.The impedance-based method is an effective way to deal with the stability issue.In this study,the impedance-based method is used to investigate the small-signal stability of a cascaded 12-pulse line-commutated converter-based high-voltage direct current(LCC-HVDC)transmission system.In the modeling part,the impedance models of the single rectifier and inverter are established respectively with consideration to the effect of frequency coupling,which has improved the accuracy of the models.Based on the models,the AC impedance models of the cascaded LCC-HVDC transmission system are established both on the rectifier and inverter side.In the stability analysis part,the stability of the system is analyzed under different working conditions.The simulation results reveal that the established impedance model can properly represent the stability of this system.The findings of this study can provide a theoretical reference for the stability design and oscillation suppression strategy of LCC-HVDC transmission systems and LCC interconnected systems.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
In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocal...In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.展开更多
The intestine is a key component of the barrier,absorption,and immune systems,contributing significantly to maintaining internal homeostasis and influencing disease progression.Its distinctive physiological functions ...The intestine is a key component of the barrier,absorption,and immune systems,contributing significantly to maintaining internal homeostasis and influencing disease progression.Its distinctive physiological functions arise from a complex interplay between its structure and microenvironment.Recent advancements in bioengineering technologies now enable the construction of in vitro intestinal models that faithfully recapitulate the organizational and functional characteristics of native tissue.This review examines the interface between in vitro models and native intestinal biology,offering insights into the replication of organ functions from a manufacturing perspective.We explore bioengineering strategies that enable the mapping of cross-scale structures and the creation of biomimetic environments essential for physiological performance.Furthermore,we discuss pragmatic optimization strategies for applying these models to both physiological and pathological studies,thereby enhancing their translational potential for drug development,disease modeling,and personalized medicine.In contrast to previous reviews,this work proposes an engineering-centered framework for linking structural fabrication strategies to functional performance across intestinal model types.展开更多
Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and ...Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.展开更多
Background:The traditional method of heterotopic abdominal heart transplantation(HTx)involves crossclamping the inferior vena cava,which inevitably leads to bilateral lower limb ischemia(LI).This study first aimed to ...Background:The traditional method of heterotopic abdominal heart transplantation(HTx)involves crossclamping the inferior vena cava,which inevitably leads to bilateral lower limb ischemia(LI).This study first aimed to investigate the impact of LI on renal function in rats subjected to unilateral nephrectomy(UNx).Second,a modified method utilizing renal vessel-assisted anastomosis in rats with left UNx was compared with the traditional method for abdominal HTx.Methods:Male Sprague-Dawley rats were utilized as subjects for both experimental phases.In experiment 1,the animals were divided into four groups:sham operation group;LI group-rats undergoing occlusion of the abdominal aorta and vena cava below the renal vessels;UNx group-rats with left UNx;and LI+UNx group.All operated animals were monitored for up to 7 days for biochemical markers,renal histopathology,and survival rates.In experiment 2,we introduced the renal vessel-assisted method as the experimental group and compared it against the traditional method as the control within rat heterotopic HTx models.We assessed operative characteristics,echocardiography results,histological findings,and graft survival.Results:First,LI resulted in acute kidney dysfunction characterized by a decrease in 7day survival rates and creatinine clearance rates in both the LI and LI+UNx groups compared to the sham operation and UNx groups.Particularly,histopathological damage in the kidney and liver did not exhibit significant effects during this period.Second,the implementation of the renal vessel-assisted method significantly reduced bleeding volume at suture sites and enhanced the 7day survival rate compared to the traditional method.Conclusion:Acute kidney injury was induced by LI postoperation in treated rats.The renal vessel-assisted method demonstrated its effectiveness as a superior alternative that mitigates complications associated with the traditional method.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,suc...This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.展开更多
Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhib...Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhibit complex patterns of zero AF episodes,which may arise from either true AF-free status(structural zeros)or missed AF episodes due to intermittent monitoring(random zeros).Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data.The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations.Unlike traditional approaches requiring fully specified zero-inflated models,the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions,with weights determined by binomial sizes.A closed-form test statistic is developed,and its asymptotic distribution is derived using estimating equations.Simulations demonstrate superior performance over existing methods,and real-world AF monitoring data validate the practical utility of our proposed test.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.12372214 and U2341231)。
文摘The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
基金supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
基金co-supported by the National Natural Science Foundation of China(Nos.12102034 and 12125201)the Open Fund of State Key Laboratory of Robotics and Systems(HIT),China。
文摘In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable-driven manipulator.In this paper,the kinematic analysis of a type of cable-driven manipulator is performed,and a motion planning scheme is conducted to actuate this manipulator.Moreover,a flexible multi-body dynamic model of a cable-driven manipulator considering the frictional contact between the cables and pulleys is established.To describe properties such as flexibility,vibration,and variable length of the cable,this paper utilizes reducedorder beam elements of the Absolute Nodal Coordinates Formulation(ANCF)in Arbitrary Lagrangian Eulerian(ALE)framework.Additionally,a virtual element is introduced to model the contact segment in the cable-pulley system.A tension decay factor is employed to account for the friction in the contact segment.To validate the proposed method,a semi-analytical model based on D'Alembert's principle is established.Cross-verification is performed to validate the accuracy of both models.The model is further applied to simulate the rotation of the cable-driven manipulator with different structural parameters and frictional factors.The results from the analyses provide valuable guidance for the design and motion control of the in-space cable-driven manipulator.Finally,a prototype of a single module is manufactured and tested.Ground experiments are carried out to verify the kinematic and dynamic models.
基金supported by funding from the U.S.Department of Defense,Nos.W911NF-23-1-0276,W81XWH2211065the NIH,No.P41EB027062(all to DLK).
文摘Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury in humans are severely limited by the inaccessibility of living brain tissue,creating a need for in vitro model systems to study cellular mechanisms of degeneration and regeneration following injury.Here we describe methods to establish a 3D human brain tissue model,consisting of a silk-collagen composite scaffold seeded with human neurons,astrocytes,and microglia,to study neuro-regeneration after traumatic brain injury.Step-by-step fabrication,injury,and analytical assessments of the 3D“triculture”system are described.Using this tissue model system,we demonstrate that glial cells promote regeneration of neuronal networks within the injury site over several weeks post-injury.Further,we found that regenerating networks in the 3D triculture tissues did not secrete early markers of neurodegenerative disease,but displayed signs of excitatory/inhibitory imbalance,suggesting that pro-regenerative treatments for traumatic brain injury in the future may need to direct cell differentiation to promote proper function.The mechanical stability of this model system enables physiologically relevant impact injury and long-term culture capability,while its modular design enables modification of cell contents,extracellular matrix composition,and scaffold properties.This adaptability could allow the integration of patient-derived cells and genetic modifications to bridge research and clinical applications focused on personalized targeted therapies.This in vitro system provides a valuable platform for accelerating therapeutic advancements in traumatic brain injury and neurodegenerative disorders,ultimately improving patient outcomes.
基金supported by the Advanced Materials-National Science and Technology Major Project(Grant No.2025ZD0618401)the National Natural Science Foundation of China(Grant No.12504285)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20250472)NFSG grant from BITS-Pilani,Dubai campus。
文摘The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-time scenarios.This review begins with a concise overview of traditional tight-binding(TB)models,including both(semi-)empirical and first-principles approaches,establishing the foundation for understanding MLTB developments.We then present a systematic classification of existing MLTB methodologies,grouped into two major categories:direct prediction of TB Hamiltonian elements and inference of empirical parameters.A comparative analysis with other ML-based electronic structure models is also provided,highlighting the advancement of MLTB approaches.Finally,we explore the emerging MLTB application ecosystem,highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains.The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena.
基金financial support from the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant No.ZDJ2021-12)。
文摘The assessment of landslide susceptibility triggered by earthquakes serves as a fundamental basis for effective emergency response and post-disaster reconstruction efforts.However,current predictive models often face limitations in accuracy,with the prediction rates of most models ranging from 80%to 90%.This study introduces a new hybrid machine learning framework,termed the Subtractive Clustering Method-based Adaptive Neural Network Fuzzy Inference System(SCM-ANFIS),and evaluates its performance in the Wenchuan earthquake region.This region features distinctive geology(e.g.,Longmenshan Fault-governed complex tectonics)and abundant fundamental data;additionally,the 2008 Wenchuan Earthquake provides a pertinent case for earthquakeinduced landslide model evaluation.Based on a literature review and correlation analysis,this study systematically identified 12 key influencing factors that collectively characterize the region's high landslide susceptibility,shaped by intense seismic activity,complex terrain,and fragmented rock masses.Positive and negative samples were extracted as target variables through buffer sampling to calculate earthquake-induced landslide susceptibility.The susceptibility zoning map was then calibrated and generated by incorporating the regional landslide area percentage.The study concludes the following:(1)Compared to traditional machine learning approaches,the model demonstrates strong performance and stability,achieving a prediction accuracy of 98.5%.Approximately 97.89%of historically documented landslides in the Wenchuan region were located within areas identified as having high susceptibility,which aligns well with observed spatial distributions.(2)Increase in the buffer distance contributes to enhance prediction accuracy while a larger sample size improves model stability.(3)The model exhibits superior performance and possesses scalability for application in other regions,such as Jiuzhaigou and Luding.(4)Nonetheless,limitations remain regarding uncertainty,sample composition,algorithmic design,and practical implementation.Future research should focus on improving data precision and optimizing algorithmic frameworks.Overall,this study provides valuable support for landslide susceptibility assessments and contributes essential data for disaster risk mitigation efforts.
基金Southwest Hospital Boqing Innovation Fund,Grant/Award Number:2024BQCXJJ-9Fundings for Young Investigators of PLA,Grant/Award Number:2022-JCJQ-QT-004+3 种基金NSFC Key Projects of the Regional Innovation and Development Joint Fund,Grant/Award Number:U23A20413China Postdoctoral Science Foundation,Grant/Award Number:2023M744280National Natural Science Foundation of China,Grant/Award Number:82103778,82172449 and 82172489Southwest Hospital Postdoctoral Starting Fund,Grant/Award Number:5175ZA36BP。
文摘Background:Skeletal tuberculosis(TB)remains a persistent clinical and research chal-lenge due to its chronic course,osteolytic destruction,and the limitations of existing animal models,which often require high-level biosafety containment or fail to repli-cate human skeletal pathology.Methods:This study developed a biosafe,accessible,and versatile murine model of skeletal TB using Mycobacterium smegmatis,a fast-growing,nonpathogenic myco-bacterial species with high genomic homology to Mycobacterium tuberculosis.Three infection routes-subperiosteal calvarial injection,intratibial injection,and intra-cardiac inoculation-were systematically evaluated for their ability to induce lo-calized versus disseminated bone infection under standard biosafety level(BSL)-1 conditions.Results:Subperiosteal calvarial and intratibial injection of M.smegmatis induced local-ized bone lesions characterized by osteolysis,sequestrum formation,granulomatous inflammation,and increased osteoclast activity.Intratibial infection additionally trig-gered compartment-specific immune responses,including neutrophil and macrophage expansion,transient B-cell depletion,and activation of interferon-γ^(+)(IFN-γ^(+))T cells,reflecting active immune remodeling at the infection site.Systemic dissemination via intracardiac injection reproducibly generated progressive vertebral and tibial bone destruction with organized granuloma formation and immune cell infiltration but without prominent sequestrum formation.Compared to intratibial infection,intracar-diac delivery exhibited lower intragroup variability and more closely recapitulated the diffuse progression of extrapulmonary skeletal tuberculosis.Conclusions:This M.smegmatis-based murine model provides a straightforward,reliable,and immunopathologically relevant platform for exploring host-pathogen dynamics,immune-driven bone destruction,and early-stage therapeutic testing in skeletal TB,all within standard BSL-1 laboratories.This model fills a critical gap by enabling BSL-1 research into skeletal TB mechanisms and drug development.
文摘Background:In preclinical research,tumor growth inhibition in subcutaneous models is frequently employed to evaluate therapeutic efficacy;however,such models often lack clinical translatability.Methods:To better approximate clinical reality,taking the case of doxorubicin treatment,we utilized an orthotopic transplant and resection(OtR)strategy to systematically assess the effects of neoadjuvant chemotherapy,adjuvant chem-otherapy,and their combination on tumor growth,recurrence,and malignant progression.Results:Surprisingly,none of the treatments improved mouse survival,with adjuvant therapy even shortening it.Although neoadjuvant chemotherapy delayed preopera-tive tumor growth,and all regimens reduced recurrence rates,none effectively pre-vented metastasis.Furthermore,all treatment groups exhibited weight loss,indicative of chemotherapy-induced cachexia.Conclusions:Collectively,these findings demonstrate that reduced tumor growth in preclinical mouse models does not necessarily translate into overall survival benefit.Our results emphasize the critical importance of prioritizing metastasis prevention over tumor growth inhibition as a key efficacy endpoint in antitumor drug evaluation.
基金supported by the National Key Research and Development Program of China,grant number 2023YFB4203200。
文摘Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability.The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of wind speed and direction in the wind field,leading to an increased risk of load overload,severely affecting operational lifespan and reducing power generation efficiency.This impact is even more pronounced during the passage of a cold front.To address this issue,this paper proposes an independent variable-pitch control method that optimizes predictions by utilizing the spatiotemporal relationship between pre-observed cold front patterns and their dynamic propagation.First,a cold front and cold front propagation model suitable for engineering applications was derived.And a non-uniform inflow load model of turbine is established,which,combined with tower vibration response and rotor dynamic loads,accurately simulates the force distribution under complex inflow conditions.Subsequently,a pre-observation-based active cyclic pitch control method is presented,dynamically computing optimal pitch angle sequences by predicting wind field trends.This method eliminates the need for iterative optimization algorithms and reduces control latency to achieve proactive load management.Simulation verification shows that the proposed control strategy can effectively reduce key structural loads and increase power generation without relying on complex optimization algorithms.This method provides a practical solution for improving the economic benefits and operational reliability of wind farms under special wind conditions.
基金supported by the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202307)the Basic Research Fund of CAMS(No.2023Z016)+1 种基金the National Natural Scientific Foundation of China(No.42275037)the Jiangsu Collaborative Innovation Center for Climate Change。
文摘In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.
基金supported in part by the National Natural Science Foundation of China under 52125704 and 51937001.
文摘Interaction between the converter and the grid may lead to harmonic oscillations.The impedance-based method is an effective way to deal with the stability issue.In this study,the impedance-based method is used to investigate the small-signal stability of a cascaded 12-pulse line-commutated converter-based high-voltage direct current(LCC-HVDC)transmission system.In the modeling part,the impedance models of the single rectifier and inverter are established respectively with consideration to the effect of frequency coupling,which has improved the accuracy of the models.Based on the models,the AC impedance models of the cascaded LCC-HVDC transmission system are established both on the rectifier and inverter side.In the stability analysis part,the stability of the system is analyzed under different working conditions.The simulation results reveal that the established impedance model can properly represent the stability of this system.The findings of this study can provide a theoretical reference for the stability design and oscillation suppression strategy of LCC-HVDC transmission systems and LCC interconnected systems.
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Supported by the National Natural Science Foundation of China(Grant No.12261081).
文摘In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.
基金the support from the National Key Research and Development Program of China(Nos.2024YFB4607700 and 2018YFA0703000)the Natural Science Foundation of Zhejiang Province(Nos.LDQ23E050001 and LQ24H260006)+2 种基金the National Natural Science Foundation of China(Nos.62303290,52305325,and 52405305)Shanghai Magnolia Talent Program Pujiang Project(No.23PJD036)The project was also supported by the State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology(No.P2025-002).
文摘The intestine is a key component of the barrier,absorption,and immune systems,contributing significantly to maintaining internal homeostasis and influencing disease progression.Its distinctive physiological functions arise from a complex interplay between its structure and microenvironment.Recent advancements in bioengineering technologies now enable the construction of in vitro intestinal models that faithfully recapitulate the organizational and functional characteristics of native tissue.This review examines the interface between in vitro models and native intestinal biology,offering insights into the replication of organ functions from a manufacturing perspective.We explore bioengineering strategies that enable the mapping of cross-scale structures and the creation of biomimetic environments essential for physiological performance.Furthermore,we discuss pragmatic optimization strategies for applying these models to both physiological and pathological studies,thereby enhancing their translational potential for drug development,disease modeling,and personalized medicine.In contrast to previous reviews,this work proposes an engineering-centered framework for linking structural fabrication strategies to functional performance across intestinal model types.
基金supported by ScientificResearch Fund of National Health Commission of the People’s Republic of China-Major Science and Technology Program for Medicine and Health in Zhejiang Province(WKJ-ZJ-2406).
文摘Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.
基金The Youth Project of Tianjin Natural Science Foundation,Grant/Award Number:23JCQNJC01380。
文摘Background:The traditional method of heterotopic abdominal heart transplantation(HTx)involves crossclamping the inferior vena cava,which inevitably leads to bilateral lower limb ischemia(LI).This study first aimed to investigate the impact of LI on renal function in rats subjected to unilateral nephrectomy(UNx).Second,a modified method utilizing renal vessel-assisted anastomosis in rats with left UNx was compared with the traditional method for abdominal HTx.Methods:Male Sprague-Dawley rats were utilized as subjects for both experimental phases.In experiment 1,the animals were divided into four groups:sham operation group;LI group-rats undergoing occlusion of the abdominal aorta and vena cava below the renal vessels;UNx group-rats with left UNx;and LI+UNx group.All operated animals were monitored for up to 7 days for biochemical markers,renal histopathology,and survival rates.In experiment 2,we introduced the renal vessel-assisted method as the experimental group and compared it against the traditional method as the control within rat heterotopic HTx models.We assessed operative characteristics,echocardiography results,histological findings,and graft survival.Results:First,LI resulted in acute kidney dysfunction characterized by a decrease in 7day survival rates and creatinine clearance rates in both the LI and LI+UNx groups compared to the sham operation and UNx groups.Particularly,histopathological damage in the kidney and liver did not exhibit significant effects during this period.Second,the implementation of the renal vessel-assisted method significantly reduced bleeding volume at suture sites and enhanced the 7day survival rate compared to the traditional method.Conclusion:Acute kidney injury was induced by LI postoperation in treated rats.The renal vessel-assisted method demonstrated its effectiveness as a superior alternative that mitigates complications associated with the traditional method.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
文摘This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.
基金supported by the Fundamental Research Funds for the Central Universities in UIBE under Grant No.CXTD14-05。
文摘Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhibit complex patterns of zero AF episodes,which may arise from either true AF-free status(structural zeros)or missed AF episodes due to intermittent monitoring(random zeros).Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data.The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations.Unlike traditional approaches requiring fully specified zero-inflated models,the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions,with weights determined by binomial sizes.A closed-form test statistic is developed,and its asymptotic distribution is derived using estimating equations.Simulations demonstrate superior performance over existing methods,and real-world AF monitoring data validate the practical utility of our proposed test.
