Center pivot irrigation systems usually apply a relatively uniform amount of water to fields that are often inherently variable, which could lead to significant waste of water and energy. To address this issue, our te...Center pivot irrigation systems usually apply a relatively uniform amount of water to fields that are often inherently variable, which could lead to significant waste of water and energy. To address this issue, our team is now developing an Intelligent Center Pivot (ICP) by integrating sensor-based irrigation scheduling with variable rate irrigation technology. However, before this technology can be applied in commercial production, it is necessary to educate growers about its practicality and potential benefits. The objective of this study was to develop a portable tabletop intelligent center pivot model (ICPDemo) to demonstrate and promote adoption of the ICP technology. This paper describes an ICPDemo constructed in 2014, including the design specifications, electro-mechanical design, control strategy, and performance. The ICPDemo has performed according to design specifications and is successfully being used to demonstrate the benefits and effectiveness of ICP technology for irrigation scheduling.展开更多
The manufacturing of spiral groove structure of two-dimensional valve(2D valve)feedback mechanism has shortcomings of both high cost and time-consuming.This paper presents a novel configuration of rotary electro-mecha...The manufacturing of spiral groove structure of two-dimensional valve(2D valve)feedback mechanism has shortcomings of both high cost and time-consuming.This paper presents a novel configuration of rotary electro-mechanical converter with negative feedback mechanism(REMC-NFM)in order to replace the feedback mechanism of spiral groove and thus reduce cost of valve manufacturing.In order to rapidly and quantitative evaluate the driving and feedback performance of the REMC-NFM,an analytical model taking leakage flux,edge effect and permeability nonlinearity into account is formulated based on the equivalent magnetic circuit approach.Then the model is properly simplified in order to obtain the optimal pitch angle.FEM simulation is used to study the influence of crucial parameters on the performance of REMC-NFM.A prototype of REMC-NFM is designed and machined,and an exclusive experimental platform is built.The torque-angle characteristics,torque-displacement characteristics,and magnetic flux density in the working air gap with different excitation currents are measured.The experimental results are in good agreement with the analytical and FEM simulated results,which verifies the correctness of the analytical model.For torque-angle characteristics,the overall torque increases with both current and rotation angle,which reaches about 0.48 N·m with 1.5 A and 1.5°.While for torque-displacement characteristics,the overall torque increases with current yet decrease with armature displacement due to the negative feedback mechanism,which is about 0.16 N·m with 1.5 A and 0.8 mm.Besides,experimental results of conventional torque motor are compared with counterparts of REMC-NFM in order to validate the simplified model.The research indicates that the REMC-NFM can be potentially used as the electro-mechanical converter for 2D valves in civil servo areas.展开更多
In this paper,a coordinated control scheme for wind turbine generator(WTG)and supercapacitor energy storage system(ESS)is proposed for temporary frequency supports.Inertial control is designed by using generator torqu...In this paper,a coordinated control scheme for wind turbine generator(WTG)and supercapacitor energy storage system(ESS)is proposed for temporary frequency supports.Inertial control is designed by using generator torque limit considering the security of WTG system,while ESS releases its energy to compensate the sudden active power deficit during the recovery process of turbine rotor.WTG is modeled using the fatigue,aerodynamic,structure,turbulence(FAST)code,which identifies the mechanical loadings of the turbine and addresses electro-mechanical interactions in the wind energy system.A damping controller is augmented to the inertial control to suppress severe mechanical oscillations in the shaft and tower of the turbine during frequency supports.Furthermore,the result of small-signal stability analysis shows that the WTGESS tends to improve the stability of the whole multi-energy power grid.The major contributions of this paper will be validated by utilizing the proposed control method that combines the grid support capability and maintaining the integrity of structural design of the turbine for normal operations.展开更多
Permanent magnet synchronous motor based electro-mechanical actuation servo drives have widespread applications in the aviation field,such as unmanned aerial vehicle electric servos,electric cabin doors,and mechanical...Permanent magnet synchronous motor based electro-mechanical actuation servo drives have widespread applications in the aviation field,such as unmanned aerial vehicle electric servos,electric cabin doors,and mechanical arms.The performance of the servo drive,which encompasses the response to the torque,efficiency,control bandwidth and the steady-state positioning accuracy,significantly influences the performance of the aviation actuation.Consequently,enhancing the control bandwidth and refining the positioning accuracy of aviation electro-mechanical actuation servo drives have emerged as a focal point of research.This paper investigates the multi-source disturbances present in aviation electro-mechanical actuation servo systems and summarizes recent research on high-performance servo control methods based on active disturbance rejection control(ADRC).We present a comprehensive overview of the research status pertaining to servo control architecture,strategies for suppressing disturbances in the current loop,and ADRC-based strategies for the position loop.We delineate the research challenges and difficulties encountered by aviation electro-mechanical actuation servo drive control technology.展开更多
The accepted doping ion in Ti^(4+)-site of PbZr_(y)Ti_(1–y)O_(3)(PZT)-based piezoelectric ceramics is a well-known method to increase mechanical quality factor(Q_(m)),since the acceptor coupled by oxygen vacancy beco...The accepted doping ion in Ti^(4+)-site of PbZr_(y)Ti_(1–y)O_(3)(PZT)-based piezoelectric ceramics is a well-known method to increase mechanical quality factor(Q_(m)),since the acceptor coupled by oxygen vacancy becomes defect dipole,which prevents the domain rotation.In this field,a serious problem is that generally,Qm decreases as the temperature(T)increases,since the oxygen vacancies are decoupled from the defect dipoles.In this work,Q_(m) of Pb_(0.95)Sr_(0.05)(Zr_(0.53)Ti_(0.47))O_(3)(PSZT)ceramics doped by 0.40%Fe_(2)O_(3)(in mole)abnormally increases as T increases,of which the Qm and piezoelectric coefficient(d_(33))at room temperature and Curie temperature(TC)are 507,292 pC/N,and 345℃,respectively.The maximum Qm of 824 was achieved in the range of 120–160℃,which is 62.52%higher than that at room temperature,while the dynamic piezoelectric constant(d_(31))was just slightly decreased by 3.85%.X-ray diffraction(XRD)and piezoresponse force microscopy results show that the interplanar spacing and the fine domains form as temperature increases,and the thermally stimulated depolarization current shows that the defect dipoles are stable even the temperature up to 240℃.It can be deduced that the aggregation of oxygen vacancies near the fine domains and defect dipole can be stable up to 240℃,which pins domain rotation,resulting in the enhanced Q_(m) with the increasing temperature.These results give a potential path to design high Q_(m) at high temperature.展开更多
The precise computation of nanoelectromechanical switches’(NEMS)multi-physical interactions requires advanced numerical models and is a crucial part of the development of micro-and nano-systems.This paper presents a ...The precise computation of nanoelectromechanical switches’(NEMS)multi-physical interactions requires advanced numerical models and is a crucial part of the development of micro-and nano-systems.This paper presents a novel compound numerical method to study the instability of a functionally graded(FG)beam-type NEMS,considering surface elasticity effects as stated by Gurtin-Murdoch theory in an Euler-Bernoulli beam.The presented method is based on a combination of the Method of Adjoints(MoA)together with the Bézier-based multistep technique.By utilizing the MoA,a boundary value problem(BVP)is turned into an initial value problem(IVP).The resulting IVP is then solved by employing a cost-efficient multi-step process.It is demonstrated that the mentioned method can arrive at a high level of accuracy.Furthermore,it is revealed that the stability of the presented methodology is far better than that of other common multi-step methods,such as Adams-Bashforth,particularly at higher step sizes.Finally,the effects of axially functionally graded(FG)properties on the pull-in phenomenon and the main design parameters of NEMS,including the detachment length,are inspected.It was shown that the main parameter of design is the modulus of elasticity of the material,as Silver(Ag),which had better mechanical properties,showed almost a 6%improvement compared to aluminum(Al).However,by applying the correct amount of material with sturdier surface parameters,such as Aluminum(Al),at certain points,the nanobeams’functionality can be improved even further by around 1.5%.展开更多
Bladder cancer(BC)is a common malignancy and among the leading causes of cancer death worldwide.Analysis of BC cells is of great significance for clinical diagnosis and disease treatment.Current approaches rely mainly...Bladder cancer(BC)is a common malignancy and among the leading causes of cancer death worldwide.Analysis of BC cells is of great significance for clinical diagnosis and disease treatment.Current approaches rely mainly on imaging-based technology,which requires complex staining and sophisticated instrumentation.In this work,we develop a label-free method based on artificial intelligence(AI)-assisted impedance-based flow cytometry(IFC)to differentiate between various BC cells and epithelial cells at single-cell resolution.By applying multiple-frequency excitations,the electrical characteristics of cells,including membrane and nuclear opacities,are extracted,allowing distinction to be made between epithelial cells,low-grade,and high-grade BC cells.Through the use of a constriction channel,the electro-mechanical properties associated with active deformation behavior of cells are investigated,and it is demonstrated that BC cells have a greater capability of shape recovery,an observation that further increases differentiation accuracy.With the assistance of a convolutional neural network-based AI algorithm,IFC is able to effectively differentiate various BC and epithelial cells with accuracies of over 95%.In addition,different grades of BC cells are successfully differentiated in both spiked mixed samples and bladder tumor tissues.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
Braking on low adhesion-coefficient roads, hybrid electric vehicle＇s motor regenerative torque is switched off to safeguard the normal anti-lock braking system （ABS） fimction. When the ABS control is terminated, th...Braking on low adhesion-coefficient roads, hybrid electric vehicle＇s motor regenerative torque is switched off to safeguard the normal anti-lock braking system （ABS） fimction. When the ABS control is terminated, the motor regenerative braking is readmitted. Aiming at avoiding permanent cycles from hydraulic anti-lock braking to motor regenerative braking, a novel electro-mechanical hybrid anti-lock braking system using fuzzy logic is designed. Different from the traditional single control structure, this system has a two-layered hierarchical structure, The first layer is responsible for harmonious adjustment or interaction between regenerative system and anti-lock braking system. The second layer is responsible for braking torque distribution and adjustment. The closed-loop simulation model is built. Control strategy and method for coordination between regenerative and anti-lock braking are developed. Simulation braking on low adhesion-coefficient roads with fuzzy logic control and real vehicle braking field test are presented. The results from simulating analysis and experiment show braking performance of the vehicle is perfect, harmonious coordination between regenerative and anti-lock braking function, significant amount of braking energy can be recovered and the proposed control strategy and method are effective.展开更多
Automotive industry,as an important pillar of the national economy,has been rapidly developing in recent years.But proplems such as energy comsumption and environmental pollution are posed at the same time.Electro-mec...Automotive industry,as an important pillar of the national economy,has been rapidly developing in recent years.But proplems such as energy comsumption and environmental pollution are posed at the same time.Electro-mechanical variable transmission system is considered one of avilable workarounds.It is brought forward a kind of design methods of dual-mode electro-mechanical variable transmission system rotational speed characteristics and dual-mode drive diagrams.With the motor operating behavior of running in four quadrants and the speed characteristics of the simple internal and external meshing single planetary gear train,four kinds of dual-mode electro-mechanical transmission system scheme are designed.And the velocity,torque and power characteristics of one of the programs are analyzed.The magnitude of the electric split-flow power is an important factor which influences the system performance,so in the parameters matching design,it needs to reduce the power needs under the first mode of the motor.The motor,output rotational speed range and the position of the mode switching point have relationships with the characteristics design of the planetary gear set.The analysis method is to provide a reference for hybrid vehicles＇ design.As the involved rotational speed and torque relationships are the natural contact of every part of transmission system,a theory basis of system program and performance analysis is provided.展开更多
文摘Center pivot irrigation systems usually apply a relatively uniform amount of water to fields that are often inherently variable, which could lead to significant waste of water and energy. To address this issue, our team is now developing an Intelligent Center Pivot (ICP) by integrating sensor-based irrigation scheduling with variable rate irrigation technology. However, before this technology can be applied in commercial production, it is necessary to educate growers about its practicality and potential benefits. The objective of this study was to develop a portable tabletop intelligent center pivot model (ICPDemo) to demonstrate and promote adoption of the ICP technology. This paper describes an ICPDemo constructed in 2014, including the design specifications, electro-mechanical design, control strategy, and performance. The ICPDemo has performed according to design specifications and is successfully being used to demonstrate the benefits and effectiveness of ICP technology for irrigation scheduling.
基金National Natural Science Foundation of China(Grant Nos.51975524,51405443)National Key Research and Development Program of China(Grant No.2019YFB2005200).
文摘The manufacturing of spiral groove structure of two-dimensional valve(2D valve)feedback mechanism has shortcomings of both high cost and time-consuming.This paper presents a novel configuration of rotary electro-mechanical converter with negative feedback mechanism(REMC-NFM)in order to replace the feedback mechanism of spiral groove and thus reduce cost of valve manufacturing.In order to rapidly and quantitative evaluate the driving and feedback performance of the REMC-NFM,an analytical model taking leakage flux,edge effect and permeability nonlinearity into account is formulated based on the equivalent magnetic circuit approach.Then the model is properly simplified in order to obtain the optimal pitch angle.FEM simulation is used to study the influence of crucial parameters on the performance of REMC-NFM.A prototype of REMC-NFM is designed and machined,and an exclusive experimental platform is built.The torque-angle characteristics,torque-displacement characteristics,and magnetic flux density in the working air gap with different excitation currents are measured.The experimental results are in good agreement with the analytical and FEM simulated results,which verifies the correctness of the analytical model.For torque-angle characteristics,the overall torque increases with both current and rotation angle,which reaches about 0.48 N·m with 1.5 A and 1.5°.While for torque-displacement characteristics,the overall torque increases with current yet decrease with armature displacement due to the negative feedback mechanism,which is about 0.16 N·m with 1.5 A and 0.8 mm.Besides,experimental results of conventional torque motor are compared with counterparts of REMC-NFM in order to validate the simplified model.The research indicates that the REMC-NFM can be potentially used as the electro-mechanical converter for 2D valves in civil servo areas.
基金supported by the U.S.National science foundation(No.1711951)
文摘In this paper,a coordinated control scheme for wind turbine generator(WTG)and supercapacitor energy storage system(ESS)is proposed for temporary frequency supports.Inertial control is designed by using generator torque limit considering the security of WTG system,while ESS releases its energy to compensate the sudden active power deficit during the recovery process of turbine rotor.WTG is modeled using the fatigue,aerodynamic,structure,turbulence(FAST)code,which identifies the mechanical loadings of the turbine and addresses electro-mechanical interactions in the wind energy system.A damping controller is augmented to the inertial control to suppress severe mechanical oscillations in the shaft and tower of the turbine during frequency supports.Furthermore,the result of small-signal stability analysis shows that the WTGESS tends to improve the stability of the whole multi-energy power grid.The major contributions of this paper will be validated by utilizing the proposed control method that combines the grid support capability and maintaining the integrity of structural design of the turbine for normal operations.
基金supported by the National Natural Science Foundation of China(Nos.52177059 and 52407064).
文摘Permanent magnet synchronous motor based electro-mechanical actuation servo drives have widespread applications in the aviation field,such as unmanned aerial vehicle electric servos,electric cabin doors,and mechanical arms.The performance of the servo drive,which encompasses the response to the torque,efficiency,control bandwidth and the steady-state positioning accuracy,significantly influences the performance of the aviation actuation.Consequently,enhancing the control bandwidth and refining the positioning accuracy of aviation electro-mechanical actuation servo drives have emerged as a focal point of research.This paper investigates the multi-source disturbances present in aviation electro-mechanical actuation servo systems and summarizes recent research on high-performance servo control methods based on active disturbance rejection control(ADRC).We present a comprehensive overview of the research status pertaining to servo control architecture,strategies for suppressing disturbances in the current loop,and ADRC-based strategies for the position loop.We delineate the research challenges and difficulties encountered by aviation electro-mechanical actuation servo drive control technology.
基金National Natural Science Foundation of China(U2241242)National Key R&D Program of China(2023YFB3812000,2021YFA0716502)。
文摘The accepted doping ion in Ti^(4+)-site of PbZr_(y)Ti_(1–y)O_(3)(PZT)-based piezoelectric ceramics is a well-known method to increase mechanical quality factor(Q_(m)),since the acceptor coupled by oxygen vacancy becomes defect dipole,which prevents the domain rotation.In this field,a serious problem is that generally,Qm decreases as the temperature(T)increases,since the oxygen vacancies are decoupled from the defect dipoles.In this work,Q_(m) of Pb_(0.95)Sr_(0.05)(Zr_(0.53)Ti_(0.47))O_(3)(PSZT)ceramics doped by 0.40%Fe_(2)O_(3)(in mole)abnormally increases as T increases,of which the Qm and piezoelectric coefficient(d_(33))at room temperature and Curie temperature(TC)are 507,292 pC/N,and 345℃,respectively.The maximum Qm of 824 was achieved in the range of 120–160℃,which is 62.52%higher than that at room temperature,while the dynamic piezoelectric constant(d_(31))was just slightly decreased by 3.85%.X-ray diffraction(XRD)and piezoresponse force microscopy results show that the interplanar spacing and the fine domains form as temperature increases,and the thermally stimulated depolarization current shows that the defect dipoles are stable even the temperature up to 240℃.It can be deduced that the aggregation of oxygen vacancies near the fine domains and defect dipole can be stable up to 240℃,which pins domain rotation,resulting in the enhanced Q_(m) with the increasing temperature.These results give a potential path to design high Q_(m) at high temperature.
文摘The precise computation of nanoelectromechanical switches’(NEMS)multi-physical interactions requires advanced numerical models and is a crucial part of the development of micro-and nano-systems.This paper presents a novel compound numerical method to study the instability of a functionally graded(FG)beam-type NEMS,considering surface elasticity effects as stated by Gurtin-Murdoch theory in an Euler-Bernoulli beam.The presented method is based on a combination of the Method of Adjoints(MoA)together with the Bézier-based multistep technique.By utilizing the MoA,a boundary value problem(BVP)is turned into an initial value problem(IVP).The resulting IVP is then solved by employing a cost-efficient multi-step process.It is demonstrated that the mentioned method can arrive at a high level of accuracy.Furthermore,it is revealed that the stability of the presented methodology is far better than that of other common multi-step methods,such as Adams-Bashforth,particularly at higher step sizes.Finally,the effects of axially functionally graded(FG)properties on the pull-in phenomenon and the main design parameters of NEMS,including the detachment length,are inspected.It was shown that the main parameter of design is the modulus of elasticity of the material,as Silver(Ag),which had better mechanical properties,showed almost a 6%improvement compared to aluminum(Al).However,by applying the correct amount of material with sturdier surface parameters,such as Aluminum(Al),at certain points,the nanobeams’functionality can be improved even further by around 1.5%.
基金financial support from the National Natural Science Foundation of China(NSFC Grant No.22076138)the National Natural Science Foundation of China(NSFC Grant No.62174119).
文摘Bladder cancer(BC)is a common malignancy and among the leading causes of cancer death worldwide.Analysis of BC cells is of great significance for clinical diagnosis and disease treatment.Current approaches rely mainly on imaging-based technology,which requires complex staining and sophisticated instrumentation.In this work,we develop a label-free method based on artificial intelligence(AI)-assisted impedance-based flow cytometry(IFC)to differentiate between various BC cells and epithelial cells at single-cell resolution.By applying multiple-frequency excitations,the electrical characteristics of cells,including membrane and nuclear opacities,are extracted,allowing distinction to be made between epithelial cells,low-grade,and high-grade BC cells.Through the use of a constriction channel,the electro-mechanical properties associated with active deformation behavior of cells are investigated,and it is demonstrated that BC cells have a greater capability of shape recovery,an observation that further increases differentiation accuracy.With the assistance of a convolutional neural network-based AI algorithm,IFC is able to effectively differentiate various BC and epithelial cells with accuracies of over 95%.In addition,different grades of BC cells are successfully differentiated in both spiked mixed samples and bladder tumor tissues.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
基金supported by National Development and Reform Commission of China (Grant No. 2005934)
文摘Braking on low adhesion-coefficient roads, hybrid electric vehicle＇s motor regenerative torque is switched off to safeguard the normal anti-lock braking system （ABS） fimction. When the ABS control is terminated, the motor regenerative braking is readmitted. Aiming at avoiding permanent cycles from hydraulic anti-lock braking to motor regenerative braking, a novel electro-mechanical hybrid anti-lock braking system using fuzzy logic is designed. Different from the traditional single control structure, this system has a two-layered hierarchical structure, The first layer is responsible for harmonious adjustment or interaction between regenerative system and anti-lock braking system. The second layer is responsible for braking torque distribution and adjustment. The closed-loop simulation model is built. Control strategy and method for coordination between regenerative and anti-lock braking are developed. Simulation braking on low adhesion-coefficient roads with fuzzy logic control and real vehicle braking field test are presented. The results from simulating analysis and experiment show braking performance of the vehicle is perfect, harmonious coordination between regenerative and anti-lock braking function, significant amount of braking energy can be recovered and the proposed control strategy and method are effective.
基金supported by Foundation of National Key Lab of Vehicular Transmission of China
文摘Automotive industry,as an important pillar of the national economy,has been rapidly developing in recent years.But proplems such as energy comsumption and environmental pollution are posed at the same time.Electro-mechanical variable transmission system is considered one of avilable workarounds.It is brought forward a kind of design methods of dual-mode electro-mechanical variable transmission system rotational speed characteristics and dual-mode drive diagrams.With the motor operating behavior of running in four quadrants and the speed characteristics of the simple internal and external meshing single planetary gear train,four kinds of dual-mode electro-mechanical transmission system scheme are designed.And the velocity,torque and power characteristics of one of the programs are analyzed.The magnitude of the electric split-flow power is an important factor which influences the system performance,so in the parameters matching design,it needs to reduce the power needs under the first mode of the motor.The motor,output rotational speed range and the position of the mode switching point have relationships with the characteristics design of the planetary gear set.The analysis method is to provide a reference for hybrid vehicles＇ design.As the involved rotational speed and torque relationships are the natural contact of every part of transmission system,a theory basis of system program and performance analysis is provided.
