Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disruptin...Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disrupting the neural connections that allow communication between the brain and the rest of the body, which results in varying degrees of motor and sensory impairment. Disconnection in the spinal tracts is an irreversible condition owing to the poor capacity for spontaneous axonal regeneration in the affected neurons.展开更多
During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive...During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h^(2)/(4√2),where h is the piecewise length.展开更多
Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing...Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.展开更多
As a critical component of the in situ stress state,determination of the minimum horizontal principal stress plays a significant role in both geotechnical and petroleum engineering.To this end,a gene expression progra...As a critical component of the in situ stress state,determination of the minimum horizontal principal stress plays a significant role in both geotechnical and petroleum engineering.To this end,a gene expression programming(GEP)algorithm-based model,in which the data of borehole breakout size,vertical principal stress,and rock strength characteristics are used as the inputs,is proposed to predict the minimum horizontal principal stress.Seventy-nine(79)samples with seven features are collected to construct the minimum horizontal principal stress dataset used for training models.Twenty-four(24)GEP model hyperparameter sets were configured to explore the key parameter combinations among the inputs and their potential relationships with the minimum horizontal principal stresses.Model performance was evaluated using root mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R^(2)).By comparing predictive performance and parameter composition,two models were selected from 24 GEP models that demonstrated excellent predictive performance and simpler parameter composition.Compared with prevalent models,the results indicate that the two selected GEP models have better performance on the test set(R^(2)=0.9568 and 0.9621).Additionally,the results conducted by SHapley Additive exPlanations(SHAP)sensitivity analysis and Local Interpretable Model-agnostic Explanations(LIME)demonstrate that the vertical principal stress is the most influential parameter in both GEP models.The two GEP models have simple parameter compositions as well as stable and excellent prediction performance,which is a viable method for predicting the minimum horizontal principal stresses.展开更多
Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells c...Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.展开更多
The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functio...The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
Due to the small size,active mobility,and intrinsic softness,miniature soft robots hold promising po-tentials in reaching the deep region inside living bodies otherwise inaccessible with compelling agility,adaptabilit...Due to the small size,active mobility,and intrinsic softness,miniature soft robots hold promising po-tentials in reaching the deep region inside living bodies otherwise inaccessible with compelling agility,adaptability and safety.Various materials and actuation strategies have been developed for creating soft robots,among which,ferromagnetic soft materials that self-actuate in response to external magnetic fields have attracted worldwide attention due to their remote controllability and excellent compatibil-ity with biological tissues.This review presents comprehensive and systematic research advancements in the design,fabrication,and applications of ferromagnetic soft materials for miniature robots,providing in-sights into their potential use in biomedical fields and beyond.The programming strategies of ferromag-netic soft materials are summarized and classified,including mold-assisted programming,3D printing-assisted programming,microassembly-assisted programming,and magnetization reprogramming.Each approach possesses unique advantages in manipulating the magnetic responsiveness of ferromagnetic soft materials to achieve outstanding actuation and deformation performances.We then discuss the biomedi-cal applications of ferromagnetic soft material-based soft robots(e.g.,minimally invasive surgery,targeted delivery,and tissue engineering),highlighting their potentials in revolutionizing biomedical technologies.This review also points out the current challenges and provides insights into future research directions,which we hope can serve as a useful reference for the development of next-generation adaptive miniature robots.展开更多
Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we pr...Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.展开更多
基金financially supported by Ministerio de Ciencia e Innovación projects SAF2017-82736-C2-1-R to MTMFin Universidad Autónoma de Madrid and by Fundación Universidad Francisco de Vitoria to JS+2 种基金a predoctoral scholarship from Fundación Universidad Francisco de Vitoriafinancial support from a 6-month contract from Universidad Autónoma de Madrida 3-month contract from the School of Medicine of Universidad Francisco de Vitoria。
文摘Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disrupting the neural connections that allow communication between the brain and the rest of the body, which results in varying degrees of motor and sensory impairment. Disconnection in the spinal tracts is an irreversible condition owing to the poor capacity for spontaneous axonal regeneration in the affected neurons.
基金supported by the National Natural Sci‐ence Foundation of China(Grant No.62306325)。
文摘During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h^(2)/(4√2),where h is the piecewise length.
文摘Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.
基金partially supported by the National Natural Science Foundation of China(Grant Nos.42177164 and 52474121)the Distinguished Youth Science Foundation of Hunan Province of China(Grant No.2022JJ10073).
文摘As a critical component of the in situ stress state,determination of the minimum horizontal principal stress plays a significant role in both geotechnical and petroleum engineering.To this end,a gene expression programming(GEP)algorithm-based model,in which the data of borehole breakout size,vertical principal stress,and rock strength characteristics are used as the inputs,is proposed to predict the minimum horizontal principal stress.Seventy-nine(79)samples with seven features are collected to construct the minimum horizontal principal stress dataset used for training models.Twenty-four(24)GEP model hyperparameter sets were configured to explore the key parameter combinations among the inputs and their potential relationships with the minimum horizontal principal stresses.Model performance was evaluated using root mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R^(2)).By comparing predictive performance and parameter composition,two models were selected from 24 GEP models that demonstrated excellent predictive performance and simpler parameter composition.Compared with prevalent models,the results indicate that the two selected GEP models have better performance on the test set(R^(2)=0.9568 and 0.9621).Additionally,the results conducted by SHapley Additive exPlanations(SHAP)sensitivity analysis and Local Interpretable Model-agnostic Explanations(LIME)demonstrate that the vertical principal stress is the most influential parameter in both GEP models.The two GEP models have simple parameter compositions as well as stable and excellent prediction performance,which is a viable method for predicting the minimum horizontal principal stresses.
基金supported by Canada First Research Excellence Fund,Medicine by Design(to CMM)。
文摘Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.
基金supported by National Institute on Aging(NIH-NIA)R21 AG074152(to KMA)National Institute of Allergy and Infectious Diseases(NIAID)grant DP2 AI171150(to KMA)Department of Defense(DoD)grant AZ210089(to KMA)。
文摘The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金the National Key R&D Program of China(No.2023YFE0208700)National Natural Sci-ence Foundation of China(No.92163109 and 52072095)+7 种基金Shenzhen Science and Technology Program(No.RCJC20231211090000001,GXWD20231129101105001)the National Natural Science Foundation of China(No.52205590)the Natural Science Foundation of Jiangsu Province(No.BK20220834)the Start-up Research Fund of Southeast University(No.RF1028623098)the State Key Laboratory of Robotics and Systems(HIT)(No.SKLRS-2024-KF-11)National Natural Science Foundation of China(No.52202348)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011491)Shenzhen Science and Technology Program(Nos.GXWD20220818224716001,KJZD20231023100302006).
文摘Due to the small size,active mobility,and intrinsic softness,miniature soft robots hold promising po-tentials in reaching the deep region inside living bodies otherwise inaccessible with compelling agility,adaptability and safety.Various materials and actuation strategies have been developed for creating soft robots,among which,ferromagnetic soft materials that self-actuate in response to external magnetic fields have attracted worldwide attention due to their remote controllability and excellent compatibil-ity with biological tissues.This review presents comprehensive and systematic research advancements in the design,fabrication,and applications of ferromagnetic soft materials for miniature robots,providing in-sights into their potential use in biomedical fields and beyond.The programming strategies of ferromag-netic soft materials are summarized and classified,including mold-assisted programming,3D printing-assisted programming,microassembly-assisted programming,and magnetization reprogramming.Each approach possesses unique advantages in manipulating the magnetic responsiveness of ferromagnetic soft materials to achieve outstanding actuation and deformation performances.We then discuss the biomedi-cal applications of ferromagnetic soft material-based soft robots(e.g.,minimally invasive surgery,targeted delivery,and tissue engineering),highlighting their potentials in revolutionizing biomedical technologies.This review also points out the current challenges and provides insights into future research directions,which we hope can serve as a useful reference for the development of next-generation adaptive miniature robots.
基金supported in part by the National Key Research and Development Program of China(2024YFB4709100,2021YFE0206100)the National Natural Science Foundation of China(62073321)+1 种基金the National Defense Basic Scientific Research Program(JCKY2019203C029)the Science and Technology Development Fund,Macao SAR,China(0015/2020/AMJ)
文摘Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.
