For the maximal space-like hypersurface defined on 2-dimensional space forms,based on the regularity and the strict convexity of the level sets,the steepest descents are well defined.In this paper,we come to estimate ...For the maximal space-like hypersurface defined on 2-dimensional space forms,based on the regularity and the strict convexity of the level sets,the steepest descents are well defined.In this paper,we come to estimate the curvature of its steepest descents by deriving a differential equality.展开更多
Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces.The trapezoidal cavity form is compared with its thermal and flow performance,and it is reveal...Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces.The trapezoidal cavity form is compared with its thermal and flow performance,and it is revealed that trapezoidal fins tend to be more efficient,particularly when material optimization is critical.Motivated by the increasing need for sustainable energy management,this work analyses the thermal performance of inclined trapezoidal and rectangular porous fins utilising a unique hybrid nanofluid.The effectiveness of nanoparticles in a working fluid is primarily determined by their thermophysical properties;hence,optimising these properties can significantly improve overall performance.This study considers the dispersion of Graphene Oxide(GO)and Molybdenum Disulfide in the base fluid,engine oil.Temperature profiles are analysed by altering the radiative,porosity,wet porous,and angle of inclination parameters.Surface and contour plots are constructed by using the Lobatto IIIa Collocation Method with BVP5C solver in MATLAB and Gradient Descent Optimisation to predict the combined heat transfer rate.According to the study,fluid temperature consistently decreases when the angle of inclination,wet porous parameter,porosity parameter,and radiative parameter increase,suggesting significantly improved heat dissipation.The trapezoidal fin consistently exhibits a superior heat transfer mechanism than a rectangular fin.It is found that the trapezoidal fin transmits heat at a rate that is 0.05%higher than that of the rectangular fin.Validation of the present study is done through the comparison of previous studies.This research provides useful design insights for sophisticated engineering uses,including electrical cooling devices,heat exchangers,radiators,and solar heaters.展开更多
The Hongyancun subway station in Chongqing,China,is 116 meters deep and the difference in air pressure often leaves users with clogged(堵塞的)ears when accessed via its elevator.When the air pressure outside the eardr...The Hongyancun subway station in Chongqing,China,is 116 meters deep and the difference in air pressure often leaves users with clogged(堵塞的)ears when accessed via its elevator.When the air pressure outside the eardrum(耳膜)becomes different than the pressure inside,you experience ear barotrauma(气压伤).It occurs most often during steep ascents and descents and is usually associated with plane take⁃offs and landings,or driving up or down mountains.Most subway stations dont usually cause ear barotrauma,because they arent deep or steep enough for your ears to register a significant enough difference in air pressure.But taking the elevator to reach Chinas deepest subway station might actually clog up your ears.Thats because it is located 116 meters below the surface,which is the equivalent of about 40 floors underground.展开更多
In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradien...In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.展开更多
This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The ba...This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The base station(BS)provides short packet services to ground users using the non-orthogonal multiple access(NOMA)protocol through UAV-RIS,while preventing eavesdropper attacks.To optimize SEE,a joint optimization is performed concerning power allocation,UAV position,decoding order,and RIS phase shifts.An iterative algorithm based on block coordinate descent is proposed for mixed-integer non-convex SEE optimization problem.The original problem is decomposed into three sub-problems,solved alternately using successive convex approximation(SCA),quadratic transformation,penalty function,and semi-definite programming(SDP).Simulation results demonstrate the performance of the UAV-RIS-enhanced short-packet system under different parameters and verify the algorithm’s convergence.Compared to benchmark schemes such as orthogonal multiple access,long packet communication,and sum SEE,the proposed UAV-RIS-enhanced short-packet scheme achieves the higher minimum user’s SEE.展开更多
Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structur...Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structure that pulls the testis into the scrotum.However,the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated.To identify the androgen-regulated genes,we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout(Lhcgr KO)mice,an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency.We found that the expression of secreted protein acidic and rich in cysteine(SPARC)-related modular calcium binding 1(Smoc1)was the most severely suppressed at both the transcript and protein levels,while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice.The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist,flutamide.In addition,in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells.In the cultures of myogenic differentiation medium,both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7(Pax7)and myogenic factor 5(Myf5).After short-interfering RNA-mediated knocking down of Smoc1,the expression of Pax7 and Myf5 diminished,and testosterone alone did not recover,but additional SMOC1 did.These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network...Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models.Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully.Most existing initialization methods mainly focus on gradient flow-related problems,such as gradient vanishing or exploding,or other existing approaches that require extra models that do not consider our setting,which is more practical.To address these problems,we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully connected layer in neural networks froma small set of training data with fewer classes.The approach relies on partitioning the output values from pre-trained models for a small set into two separate intervals determined by the targets.This process is framed as an optimization problem for each output neuron and class.The optimization selects the highest values as weights,considering their direction towards the respective classes.Furthermore,empirical 145 experiments involve a variety of neural networkmodels tested acrossmultiple benchmarks and domains,occasionally yielding accuracies comparable to those achieved with gradient descent methods by using only small subsets.展开更多
Obstructed defecation syndrome(ODS)represents an important cause of constipation,primarily arising from dysfunctions within the pelvic floor.Characterized by an inability to complete defecation or effectively evacuate...Obstructed defecation syndrome(ODS)represents an important cause of constipation,primarily arising from dysfunctions within the pelvic floor.Characterized by an inability to complete defecation or effectively evacuate fecal material despite the urge to defecate,ODS results in a persistent sensation of incomplete evacuation and often requires excessive straining during defecation.Conventional clinical examinations fail to adequately assess the complex dynamic dysfunctions of the pelvic floor and anorectal region.Magnetic resonance defecography(MRD),a sophisticated form of dynamic pelvic floor imaging,provides a comprehensive,non-invasive means of visualizing and quantifying various anorectal and pelvic floor abnormalities.By allowing detailed assessment of structural and functional deficits during the defecation process,MRD plays a crucial role in the diagnostic workup of ODS,enabling colorectal surgeons to formulate more precise and individualized treatment strategies.This manuscript highlights the important anatomical and functional disorders of pelvic floor that are associated with ODS.展开更多
An optimal feedback guidance law with disturbance rejection objective is proposed for endoatmospheric powered descent.This guidance law with an affine form is derived by solving a novel problem called Endoatmospheric ...An optimal feedback guidance law with disturbance rejection objective is proposed for endoatmospheric powered descent.This guidance law with an affine form is derived by solving a novel problem called Endoatmospheric Powered Descent Guidance with Disturbance Rejection(Endo-PDG-DR).The key idea of formulating the Endo-PDG-DR problem is dividing disturbances into two parts,modeled and unmodeled disturbances:the modeled disturbance is proactively exploited by augmenting it as a new state of a dynamics model;the unmodeled disturbance is reactively attenuated in terms of its effect on the guidance performance by adjoining a parameterized time-varying quadratic performance index in the proposed optimal guidance problem.A Pseudospectral Differential Dynamic Programming(PDDP)method is developed to solve the Endo-PDG-DR problem,and correspondingly a robust neighboring optimal state feedback law is obtained,which has two synergistic functionalities.One is adaptive optimal steering to accommodate the modeled disturbance,and the other is disturbance attenuation to compensate for the state perturbation effect induced by the unmodeled disturbance.Using the derived feedback guidance law,a disturbance rejection level is quantified,and is correspondingly optimized by designing a quadratic weighting parameter tuning law.The numerical computations of interest are performed within a pseudospectral setting,ensuring polynomial analytical solution,high computational efficiency,and reliable convergence.展开更多
Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
Adaptive graph neural networks(AGNNs)have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables.This article intro...Adaptive graph neural networks(AGNNs)have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables.This article introduces a novel GNN framework,termed entropy-regularized ensemble adaptive graph(E^(2)AG),aimed at enhancing the predictive accuracy of AGNNs.Specifically,this work pioneers a novel AGNN learning approach based on mirror descent,which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs.Subsequently,motivated by multi-head self-attention mechanism,the training of ensembled AGNNs is rigorously examined within this framework,incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph.After that,the architecture and training algorithm of the model are then concisely summarized.Finally,to ascertain the efficacy of the proposed E^(2)AG model,extensive experiments are conducted on real-world industrial datasets.The evaluation focuses on prediction accuracy,model efficacy,and sensitivity analysis,demonstrating the superiority of E^(2)AG in industrial soft sensing applications.展开更多
Reconfigurable Intelligent Surfaces(RISs)enable programmable wireless environments and thus have great potential for enhancing physical layer security.However,the security gain of conventional passive RISs is often li...Reconfigurable Intelligent Surfaces(RISs)enable programmable wireless environments and thus have great potential for enhancing physical layer security.However,the security gain of conventional passive RISs is often limited by the“multiplicative fading”effect through reflection links,which becomes severe in the case of double reflections and significantly degrades the security performance.In this paper,we consider a wireless system that consists of a fixed passive RIS and an Unmanned Aerial Vehicle(UAV)-mounted active RIS,where the UAV-enabled aerial amplification and reflection are exploited to compensate for the multiplicative fading effect.We formulate the problem to maximize the secrecy rate by jointly considering the optimal deployment of the UAV-based active RIS and the reflection coefficients at both the passive and active RISs.To enable efficient algorithm design,we decompose the problem into two layers:the outer layer optimizes the UAV deployment through deep reinforcement learning,while the inner layer solves the beamforming and reflection design using a block coordinate descent framework.Simulation results demonstrate the convergence of the proposed learning procedure,and indicate that the active RIS with learned deployment can effectively enhance the reflection and significantly improve the secrecy rate.展开更多
In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data privacy.In classical federated learning, the...In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data privacy.In classical federated learning, the communication channel between the devices poses a potential risk of compromising private information. To reduce the risk of adversary eavesdropping in the communication channel, we propose TRADE(transmit difference weight) concept. This concept replaces the decentralized federated learning algorithm's transmitted weight parameters with differential weight parameters, enhancing the privacy data against eavesdropping. Subsequently, by integrating the TRADE concept with the primal-dual stochastic gradient descent(SGD)algorithm, we propose a decentralized TRADE primal-dual SGD algorithm. We demonstrate that our proposed algorithm's convergence properties are the same as those of the primal-dual SGD algorithm while providing enhanced privacy protection. We validate the algorithm's performance on fault diagnosis task using the Case Western Reserve University dataset, and image classification tasks using the CIFAR-10 and CIFAR-100 datasets,revealing model accuracy comparable to centralized federated learning. Additionally, the experiments confirm the algorithm's privacy protection capability.展开更多
In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios,the limitations of existing research,including real-time calculation,accuracy efficiency trade-off,and the absence of t...In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios,the limitations of existing research,including real-time calculation,accuracy efficiency trade-off,and the absence of the three-dimensional attack area model,restrict their practical applications.To address these issues,an improved backtracking algorithm is proposed to improve calculation efficiency.A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm.Furthermore,the age-layered population structure genetic programming(ALPS-GP)algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area,considering real-time requirements.The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm.The study reveals a remarkable combination of high accuracy and efficient real-time computation,with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just 10^(-4)s,thus meeting the requirements of real-time combat scenarios.展开更多
In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically ...In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge(SoC)of energy storage units,this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units.First,a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process.Next,a control framework based on variable integral coefficients is proposed within the secondary frequency regulation model,along with an objective function that simultaneously considers both Automatic Generation Control(AGC)command tracking performance and SoC recovery requirements of energy storage units.Finally,a gradient descent optimization method is used to dynamically adjust the gain of the energy storage integral controller,allowingmultiple energy storage units to respond in real-time to AGC instructions and SoC variations.Simulation results confirmthe effectiveness of the proposedmethod.Compared to traditional strategies,the proposed approach takes into account the SoCdiscrepancies amongmultiple energy storage units and the duration of system net power imbalances.It successfully implements secondary frequency regulation while achieving dynamic power allocation among the units.展开更多
Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w...Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.展开更多
This thesis is intended to interpret Jack London's The Call of the Wild from the per-spective of four western classical myths:the loss of happiness,initiation,the descent into hell and resurrection.This article is...This thesis is intended to interpret Jack London's The Call of the Wild from the per-spective of four western classical myths:the loss of happiness,initiation,the descent into hell and resurrection.This article is aimed at providing fans of The Call of the Wild with a new perspective in exploring the novel.展开更多
基金the National Natural Science Foundation of China(Grant No.11471188)the STPF of Shandong Province(No.J17KA161).
文摘For the maximal space-like hypersurface defined on 2-dimensional space forms,based on the regularity and the strict convexity of the level sets,the steepest descents are well defined.In this paper,we come to estimate the curvature of its steepest descents by deriving a differential equality.
基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)and the Seoul Metropolitan Government(2025-RISE-01-027-04).
文摘Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces.The trapezoidal cavity form is compared with its thermal and flow performance,and it is revealed that trapezoidal fins tend to be more efficient,particularly when material optimization is critical.Motivated by the increasing need for sustainable energy management,this work analyses the thermal performance of inclined trapezoidal and rectangular porous fins utilising a unique hybrid nanofluid.The effectiveness of nanoparticles in a working fluid is primarily determined by their thermophysical properties;hence,optimising these properties can significantly improve overall performance.This study considers the dispersion of Graphene Oxide(GO)and Molybdenum Disulfide in the base fluid,engine oil.Temperature profiles are analysed by altering the radiative,porosity,wet porous,and angle of inclination parameters.Surface and contour plots are constructed by using the Lobatto IIIa Collocation Method with BVP5C solver in MATLAB and Gradient Descent Optimisation to predict the combined heat transfer rate.According to the study,fluid temperature consistently decreases when the angle of inclination,wet porous parameter,porosity parameter,and radiative parameter increase,suggesting significantly improved heat dissipation.The trapezoidal fin consistently exhibits a superior heat transfer mechanism than a rectangular fin.It is found that the trapezoidal fin transmits heat at a rate that is 0.05%higher than that of the rectangular fin.Validation of the present study is done through the comparison of previous studies.This research provides useful design insights for sophisticated engineering uses,including electrical cooling devices,heat exchangers,radiators,and solar heaters.
文摘The Hongyancun subway station in Chongqing,China,is 116 meters deep and the difference in air pressure often leaves users with clogged(堵塞的)ears when accessed via its elevator.When the air pressure outside the eardrum(耳膜)becomes different than the pressure inside,you experience ear barotrauma(气压伤).It occurs most often during steep ascents and descents and is usually associated with plane take⁃offs and landings,or driving up or down mountains.Most subway stations dont usually cause ear barotrauma,because they arent deep or steep enough for your ears to register a significant enough difference in air pressure.But taking the elevator to reach Chinas deepest subway station might actually clog up your ears.Thats because it is located 116 meters below the surface,which is the equivalent of about 40 floors underground.
基金Supported by the Science and Technology Project of Guangxi(Guike AD23023002)。
文摘In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.
基金co-supported by the National Natural Science Foundation of China(Nos.U23A20279,62271094)the National Key R&D Program of China(No.SQ2023YFB2500024)+2 种基金the Science Foundation for Youths of Natural Science Foundation of Sichuan Provincial,China(No.2022NSFSC0936)the China Postdoctoral Science Foundation(No.2022M720666)the Open Fund of Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,China(No.BDIC-2023-B-002).
文摘This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The base station(BS)provides short packet services to ground users using the non-orthogonal multiple access(NOMA)protocol through UAV-RIS,while preventing eavesdropper attacks.To optimize SEE,a joint optimization is performed concerning power allocation,UAV position,decoding order,and RIS phase shifts.An iterative algorithm based on block coordinate descent is proposed for mixed-integer non-convex SEE optimization problem.The original problem is decomposed into three sub-problems,solved alternately using successive convex approximation(SCA),quadratic transformation,penalty function,and semi-definite programming(SDP).Simulation results demonstrate the performance of the UAV-RIS-enhanced short-packet system under different parameters and verify the algorithm’s convergence.Compared to benchmark schemes such as orthogonal multiple access,long packet communication,and sum SEE,the proposed UAV-RIS-enhanced short-packet scheme achieves the higher minimum user’s SEE.
基金supported in part by the Department of OB/GYN research funds(University of Louisville,Louisville,KY,USA)Jilin Province Health Technology Capability Enhancement funds(No.2022JC055).
文摘Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structure that pulls the testis into the scrotum.However,the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated.To identify the androgen-regulated genes,we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout(Lhcgr KO)mice,an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency.We found that the expression of secreted protein acidic and rich in cysteine(SPARC)-related modular calcium binding 1(Smoc1)was the most severely suppressed at both the transcript and protein levels,while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice.The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist,flutamide.In addition,in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells.In the cultures of myogenic differentiation medium,both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7(Pax7)and myogenic factor 5(Myf5).After short-interfering RNA-mediated knocking down of Smoc1,the expression of Pax7 and Myf5 diminished,and testosterone alone did not recover,but additional SMOC1 did.These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
基金supported by the BK21 FOUR project(AI-driven Convergence Software Education Research Program)funded by the Ministry of Education,School of Computer Science and Engineering,Kyungpook National University,Republic of Korea(4120240214871)supported by the New Faculty Start Up Fund from LSU Health Sciences New Orleans,LA,USA.
文摘Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models.Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully.Most existing initialization methods mainly focus on gradient flow-related problems,such as gradient vanishing or exploding,or other existing approaches that require extra models that do not consider our setting,which is more practical.To address these problems,we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully connected layer in neural networks froma small set of training data with fewer classes.The approach relies on partitioning the output values from pre-trained models for a small set into two separate intervals determined by the targets.This process is framed as an optimization problem for each output neuron and class.The optimization selects the highest values as weights,considering their direction towards the respective classes.Furthermore,empirical 145 experiments involve a variety of neural networkmodels tested acrossmultiple benchmarks and domains,occasionally yielding accuracies comparable to those achieved with gradient descent methods by using only small subsets.
文摘Obstructed defecation syndrome(ODS)represents an important cause of constipation,primarily arising from dysfunctions within the pelvic floor.Characterized by an inability to complete defecation or effectively evacuate fecal material despite the urge to defecate,ODS results in a persistent sensation of incomplete evacuation and often requires excessive straining during defecation.Conventional clinical examinations fail to adequately assess the complex dynamic dysfunctions of the pelvic floor and anorectal region.Magnetic resonance defecography(MRD),a sophisticated form of dynamic pelvic floor imaging,provides a comprehensive,non-invasive means of visualizing and quantifying various anorectal and pelvic floor abnormalities.By allowing detailed assessment of structural and functional deficits during the defecation process,MRD plays a crucial role in the diagnostic workup of ODS,enabling colorectal surgeons to formulate more precise and individualized treatment strategies.This manuscript highlights the important anatomical and functional disorders of pelvic floor that are associated with ODS.
基金co-supported by the National Natural Science Foundation of China(No.62103014)。
文摘An optimal feedback guidance law with disturbance rejection objective is proposed for endoatmospheric powered descent.This guidance law with an affine form is derived by solving a novel problem called Endoatmospheric Powered Descent Guidance with Disturbance Rejection(Endo-PDG-DR).The key idea of formulating the Endo-PDG-DR problem is dividing disturbances into two parts,modeled and unmodeled disturbances:the modeled disturbance is proactively exploited by augmenting it as a new state of a dynamics model;the unmodeled disturbance is reactively attenuated in terms of its effect on the guidance performance by adjoining a parameterized time-varying quadratic performance index in the proposed optimal guidance problem.A Pseudospectral Differential Dynamic Programming(PDDP)method is developed to solve the Endo-PDG-DR problem,and correspondingly a robust neighboring optimal state feedback law is obtained,which has two synergistic functionalities.One is adaptive optimal steering to accommodate the modeled disturbance,and the other is disturbance attenuation to compensate for the state perturbation effect induced by the unmodeled disturbance.Using the derived feedback guidance law,a disturbance rejection level is quantified,and is correspondingly optimized by designing a quadratic weighting parameter tuning law.The numerical computations of interest are performed within a pseudospectral setting,ensuring polynomial analytical solution,high computational efficiency,and reliable convergence.
基金supported in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
基金supported in part by the National Natural Science Foundation of China(NSFC)(62473103,62203169,62473121)the Postdoctoral Science Foundation of Zhejiang Province(ZJ2023011).
文摘Adaptive graph neural networks(AGNNs)have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables.This article introduces a novel GNN framework,termed entropy-regularized ensemble adaptive graph(E^(2)AG),aimed at enhancing the predictive accuracy of AGNNs.Specifically,this work pioneers a novel AGNN learning approach based on mirror descent,which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs.Subsequently,motivated by multi-head self-attention mechanism,the training of ensembled AGNNs is rigorously examined within this framework,incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph.After that,the architecture and training algorithm of the model are then concisely summarized.Finally,to ascertain the efficacy of the proposed E^(2)AG model,extensive experiments are conducted on real-world industrial datasets.The evaluation focuses on prediction accuracy,model efficacy,and sensitivity analysis,demonstrating the superiority of E^(2)AG in industrial soft sensing applications.
基金co-supported by the National Natural Science Foundation of China(Nos.62301431 and U22B2013)the Guangdong Basic and Applied Basic Research Foundation,China(No.2024A1515030215)+3 种基金the Key Research and Development Program of Shaanxi Province,China(No.2023-GHZD-05)the Innovation Capability Support Program of Shaanxi Province,China(No.2021TD-08)the National Key Laboratory of Wireless Communications Foundation,China(No.IFN20230111)the Open Research Subject of State Key Laboratory of Intelligent Game,China(No.ZBKF-24-04).
文摘Reconfigurable Intelligent Surfaces(RISs)enable programmable wireless environments and thus have great potential for enhancing physical layer security.However,the security gain of conventional passive RISs is often limited by the“multiplicative fading”effect through reflection links,which becomes severe in the case of double reflections and significantly degrades the security performance.In this paper,we consider a wireless system that consists of a fixed passive RIS and an Unmanned Aerial Vehicle(UAV)-mounted active RIS,where the UAV-enabled aerial amplification and reflection are exploited to compensate for the multiplicative fading effect.We formulate the problem to maximize the secrecy rate by jointly considering the optimal deployment of the UAV-based active RIS and the reflection coefficients at both the passive and active RISs.To enable efficient algorithm design,we decompose the problem into two layers:the outer layer optimizes the UAV deployment through deep reinforcement learning,while the inner layer solves the beamforming and reflection design using a block coordinate descent framework.Simulation results demonstrate the convergence of the proposed learning procedure,and indicate that the active RIS with learned deployment can effectively enhance the reflection and significantly improve the secrecy rate.
基金supported by the National Key Research and Development Program of China(2022YFB3305904)the National Natural Science Foundation of China(62133003,61991403,61991400)+4 种基金the Open Project of State Key Laboratory of Synthetical Automation for Process Industries(SAPI-2024-KFKT-05,SAPI-2024-KFKT-08)China Academy of Engineering Institute of Land Cooperation Consulting Project(2023-DFZD-60-02,N2424004)the Fundamental Research Funds for the Central UniversitiesShanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Key Research and Development Program of Liaoning Province(2023JH26/10200011)
文摘In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data privacy.In classical federated learning, the communication channel between the devices poses a potential risk of compromising private information. To reduce the risk of adversary eavesdropping in the communication channel, we propose TRADE(transmit difference weight) concept. This concept replaces the decentralized federated learning algorithm's transmitted weight parameters with differential weight parameters, enhancing the privacy data against eavesdropping. Subsequently, by integrating the TRADE concept with the primal-dual stochastic gradient descent(SGD)algorithm, we propose a decentralized TRADE primal-dual SGD algorithm. We demonstrate that our proposed algorithm's convergence properties are the same as those of the primal-dual SGD algorithm while providing enhanced privacy protection. We validate the algorithm's performance on fault diagnosis task using the Case Western Reserve University dataset, and image classification tasks using the CIFAR-10 and CIFAR-100 datasets,revealing model accuracy comparable to centralized federated learning. Additionally, the experiments confirm the algorithm's privacy protection capability.
基金National Natural Science Foundation of China(62373187)Forward-looking Layout Special Projects(ILA220591A22)。
文摘In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios,the limitations of existing research,including real-time calculation,accuracy efficiency trade-off,and the absence of the three-dimensional attack area model,restrict their practical applications.To address these issues,an improved backtracking algorithm is proposed to improve calculation efficiency.A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm.Furthermore,the age-layered population structure genetic programming(ALPS-GP)algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area,considering real-time requirements.The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm.The study reveals a remarkable combination of high accuracy and efficient real-time computation,with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just 10^(-4)s,thus meeting the requirements of real-time combat scenarios.
文摘In high-renewable-energy power systems,the demand for fast-responding capabilities is growing.To address the limitations of conventional closed-loop frequency control,where the integral coefficient cannot dynamically adjust the frequency regulation command based on the state of charge(SoC)of energy storage units,this paper proposes a secondary frequency regulation control strategy based on variable integral coefficients for multiple energy storage units.First,a power-uniform controller is designed to ensure that thermal power units gradually take on more regulation power during the frequency regulation process.Next,a control framework based on variable integral coefficients is proposed within the secondary frequency regulation model,along with an objective function that simultaneously considers both Automatic Generation Control(AGC)command tracking performance and SoC recovery requirements of energy storage units.Finally,a gradient descent optimization method is used to dynamically adjust the gain of the energy storage integral controller,allowingmultiple energy storage units to respond in real-time to AGC instructions and SoC variations.Simulation results confirmthe effectiveness of the proposedmethod.Compared to traditional strategies,the proposed approach takes into account the SoCdiscrepancies amongmultiple energy storage units and the duration of system net power imbalances.It successfully implements secondary frequency regulation while achieving dynamic power allocation among the units.
基金supported By Guangdong Major Project of Basic and Applied Basic Research(2023B0303000009)Guangdong Basic and Applied Basic Research Foundation(2024A1515030153,2025A1515011587)+1 种基金Project of Department of Education of Guangdong Province(2023ZDZX4046)Shen-zhen Natural Science Fund(Stable Support Plan Program 20231122121608001),Ningbo Municipal Science and Technology Bureau(ZX2024000604).
文摘Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.
文摘This thesis is intended to interpret Jack London's The Call of the Wild from the per-spective of four western classical myths:the loss of happiness,initiation,the descent into hell and resurrection.This article is aimed at providing fans of The Call of the Wild with a new perspective in exploring the novel.
