Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations bet...
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Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switch bring great challenge for high-performance production scheduling ...
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Rapid stabilization of general stochastic quantum systems is investigated based on the rapid stability of stochastic differential *** introduce a Lyapunov-LaSalle-like theorem for a class of nonlinear stochastic syste...
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Rapid stabilization of general stochastic quantum systems is investigated based on the rapid stability of stochastic differential *** introduce a Lyapunov-LaSalle-like theorem for a class of nonlinear stochastic systems first,based on which a unified framework of rapidly stabilizing stochastic quantum systems is *** to the proposed unified framework,we design the switching state feedback controls to achieve the rapid stabilization of singlequbit systems,two-qubit systems,and N-qubit *** the unified framework,the state space is divided into two state subspaces,and the target state is located in one state subspace,while the other system equilibria are located in the other state *** the designed state feedback controls,the system state can only transit through the boundary between the two state subspaces no more than two times,and the target state is globally asymptotically stable in *** particular,the system state can converge exponentially in(all or part of)the state subspace where the target state is ***,the effectiveness and rapidity of the designed state feedback controls are shown in numerical simulations by stabilizing GHZ states for a three-qubit system.
The development of modern engineering components and equipment features large size,intricate shape and long service life,which places greater demands on valid methods for fatigue performance *** a smooth transformatio...
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The development of modern engineering components and equipment features large size,intricate shape and long service life,which places greater demands on valid methods for fatigue performance *** a smooth transformation between small-scale laboratory specimens’fatigue properties and full-scale engineering components’fatigue strength has been a long-term *** this work,two dominant factors impeding the smooth transformation—notch and size effect were experimentally studied,in which fatigue tests on Al 7075-T6511(a very high-strength aviation alloy)notched specimens of different scales were carried *** analyses identified the evidence of the size effect on notch fatigue damage ***,the Energy Field Intensity(EFI)initially developed for multiaxial notch fatigue analysis was improved by utilizing the volume ratio of the Effective Damage Zones(EDZs)for size effect *** particular,it was extended to a probabilistic model considering the inherent variability of the fatigue *** experimental data of Al 7075-T6511 notched specimens and the model-predicted results were compared,indicating the high potential of the proposed approach in fatigue evaluation under combined notch and size effects.
The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning clas...
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ISBN:
(数字)9798350369106
ISBN:
(纸本)9798350369113
The increasing prevalence of liver disease necessitates the development of accurate predictive models to aid in early diagnosis and treatment. This study investigates the effectiveness of several machine learning classifiers, Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), and Logistic Regression, in predicting liver disease outcomes based on clinical and biochemical features. Each model's performance was rigorously evaluated using multiple metrics, including F1-score, accuracy, root mean square error (RMSE), mean absolute error (MAE), precision, recall, and cross-validation ROC-AUC scores. The Random Forest Classifier emerged as the most effective model, achieving a perfect accuracy of 100% and an F1-score of 1.00, suggesting its robustness in handling complex, high-dimensional data. The Gradient Boosting Classifier also demonstrated commendable performance with an accuracy of 89% and an F1-score of 0.85, indicating its efficacy in capturing the underlying patterns of liver disease. Conversely, Logistic Regression and SVM exhibited lower performance metrics, pointing to potential limitations in their ability to model the intricacies of liver disease data effectively. The insights from this study not only enhance our understanding of liver disease prediction using machine learning but also emphasize the need for further research to validate these models in diverse clinical populations. This study contributes to the growing body of literature advocating for the integration of machine learning techniques in clinical decision-making processes, ultimately aiming to improve patient outcomes in liver disease management.
Bremsstrahlung—the spontaneous emission of broadband radiation from free electrons that are deflected by atomic nuclei—contributes to the majority of X-rays emitted from X-ray tubes and used in applications ranging ...
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Bremsstrahlung—the spontaneous emission of broadband radiation from free electrons that are deflected by atomic nuclei—contributes to the majority of X-rays emitted from X-ray tubes and used in applications ranging from medical imaging to semiconductor chip ***,we show that the bremsstrahlung intensity can be enhanced significantly—by more than three orders of magnitude—through shaping the electron wavefunction to periodically overlap with atoms in crystalline ***,we show how to shape the bremsstrahlung X-ray emission pattern into arbitrary angular emission profiles for purposes such as unidirectionality and ***,we find that these enhancements and shaped emission profiles cannot be attributed solely to the spatial overlap between the electron probability distribution and the atomic centers,as predicted by the paraxial and nonrecoil theory for free electron light *** work highlights an unprecedented regime of free electron light emission where electron waveshaping provides multi-dimensional control over practical radiation processes like *** results pave the way towards greater versatility in table-top X-ray sources and improved fundamental understanding of quantum electron-light interactions.
The behavior of multiple modes before and during minor disruption with the external resonant magnetic perturbations(RMPs)has been studied on a J-TEXT *** main component of RMPs is m/n=2/1,where m and n are the numbers...
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The behavior of multiple modes before and during minor disruption with the external resonant magnetic perturbations(RMPs)has been studied on a J-TEXT *** main component of RMPs is m/n=2/1,where m and n are the numbers of the poloidal and toroidal modes,*** the mode-locking caused by RMPs,it is found that before a minor or a major disruption(if there is no minor disruption),strong oscillations in both electron temperature and density occur if the edge safety factor q_(a)>*** analysis shows that the oscillations are caused by the m/n=3/1 *** addition,using the ECE,Mirnov coils and 2D electron cyclotron emission imaging diagnostic systems,it is found that a thermal collapse occurs on the inner side of the 2/1magnetic island during the minor disruption,and before the collapse,a 3/2 island increases,after the collapse,the 3/2 island may ***,the study also shows that these 3/1,2/1 and 3/2modes play roles in the thermal collapse of disruptions.
Backdoor attacks pose a significant threat during the model’s training phase. Attackers craft pre-defined triggers to break deep neural networks, ensuring the model accurately classifies clean samples during inferenc...
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In this article, a design approach to a radial-resonant wide beamwidth circular sector patch antenna is advanced. As properly evolved from a U-shaped d ipole, a prototype magnetic dipole can be fit in the radial direc...
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In this article, a design approach to a radial-resonant wide beamwidth circular sector patch antenna is advanced. As properly evolved from a U-shaped d ipole, a prototype magnetic dipole can be fit in the radial direction of a circular sector patch radiator, with its length set as the positive odd-integer multiples of onequarter wavelength. In this way, multiple TM0m（m = 1,2,...） modes resonant circular sector patch antenna with short-circuited circumference and widened E-plane beamwidth can be realized by proper excitation and perturbations. Prototype antennas are then designed and fabricated to validate the design approach. Experimental results reveal that the E-plane beamwidth of a dual-resonant antenna fabricated on air/Teflon substrate can be effectively broadened to 128°/120°, with an impedance bandwidth of 17.4%/7.1%, respectively. In both cases, the antenna heights are strictly limited to no more than 0.03-guided wavelength. It is evidently validated that the proposed approach can effectively enhance the operational bandwidth and beamwidth of a microstrip patch antenna while maintaining its inherent low profile merit.
作者：
Chen, KeHan, XiaosongLi, XiaoranLiang, YanchunXu, DongGuan, RenchuJilin University
Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University
Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology
Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri
Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States
Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning mode...
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