Switching Delay Effects on Input-to-State Stability of Switched Systems

In this paper, input-to-state stability (ISS) is investigated for a class of nonlinear switched systems with time-varying switching delay, in which both ISS and non-ISS subsystems are considered simultaneously. By means of the Lyapunov function method, we show that ISS can be ensured for switched systems with time-varying switching delay if the activation time of ISS subsystems is sufficiently large and switching delays satisfy certain conditions. Moreover, inspired by (Zhang et al. 2020), a time-dependent multiple Lyapunov function is considered for linear switched systems with switching delay to obtain less conservative results, where the conservativeness can be reduced by explicitly providing the lower and upper bounds of switching intervals. Finally, simulations including an example of coordination of multiagent systems are offered to verify the effectiveness of the proposed results

Approximate Controllability for a Kind of Fractional Neutral Differential Equations with Damping

This paper gains several meaningful results on the mild solutions and approximate controllability for a kind of fractional neutral differential equations with damping (FNDED) and order belonging to 1,2 in Banach spaces. At first, a new expression for the mild solutions of FNDED via the (p, q)-regularized operator family and the technique of Laplace transform is acquired. Then, we consider the approximate controllability of FNDED by means of the approximate sequence method, and simultaneously, some applicable sufficient conditions are obtained

Effect of Combined Hydrophilic Activation on Interface Characteristics of Si/Si Wafer Direct Bonding

Wafer direct bonding is an attractive approach to manufacture future micro-electro-mechanical system (MEMS) and microelectronic and optoelectronic devices. In this paper, a combined hydrophilic activated Si/Si wafer direct bonding process based on wet chemical activation and O2 plasma activation is explored. Additionally, the effect on bonding interface characteristics is comprehensively investigated. The mechanism is proposed to better understand the nature of hydrophilic bonding. The water molecule management is controlled by O2 plasma activation process. According to the contact angle measurement and FTIR spectrum analysis, it can be concluded that water molecules play an important role in the type and density of chemical bonds at the bonding interface, which influence both bonding strength and voids’ characteristics. When annealed at 350 °C, a high bonding strength of more than 18.58 MPa is obtained by tensile pulling test. Cross sectional SEM and TEM images show a defect-free and tightly bonded interface with an amorphous SiOx layer of 3.58 nm. This amorphous SiOx layer will induce an additional energy state, resulting in a lager resistance. These results can facilitate a better understanding of low-temperature hydrophilicity wafer direct bonding and provide possible guidance for achieving good performance of homogenous and heterogenous wafer direct bonding

Machine learning-based prediction of survival prognosis in cervical cancer

Abstract Background Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. Results The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. Conclusion A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%)

Development of a molecular feature-based survival prediction model of ovarian cancer using the deep neural network

202304 bcwwVersion of RecordSelf-fundedEarly releaseCC, In Press, Corrected Proof, need replace Vo