Time Dependence of the Graphene Surface Adhesion Force of the Sphere-Plane Contact at Different Relative Humidities.

Graphene has a promising application prospect in integrated circuits and microelectromechanical systems, and sphere-plane contacts are their common contact types. At present, it is difficult to explain the time dependence of the adhesion force of the sphere-plane contact by conventional theory. Therefore, a single rough peak of sphere-plane contact adhesion force model based on variable water contact angle theory and Bradley contact theory was established; the aim is to reveal the changing law of graphene adhesion force. Then, the time dependence of the graphene surface adhesion force at different humidity levels was investigated by using an atomic force microscopy spherical probe. Finally, a quantitative comparative analysis of the theory and experiment was performed. The results show that the theoretical adhesion force was in good agreement with the experimental measurement results. The time dependence of graphene surface adhesion was not obvious within a relative humidity of 45-55%. When the relative humidity was greater than 65%, the graphene surface adhesion first increased and then decreased with dwell time and finally tended to be stable. Because of the increase in relative humidity, the capillary condensation effect increases, and then the adhesion force increases with the development of the meniscus. When the water film was generated on the sample surface, the adhesion force decreased until the meniscus achieved equilibrium.

Influences of Relative Humidity and Dwell Time on Silica/Graphene Adhesion Force of a Cone-Plane Contact.

Graphene has exceptional electronic, mechanical, and thermal properties, and it is expected to have important applications in integrated circuits and other microelectronic fields. Its performances are greatly affected by surface adhesion force when it is used in a humid environment. In this paper, based on the law of variable water contact angle changing in the process of water vapor condensation, we established a cone-plane contact model, which is related to relative humidity and dwell time, to reveal the internal mechanism of the influence of relative humidity and dwell time on silica/graphene adhesion force. First, the silica/graphene adhesion force dependence of dwell time was measured by atomic force microscopy (AFM) at 45-85% RH. Then, the changing process of the meniscus between the AFM tip and the graphene surface was discussed, and the function of adhesion force with variables of dwell time and contact angle was established. Furthermore, the theoretical and experimental results were compared and analyzed. The results show that with the increase of relative humidity and dwell time, the capillary condensation increases, but the water contact angle of the cone material decreases. This causes the adhesion force to increase first and then decrease after it reaches a threshold value. Furthermore, the variable water contact angle of the graphene surface increases, but the adhesion force decreases gradually with the increase of surface water film. The theoretical results are in good agreement with the experimental results.

Dynamic Event-Triggered Predictive Control for Interval Type-2 Fuzzy Systems with Imperfect Premise Matching.

This paper investigates the dynamic event-triggered predictive control problem of interval type-2 (IT2) fuzzy systems with imperfect premise matching. First, an IT2 fuzzy systems model is proposed, including a dynamic event-triggered mechanism, which can save limited network resources by reducing the number of data packets transmitted, and a predictive controller, which can predict the state of the system between the two successful transmitted instants to deal with unreliable communication networks. Then, according to the Lyapunov stability theory and imperfect premise matching method, sufficient conditions for system stabilization and the controller gain are obtained. Finally, the validity of the proposed method is demonstrated by the numerical examples.

Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay.

This paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in which the attack occurs via both communication channels, from S-C and from C-A simultaneously, rather than considering only one, as in many papers; and the double attacks are described by high-level Markov processes rather than simple random variables. To further reduce network load, an advanced AETS with an adaptive threshold coefficient was first used in FNNs to deal with deception attacks. Moreover, given the engineering background, uncertain parameters and time-varying delay were also considered, and a feedback control scheme was adopted. Based on the above, a unique closed-loop synchronization error system was constructed. Sufficient conditions that guarantee the stability of the closed-loop system are ensured by the Lyapunov-Krasovskii functional method. Finally, a numerical example is presented to verify the effectiveness of the proposed method.

Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection.

Electroencephalogram (EEG) signals are an essential tool for the detection of epilepsy. Because of the complex time series and frequency features of EEG signals, traditional feature extraction methods have difficulty meeting the requirements of recognition performance. The tunable Q-factor wavelet transform (TQWT), which is a constant-Q transform that is easily invertible and modestly oversampled, has been successfully used for feature extraction of EEG signals. Because the constant-Q is set in advance and cannot be optimized, further applications of the TQWT are restricted. To solve this problem, the revised tunable Q-factor wavelet transform (RTQWT) is proposed in this paper. RTQWT is based on the weighted normalized entropy and overcomes the problems of a nontunable Q-factor and the lack of an optimized tunable criterion. In contrast to the continuous wavelet transform and the raw tunable Q-factor wavelet transform, the wavelet transform corresponding to the revised Q-factor, i.e., RTQWT, is sufficiently better adapted to the nonstationary nature of EEG signals. Therefore, the precise and specific characteristic subspaces obtained can improve the classification accuracy of EEG signals. The classification of the extracted features was performed using the decision tree, linear discriminant, naive Bayes, SVM and KNN classifiers. The performance of the new approach was tested by evaluating the accuracies of five time-frequency distributions: FT, EMD, DWT, CWT and TQWT. The experiments showed that the RTQWT proposed in this paper can be used to extract detailed features more effectively and improve the classification accuracy of EEG signals.

Engineering the Thermostability of Sucrose Synthase by Reshaping the Subunit Interaction Contributes to Efficient UDP-Glucose Production.

The restricted availability of UDP-glucose, an essential precursor that targets oligo/polysaccharide and glycoside synthesis, makes its practical application difficult. Sucrose synthase (Susy), which catalyzes one-step UDP-glucose synthesis, is a promising candidate. However, due to poor thermostability of Susy, mesophilic conditions are required for synthesis, which slow down the process, limit productivity, and prevent scaled and efficient UDP-glucose preparation. Here, we obtained an engineered thermostable Susy (mutant M4) from Nitrosospira multiformis through automated prediction and greedy accumulation of beneficial mutations. The mutant improved the T1/2 value at 55 °C by 27-fold, resulting in UDP-glucose synthesis at 37 g/L/h of space-time yield that met industrial biotransformation standards. Furthermore, global interaction between mutant M4 subunits was reconstructed by newly formed interfaces according to molecular dynamics simulations, with residue Trp162 playing an important role in strengthening the interface interaction. This work enabled effective, time-saving UDP-glucose production and paved the way for rational thermostability engineering of oligomeric enzymes.

Assessing the Impact of Continuous Vaccination and Voluntary Isolation on the Dynamics of COVID-19: A Mathematical Optimal Control of SEIR Epidemic Model.

In order to study the impact of continuous vaccination and voluntary isolation for the COVID-19, a susceptible-exposed-infected-recovered-quarantine-vaccines (SEIR-QV) model is proposed. A basic regeneration number R 0 is defined to determine the extinction or persistence of the disease. We numerically analyze the impact of key parameters based on actual parameters of COVID-19, such as the vaccination rate, population importation rate, and natural (or causal) mortality transmission rate on the dynamics of disease transmission. Then we obtain sensitivity indices of some parameters on R 0 by sensitivity analysis. Finally, the stability of the system and the effectiveness of the optimal control strategy are verified by numerical simulation.

Highly Enhancing Electrical, Thermal, and Mechanical Properties of Polypropylene/Graphite Intercalation Compound Composites by In Situ Expansion during Melt Mixing.

Polypropylene/graphite intercalation compound (PP/GIC) composites are prepared via melt mixing at three different temperatures (180, 200, and 220 °C). The dispersion of GICs in the composites is clearly improved due to the increased interlamellar spacing caused by in situ expansion of GICs at higher temperatures, which facilitates the intercalation of PP molecular chains into the GIC galleries. As a result, the PP/GIC composite with 10 wt% GICs prepared at 220 °C (PG220) presents a dielectric constant of about 1.3 × 108 at 103 Hz, which is about six orders higher than that of the composite prepared at 180 °C (PG180). Moreover, the thermal conductivity of the PG220 sample (0.63 Wm-1K-1) is 61.5% higher than that of the PG180 sample. The well-dispersed GICs accelerates the crystallization of PP by increasing the nucleation point and enhances the thermal stability of the composites. The PG220 sample shows a Young's modulus that is about 21.2% higher than that of the PG180 samples. The results provide an efficient approach for fabricating polymer/GIC composites without complex exfoliation and dispersion processes.

Theoretical and Experimental Study on Hot-Embossing of Glass-Microprism Array without Online Cooling Process.

Optical glass-microprism arrays are generally embossed at high temperatures, so an online cooling process is needed to remove thermal stress, but this make the cycle long and its equipment expensive. Therefore, the hot-embossing of a glass-microprism array at a low strain rate with reasonable embossing parameters was studied, aiming at reducing thermal stress and realizing its rapid microforming without online cooling process. First, the flow-field, strain-rate, and deformation behavior of glass microforming were simulated. Then, the low-cost microforming control device was designed, and the silicon carbide (SiC) die-core microgroove array was microground by the grinding-wheel microtip. Lastly, the effect of the process parameters on forming rate was studied. Results showed that the appropriate embossing parameters led to a low strain rate; then, the trapezoidal glass-microprism array could be formed without an online cooling process. The standard deviation of the theoretical and experimental forming rates was only 7%, and forming rate increased with increasing embossing temperature, embossing force, and holding duration, but cracks and adhesion occurred at a high embossing temperature and embossing force. The highest experimental forming rate reached 66.56% with embossing temperature of 630 °C, embossing force of 0.335 N, and holding duration of 12 min.

Fuzzy [Formula: see text] output-feedback control for the discrete-time system with channel fadings, sector nonlinearities, and randomly occurring interval delays and nonlinearities.

In this paper, the fuzzy [Formula: see text] output-feedback control problem is investigated for a class of discrete-time T-S fuzzy systems with channel fadings, sector nonlinearities, randomly occurring interval delays (ROIDs) and randomly occurring nonlinearities (RONs). A series of variables of the randomly occurring phenomena obeying the Bernoulli distribution is used to govern ROIDs and RONs. Meanwhile, the measurement outputs are subject to the sector nonlinearities (i.e. the sensor saturations) and we assume the system output is [Formula: see text], [Formula: see text]. The Lth-order Rice model is utilized to describe the phenomenon of channel fadings by setting different values of the channel coefficients. The aim of this work is to deal with the problem of designing a full-order dynamic fuzzy [Formula: see text] output-feedback controller such that the fuzzy closed-loop system is exponentially mean-square stable and the [Formula: see text] performance constraint is satisfied, by means of a combination of Lyapunov stability theory and stochastic analysis along with LMI methods. The proposed fuzzy controller parameters are derived by solving a convex optimization problem via the semidefinite programming technique. Finally, a numerical simulation is given to illustrate the feasibility and effectiveness of the proposed design technique.