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Numerical procedure plays a key role in tackling the solutions of nonlinear dynamical systems. With the advent of the age of big data and high-power computing, developing efficient and fast numerical algorithms is an urgent task. This paper extends the Lie derivative discretization algorithm to the nonautonomous nonlinear systems and investigates the numerical solutions of the systems. The periodic solutions of three different classical nonlinear systems are calculated, and the results are compared to those values calculated from the Runge–Kutta fourth-order algorithm, which demonstrated that the Lie derivative algorithm has the advantages of large time step and short computation time.

This paper designed a novel three-output reflective packaged grating. The optimized parameters such as the period and depth of the high-efficiency three-output grating with an incident wavelength of 1550 nm can be calculated by rigorous coupled-wave analysis (RCWA). According to the optimized result, the grating can diffract the incident light energy into three orders with an efficiency of nearly 33% under the premise of second Bragg angle incidence and the given duty ratio of 0.5. The diffraction efficiency of the packaged grating is improved compared to the surface-relief three-output grating under second Bragg angle incidence, especially for TE-polarized light.

This paper presents a current mode step-up/step-down DC–DC converter with high efficiency, small output voltage ripple, and fast transient response. The control scheme adaptively configures the converter into the proper operation mode. The efficiency is improved by reducing the switching loss, wherein the converter operates like a buck or boost converter, and conduction loss, wherein the average inductor current is reduced in transition modes. The output voltage ripple is significantly reduced by incorporating two constant time transition modes. A fast line transient response is achieved with small overshoot and undershoot voltage. An adaptive substrate selector (ASS) is introduced to dynamically switch the substrate of PMOS power transistors to the highest on-chip voltage. A lossless self-biased current sensor with high-speed and high-accuracy is also achieved. The proposed converter was designed with a standard 0.5 μm CMOS process, and can regulate an output voltage within the input voltage ranged from 2.5 V to 5.5 V. The maximum load current is 600 mA, and the maximum efficiency is 94%. The output voltage ripple is less than 15 mV in all operation modes.

The paper proposed a high efficiency boost converter with constant voltage (CV) and constant current (CC) modes. The selection of CV or CC working mode is based on the requirement, and the transient time from CV to CC mode is 230$\mu$s. The boost converter is particularly for the use of Li-ion battery portable devices. High efficiency is obtained by sleep/burst mode under light load and pulse width modulation (PWM) mode under heavy load. The quiescent current of the whole chip can be down to 6$\mu$A when the converter enters the standby mode. The converter has been made of 0.35$\mu$m complementary metal-oxide semiconductor (CMOS) process. Experimental results show that the peak efficiency is 98.2% at a 1.5A output current and a 4.2V input voltage.

In this paper, an innovative approach to achieve broadband linear power amplifier (PA) with a continuous broadband is presented. The proposed method mainly depends on the theory of control of intermodulation products and harmonics. This method results in a continuous sweet spots over a wide bandwidth. Based on the continuous sweet spots, a suboptimal solution is derived for improving the efficiency and linearity of broadband PAs. To verify the effectiveness of this method, one broadband PA operating over 4.5–5.5GHz is developed and measured, it indicates that the peak output power of the PA is 35–57dBm for a small signal input with a gain of 12dB, and the peak drain efficiency (DE) of the PA is larger than 57% over the whole working band. However, when the PA is stimulated by a 5MHz two-tone signal input, we observe that the DE keeps above 38% under the condition of the measured third-order intermodulation distortion (IMD3) is not larger than $-30$dBc.

In order to achieve high efficiency in a wider load range, a PWM/PFM/Power-Saved mode dc–dc buck converter is proposed. The mode switching technique is based on an accurate power loss model consisting of critical components/parameters: The size of the power transistor and the ON/OFF status of power-hungry subcircuits. The circuit of mode switching technique is achieved by the reference, pulse skipped modulation (PSM) comparator and feedback resistors, and its own consumption of quiescent current is extremely low. At ultra-light load, by detecting the ZCD duration and the ripple of the output, the system can switch to the Power-Saved-Mode. The proposed buck converter was implemented by using a 0.18 $\mu$m CMOS process. It achieves a peak efficiency of 97% and $>$90% efficiency from 100 $\mu$A to 600 mA. A 360 nA quiescent current is achieved. With a low output ripple of less than 60 mV, the input voltage range and regulated output voltage are 3.6–5 V and 1.2–3.3 V, respectively.

In order to improve the efficiency in computing the global properties of nonlinear systems, some effective methods have been proposed for the global analysis of these systems. However, there are only few investigations focusing on the numerical algorithms of the system response trajectories. This paper presents a higher-efficiency geometric numerical integration method with simple cell mapping for solving the basins of attraction of nonlinear systems. This numerical algorithm is based on the rule of Lie derivative, using it to calculate the trajectories of the nonlinear systems. Then, the global structure is studied by the numerical algorithm associated with a simple cell mapping method. Compared with the traditional Runge–Kutta methods of orders 4 and 5, it is demonstrated that the proposed Lie derivative iterative algorithm has significant advantages in efficiency.

Patterning materials on hydrophobic polymer substrates have extensive applications in fabricating flexible devices at low cost, while the low transfer efficiency encumbers its advance. This paper provides a facile route to transfer materials onto hydrophobic polymer substrates with high efficiency by operating in organic solvent atmosphere. Under the assistance of condensed organic solvent layer on the substrate, bovine serum albumin (BSA) are desirably transferred onto untreated hydrophobic polymer substrates, such as poly(dimethylsiloxane) (PDMS), polystyrene (PS), and poly(ethylene terephthalate) (PET) as a proof-of-concept experiment. Moreover, the combination of this method with stepwise contraction and adsorption nanolithography (SCAN) was also demonstrated as an alternative way to further miniaturize patterns prepared by other methods.

A three-port surface-relief grating with a connecting layer under the second Bragg condition is put forward and designed for the free space application in this paper. Such grating can function as a beam splitter, which can split the polarized light into the $-$2nd order and the $-$1st order and the 0th order based on the optimum grating profile parameters. By using rigorous coupled-wave analysis, a highly-efficient polarization-dependent connecting-layer-based grating can be obtained with the optimum different depths and thicknesses with the grating duty cycle of 0.65 and grating period of 1150nm. On the basis of the designed grating profile parameters, a modal method can explain the propagating process. Compared with the conventional surface-relief fused-silica grating, the diffraction efficiencies for TE polarization in three orders are improved. Therefore, the novel conception of the grating under the second Bragg condition is significant for further applications such as interferometer with improved efficiency for TE polarization.

Deep Learning in Traffic Sign Recognition (TSR) System is rapidly developing. Still, not much work has been reported in the literature on architectures that can work efficiently on multiple (more than two) databases across different countries. Each of these databases incorporates images of varied resolutions. Here, a Deep Neural Network called Multiple Database Efficient Network (MDEffNet) has been proposed for TSR. Batch normalization layers have been introduced to reduce the internal co-variant shift, and further, data augmentation has been carried out to achieve high efficiency with reasonable training time. Results at each step of the proposed architecture have been provided. The proposed model is compared with some state-of-the-art methods based on single or two databases, and solo works done in multiple (three) databases originating from different countries. The comparisons prove the high effectiveness of the proposed model. The achieved efficiency is much higher than all of them, including the average human ability. Deep learning techniques often suffer from increased memory and computational requirements. MDEffNet has only 1.36 million parameters ($\sim$ 26 times lower than the state-of-art methods) with 130 s per epoch, which is relatively low. Hence, it is also suitable for real-time embedded TSR systems.

Random numbers (RNs) have applications in many areas: lottery games, gambling, computer simulation, and, most importantly, cryptography [N. Gisin et al., Rev. Mod. Phys.74 (2002) 145]. In cryptography theory, the theoretical security of the system calls for high quality RNs. Therefore, developing methods for producing unpredictable RNs with adequate speed is an attractive topic. Early on, despite the lack of theoretical support, pseudo RNs generated by algorithmic methods performed well and satisfied reasonable statistical requirements. However, as implemented, those pseudorandom sequences were completely determined by mathematical formulas and initial seeds, which cannot introduce extra entropy or information. In these cases, “random” bits are generated that are not at all random. Physical random number generators (RNGs), which, in contrast to algorithmic methods, are based on unpredictable physical random phenomena, have attracted considerable research interest. However, the way that we extract random bits from those physical entropy sources has a large influence on the efficiency and performance of the system. In this manuscript, we will review and discuss several randomness extraction schemes that are based on radiation or photon arrival times. We analyze the robustness, post-processing requirements and, in particular, the extraction efficiency of those methods to aid in the construction of efficient, compact and robust physical RNG systems.

This paper aims at providing new modifications of Steffensen’s scheme for solving nonlinear equations. The new general approach is discussed in detail theoretically. It is observed that a super fast R-order along with an interesting computational efficiency index will be achieved. Dynamical behaviors of the schemes are given to show an enlarged radii of convergence. Some experiments are also brought forward to support the given theory.

Pd and carbon nanotube (CNT) composites were prepared by well-dispersed deposition of Pd nanoparticles on commercial CNT, and applied to the selective hydrogenation of benzoic acid (BA) to synthesize cyclohexane carboxylic acid (CCA). The catalysts and the hydrogenation products were analyzed by XRD, TEM, TG, FTIR, UV-Vis absorption, GC and GC-MS, respectively. Hydrogenation process was also optimized through varying the reaction parameters. The results demonstrate that Pd/CNT catalysts possess the highest hydrogenation efficiency, give the full conversion of BA and 100.0% selectivity towards CCA at the optimal hydrogenation conditions, by comparing with some commercial hydrogenation catalysts and Pd/C catalysts with commercial carbonaceous supports. The excellent hydrogenation performance of Pd/CNT is attributed to the stable crystalline CNT support and the high dispersion of Pd nanoparticles. In addition, the protic solvent is also beneficial to lower the activation energy barrier of BA hydrogenation, and further to improve the hydrogenation rate. This work implies that CNT can be potentially chosen as an effective carbonaceous support to prepare Pd/C catalyst with an outstanding performance of BA selective hydrogenation.

Exploiting advanced regenerable adsorbents for efficient uranium adsorption from seawater is a promising route to address uranium resources shortages. Herein, inspired by the mechanism of plant homeostasis, natural polyphenols are combined with cotton matrix to construct a polyphenol-functionalized cotton (Cot-TA-APTES) for efficient uranium extraction. Compared with pristine cotton, the polyphenol-functionalized cotton displays a rapid adsorption kinetic, and the maximum extraction efficiency for U(VI) is up 96.07% at pH 5.0 and 30${}^{\circ }$C. Additionally, Cot-TA-APTES shows a higher adsorption selection ability for U(VI) than other interfering ions. Based on FT-IR and XPS, the excellent extraction efficiency and favorable selectivity of Cot-TA-APTES for U(VI) are due to the combination of U(VI) and functional coating groups. This natural polyphenol/cotton-based absolutely bio-derived adsorbent provides a novel and potentially applicable strategy for constructing an adsorbent for nuclear environmental remediation and uranium extraction from seawater with cost-effective and biosafety.