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This paper addresses the berthing control problem for automatic ships by using a virtual guide system based on heuristic dynamic programming (HDP) method. Firstly, by introducing an automatic virtual guide system, the berthing control problem can be transformed into a tracking control problem, and then can be further transformed into an optimal regulation problem. Secondly, the HDP method is used to solve the optimal regulation control problem of the marine surface ship with unknown ship model. Then, it is proven that the tracking error, the adaptation laws and the control inputs are uniformly bounded on the basis of the Lyapunov theory. Finally, simulations are carried out on an automatic model ship and the HDP method is compared with the backstepping control to verify the effectiveness of the developed control scheme.

The Anthropocene proposal suggested that the Earth may have entered a new geological epoch, in which human activity and climate change are influencing the environment at global scale. Arrival of the Anthropocene is bringing an unprecedented challenge to the biodiversity that is essential to humans, and enhancing many benefits of nature to human being. However, biodiversity loss is aggravating in the rhythm of inevitable change in the Anthropocene, and the adaptation of biodiversity to the anthropogenic disturbance seems unable to keep pace with the human activity and climate change. Therefore, re-examination of the assumptions and practices upon the current conservation endeavor are needed. We suggested that biodiversity conservation should be paid more attention to the response from biodiversity to the human activity and climate change in the Anthropocene. Thus, the concept of biodiversity arks in the Anthropocene is proposed, that is, biodiversity arks in the Anthropocene are the areas where vulnerable biodiversity is sheltered to alleviate human activity and buffered from climate change under the anthropogenic disturbance. The concept should be implemented for biodiversity conservation to fill gaps between our knowledge and build on successful conservation and sustainability in the Anthropocene. It will be certainly important to conservation policy instruction and management under climate change, especially the implementation of climate buffering zones preserving biodiversity in the face of warming climate.

This paper investigates the problem of path following of underactuated marine surface vessels (MSVs) with uncertain nonlinear dynamics. First, the tracking target of the vessel is reduced from tracking the earth-fixed position to tracking the line-of-sight (LOS) angle by LOS method. Then, by employing the radial basis function neural network (RBFNN) to deal with the uncertain nonlinear dynamics, an adaptive NN fast power reaching law is developed for the path following problem based on the backstepping design methodology. Thereafter, the event-triggered technique is incorporated into the control design to synthesize an adaptive NN event-triggered controller with the fast power reaching convergence rate. By combining with the presented event-triggered mechanism, the controller is only updated when the triggering condition is satisfied. Therefore, both the update frequency of the controller and actuator loss are greatly reduced comparing with the traditional time-triggered controller. Theoretical analysis via Lyapunov method indicates that the tracking error can converge to zero within a finite time, meanwhile it also shows that Zeno behavior can be avoided. Simulation results with comparations illustrate the validity and superiority of the proposed controller.

This paper is concerned with secure synchronization of multi-agent systems under deception attacks in the impulsive control framework. False data is injected into sensor-to-controller channels, which causes the transmittal signals being changed. Some stochastic variables obeying the Bernoulli distribution associated with communication channels between neighbouring agents are proposed to describe the case whether channels are suffered from the attack. A distributed impulsive controller is proposed and bounded synchronization, caused by false data injection is studied. Several mean-square bounded synchronization conditions are derived and the error bound is also given. Finally, two examples are provided to verify the theoretical results.

Fully epitaxial FeCo/MgO/Fe magnetic tunnel junctions on silicon substrates were fabricated using in situ shadow-masks in an electron-beam deposition system. An inverse tunneling magnetoresistance (TMR) of −39% was observed at 77 K after annealing, which was not obtained in MTJs grown in better vacuum with the same device structure. This inverse TMR is attributed to the oxidation of the FeCo/MgO interface, which provides a negative spin polarization. Our work highlights the importance of interfacial properties on tunneling magnetoresistance and points to a simple processing route to achieve inverse TMR by carefully controlling the oxidation condition of the bottom layer.

Electrochemical impedance spectroscopy (EIS) is an effective method for studying electrochemical systems. The interpretation of EIS is the biggest challenge in this technology, which requires reasonable modeling. To overcome the subjectivity of human analysis, this work uses machine learning to carry out EIS model recognition. Raw EIS data and their equivalent circuit models are collected from the literature, and the support vector machine (SVM) is used to analyze these data. Comparing with other machine learning algorithms, SVM achieves the best comprehensive performance in this database. As a result, the optimized SVM model can efficiently figure out the most suitable equivalent circuit model of the given EIS spectrum. This study demonstrates the great potential of machine learning in electrochemical researches.

To document arid Northern China as a diversification center for desert plants, and to better understand the mechanisms of desert taxa diversification, we used five cpDNA spacers (trnL-trnF, rps16, psbA-trnH, psbK-psbI and trnS-trnG) to investigate Lagochilus ilicifolius in all distribution areas, including Northern China, Mongolia and Russia. Phylogenetic analyses showed that L. ilicifolius comprises two distinctive lineages, one distributed in China, and another in Mongolia–Russia. Our data confirmed that arid Northern China, is a distinctive area with many endemic genera. Biogeographic inferences, based on a Bayesian uncorrelated lognormal model together with molecular dating, suggested that the main diversification within the species occurred in the Pleistocene (ca. 1.38–0.3 Ma), resulting from the transition of the climate of Eurasia to a dry-cold pattern as well as the desertification caused the vicariance of desert plants.

MnO2-doped barium strontium titanate–MgO (BST/MgO) composites were sintered by the solid-state reaction method. The effect of MnO2-doping on the microstructure and microwave dielectric properties of the BST/MgO composites was investigated. The MgO grains were found to be surrounded by Mn-rich oxides, which hindered the incorporation of Mg2+ into the lattice of BST. As a result, enhanced comprehensive microwave dielectric performances, such as the reduction of dielectric loss and leakage current about 0.78 nA/cm2, and an increase of electric field tunability of dielectric constant about 13.8% at 5 kV/mm were obtained in MnO2-doped BST/MgO ceramics.

An optical temperature sensor based on the upconversion luminescence of Tm3+ has been developed. Under a 980 nm diode laser excitation, the fluorescence intensity ratio (FIR) between 700 (Tm3+:3F2,3 → 3H6) and 800 nm (Tm3+:3H4 → 3H6) upconversion emissions from Tm3+/Yb3+ codoped oxyfluoride glass ceramic was studied as a function of temperature in the range of 293–703 K. The 3F2,3 and 3H4 states of Tm3+ are verified to be thermally coupled levels. By using FIR technique, the sensitivity for detecting temperature variations achieved here is better than previous reported rare earth ions fluorescence based temperature sensors. With the advantages of intense upconversion luminescence and absolutely separated 700 and 800 nm emission bands, the Tm3+/Yb3+ codoped oxyfluoride glass ceramic is a very promising candidate for accurate optical temperature sensors with much higher sensitivity and resolution.

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined in ATLAS by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empirically measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to select library calls and choose data layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.