Quantum entanglement in plasmonic waveguides with near-zero mode indices

We investigate the quantum entanglement between two quantum dots in a plasmonic waveguide with near-zero mode index, considering the dependence of concurrence on interdot distance, quantum dot-waveguide frequency detuning and coupling strength ratio. High concurrence is achieved for a wide range of interdot distance due to the near-zero mode index, which largely relaxes the strict requirement of interdot distance in conventional dielectric waveguides or metal nanowires. The proposed quantum dot-waveguide system with near-zero phase variation along the waveguide near the mode cutoff frequency shows very promising potential in quantum optics and quantum information processing

A Glance at the Recent Additive Manufacturing Research and Development in China

This paper reviews some of the recent additive manufacturing research and development works in China. A considerable amount of AM research activities in China focuses on directed energy deposition processes, powder bed fusion processes and stereolithography, with much of the effect dedicated to system and application development. Although many of the recent results are not readily available from the literatures published in China, from the available information the areas of focus for research and development could be clearly seen. Despite some speculations, the AM research in China is vibrate and aggressive, with some areas at least several years ahead of the other countries.Mechanical Engineerin

Nuclear spin relaxation in cold atom-molecule collisions

We explore the quantum dynamics of nuclear spin relaxation in cold collisions of $^1\Sigma^+$ molecules with structureless atoms in an external magnetic field. To this end, we develop a rigorous coupled-channel methodology, which accounts for rotational and nuclear spin degrees of freedom of $^1\Sigma^+$ molecules, their interaction with an external magnetic field, as well as for anisotropic atom-molecule interactions. We apply the methodology to study collisional relaxation of the nuclear spin sublevels of $^{13}$CO molecules immersed in a cold buffer gas of $^4$He atoms. We find that nuclear spin relaxation in the ground rotational manifold of CO occurs extremely slowly due to the absence of direct couplings between the nuclear spin sublevels. The rates of collisional transitions between the $N=1$ nuclear spin states of CO are generally much higher due to the direct nuclear spin-rotation coupling between the states. These transitions obey selection rules, which depend on the values of space-fixed projections of rotational and nuclear spin angular momenta for the initial and final molecular states. For some initial states, we also observe a strong magnetic field dependence, which can be understood using the first Born approximation. We use our calculated nuclear spin relaxation rates to investigate the thermalization of a single nuclear spin state of CO$(N=0)$ immersed in a cold buffer gas of He. The calculated nuclear spin relaxation times ($T_1\simeq 0.5$ s at $T=1$ K) display a steep temperature dependence decreasing rapidly at elevated temperatures due to the increased population of rotationally excited states, which undergo nuclear spin relaxation at a much faster rate. Thus, long relaxation times of $N=0$ nuclear spin states in cold collisions with buffer gas atoms can only be maintained at sufficiently low temperatures ($kT\ll 2B_e$), where $B_e$ is the rotational constant.Comment: 41 pages, 12 figure

Influence of Fly Ash on Surface Chloride Concentration Under Shallow Immersion Condition

In this paper, the influence of fly ash content on concrete surface chloride concentration was investigated through periodical tests of surface chloride concentration of concrete by immersing three kinds of concrete specimens in 5.0 wt.% sodium chloride solution. One kind of specimen is common concrete without fly ash, whereas the other two kinds of specimens are mixed with fly ash. The results show that the surface chloride ion concentration ranges from 0.295 to 0.777 wt.% for the immersed concrete samples in this study; in the initial stage of immersion, the concrete surface chloride ion concentration is affected by added fly ash, and after 30 days of immersion, the surface chloride concentration of concrete with fly ash is 1.3 times that of concrete without fly ash at the same water–binder ratio; for the concretes without fly ash, the surface chloride concentration is linear with immersion time, whereas for the concrete with fly ash, the surface chloride concentration is nearly linear with square root of immersion time; for the concrete with fly ash, fly ash contents have little impact on the surface chloride concentration, and the surface chloride content of concrete without fly ash is gradually close to that of concrete with 15 and 30 wt.% of fly ash

A cooperative domain model for multiple phase transitions and complex conformational relaxations in polymers with shape memory effect

Shape memory polymers (SMPs) are thermo-rheologically complex materials showing significant temperature and time dependences. Their segments often undergo cooperative phase transitions and conformational relaxations simultaneously along with shape memory effect (SME). In this study, a cooperative domain model is proposed to describe the composition dependence, multiple phase transitions and conformational relaxations of SMPs within their glass transition zones. Variations in local-area compositions and cooperative domains of the amorphous SMPs cause significant differences in their segmental relaxation. At a fixed domain size, both intermolecular activation energy and relaxation time significantly influence the SME and thermomechanical properties of the SMPs. Finally, the model is successfully applied to predict the shape memory behavior of SMPs with one stage SME and triple-SME, and the theoretical results have been validated by the experimental ones. This model could be a powerful tool to understand the working mechanisms and provide a theoretical guidance for the designs of multi-SME in SMPs

Analysis and Design of the Reconfiguration Motion Qualities of a Deformable Robot Based on a Metamorphic Mechanism

Traditional wheel-legged ground mobile robots can only partially deform during wheel-leg switching, resulting in failure to achieve better environmental adaptability. Metamorphic mechanisms can be introduced into car structure designs. A new type of wheel-legged ground mobile robot, namely a deformable robot, is proposed in this study. Compared with traditional wheel-legged ground mobile robots, the deformable robot is capable of global reconfiguration, that is, when transitioning between the wheeled type (vehicle state) and the legged type (humanoid state), the shape, structure, degrees of freedom, and position of the centre of mass will change significantly. First, based on the characteristics of the wheel-legged compound motion, a structural model of the deformable robot was proposed and designed, and its reconfiguration motion was planned. Then, a kinematic model of the coupled reconfiguration process of the deformable robot was established. A horizontal lifting model was created to keep the front body level when lifting. The motion law of each active joint angle over time was designed based on the requirements of the reconfiguration motion smoothness. The criterion of reconfiguration stability was established and measures to improve it were proposed. Finally, based on the simulation verification of the smoothness, horizontality, and stability of the coupled reconfiguration of the system, a prototype of the deformable robot was developed, and a coupled reconfiguration experiment was conducted on an actual road surface. The experiment results show that the reconfiguration motion of the deformable robot between the vehicle state and the humanoid state had good motion qualities

gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation

Binary semantic segmentation in computer vision is a fundamental problem. As a model-based segmentation method, the graph-cut approach was one of the most successful binary segmentation methods thanks to its global optimality guarantee of the solutions and its practical polynomial-time complexity. Recently, many deep learning (DL) based methods have been developed for this task and yielded remarkable performance, resulting in a paradigm shift in this field. To combine the strengths of both approaches, we propose in this study to integrate the graph-cut approach into a deep learning network for end-to-end learning. Unfortunately, backward propagation through the graph-cut module in the DL network is challenging due to the combinatorial nature of the graph-cut algorithm. To tackle this challenge, we propose a novel residual graph-cut loss and a quasi-residual connection, enabling the backward propagation of the gradients of the residual graph-cut loss for effective feature learning guided by the graph-cut segmentation model. In the inference phase, globally optimal segmentation is achieved with respect to the graph-cut energy defined on the optimized image features learned from DL networks. Experiments on the public AZH chronic wound data set and the pancreas cancer data set from the medical segmentation decathlon (MSD) demonstrated promising segmentation accuracy, and improved robustness against adversarial attacks.Comment: 12 page