Experimental Study on Performance of Two-phase Ejector Refrigeration Cycle System with Two-throat Nozzle

The two-phase ejector refrigeration cycle (TPERC) system with a two-throat nozzle ejector was investigated experimentally, and the entrainment ratio of the ejector and the COP of the system were compared with those of the ejector with Laval nozzle and the TPERC system respectively. The experimental results indicate that the entrainment ratios of the two-throat nozzle ejectors with different geometric size are greater than those of the Laval nozzle ejectors under the working condition of the evaporating/condensing temperatures 1?/45?, the maximum increment of the entrainment ratio is about 18%, and the COP of the TPERC system with two-throat nozzle ejector is greater than that of the TPERC system with Laval nozzle ejector, the maximum increment of the COP is about 12%. Under the condition of the fixed evaporating temperature 1?, the entrainment ratios of both the two-throat nozzle ejector and the Laval nozzle ejector achieve the maximum values as the condensing temperature is about 45?. Under the condition of the fixed condensing temperature 50?, the entrainment ratios of the two types of ejectors achieve the maximum values as the evaporating temperature is about 3?

Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport

Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples. A recent solution to this problem is calibrating the distribution of these few sample classes by transferring statistics from the base classes with sufficient examples, where how to decide the transfer weights from base classes to novel classes is the key. However, principled approaches for learning the transfer weights have not been carefully studied. To this end, we propose a novel distribution calibration method by learning the adaptive weight matrix between novel samples and base classes, which is built upon a hierarchical Optimal Transport (H-OT) framework. By minimizing the high-level OT distance between novel samples and base classes, we can view the learned transport plan as the adaptive weight information for transferring the statistics of base classes. The learning of the cost function between a base class and novel class in the high-level OT leads to the introduction of the low-level OT, which considers the weights of all the data samples in the base class. Experimental results on standard benchmarks demonstrate that our proposed plug-and-play model outperforms competing approaches and owns desired cross-domain generalization ability, indicating the effectiveness of the learned adaptive weights

Photoinduced Temperature Gradients in Sub-wavelength Plasmonic Structures: The Thermoplasmonics of Nanocones

Plasmonic structures are renowned for their capability to efficiently convert light into heat at the nanoscale. However, despite the possibility to generate deep sub-wavelength electromagnetic hot spots, the formation of extremely localized thermal hot spots is an open challenge of research, simply because of the diffusive spread of heat along the whole metallic nanostructure. Here we tackle this challenge by exploiting single gold nanocones. We theoretically show how these structures can indeed realize extremely high temperature gradients within the metal, leading to deep sub-wavelength thermal hot spots, owing to their capability of concentrating light at the apex under resonant conditions even under continuous wave illumination. A three-dimensional Finite Element Method model is employed to study the electromagnetic field in the structure and subsequent thermoplasmonic behaviour, in terms of the three-dimensional temperature distribution. We show how the latter is affected by nanocone size, shape, and composition of the surrounding environment. Finally, we anticipate the use of photoinduced temperature gradients in nanocones for applications in optofluidics and thermoelectrics or for thermally induced nanofabrication

Bloch Surface Waves in Open Fabry–Perot Microcavities

Thanks to the increasing availability of technologies for thin film deposition, all-dielectric structures are becoming more and more attractive for integrated photonics. As light–matter interactions are involved, Bloch Surface Waves (BSWs) may represent a viable alternative to plasmonic platforms, allowing easy wavelength and polarization manipulation and reduced absorption losses. However, plasmon-based devices operating at an optical and near-infrared frequency have been demonstrated to reach extraordinary field confinement capabilities, with localized mode volumes of down to a few nanometers. Although such levels of energy localization are substantially unattainable with dielectrics, it is possible to operate subwavelength field confinement by employing high-refractive index materials with proper patterning such as, e.g., photonic crystals and metasurfaces. Here, we propose a computational study on the transverse localization of BSWs by means of quasi-flat Fabry–Perot microcavities, which have the advantage of being fully exposed toward the outer environment. These structures are constituted by defected periodic corrugations of a dielectric multilayer top surface. The dispersion and spatial distribution of BSWs’ cavity mode are presented. In addition, the hybridization of BSWs with an A exciton in a 2D flake of tungsten disulfide (WS2) is also addressed. We show evidence of strong coupling involving not only propagating BSWs but also localized BSWs, namely, band-edge and cavity modes

A computationally-efficient sandbox algorithm for multifractal analysis of large-scale complex networks with tens of millions of nodes

Multifractal analysis (MFA) is a useful tool to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. One of the widely used methods for fractal analysis is box-covering. It is known to be NP-hard. More severely, in comparison with fractal analysis algorithms, MFA algorithms have much higher computational complexity. Among various MFA algorithms for complex networks, the sandbox MFA algorithm behaves with the best computational efficiency. However, the existing sandbox algorithm is still computationally expensive. It becomes challenging to implement the MFA for large-scale networks with tens of millions of nodes. It is also not clear whether or not MFA results can be improved by a largely increased size of a theoretical network. To tackle these challenges, a computationally-efficient sandbox algorithm (CESA) is presented in this paper for MFA of large-scale networks. Our CESA employs the breadth-first search (BFS) technique to directly search the neighbor nodes of each layer of center nodes, and then to retrieve the required information. Our CESA's input is a sparse data structure derived from the compressed sparse row (CSR) format designed for compressed storage of the adjacency matrix of large-scale network. A theoretical analysis reveals that the CESA reduces the time complexity of the existing sandbox algorithm from cubic to quadratic, and also improves the space complexity from quadratic to linear. MFA experiments are performed for typical complex networks to verify our CESA. Finally, our CESA is applied to a few typical real-world networks of large scale.Comment: 19 pages, 9 figure

Droplets microfluidics platform—A tool for single cell research

Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level