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Circularly polarized luminescent chiral organogels based on fluorescence resonance energy transfer are fabricated by supramolecular co-assembly in an achiral polymer system, in which a wavelength shift and amplified glum are achieved.

Today, app stores offer ranking lists to help users to find quality apps that meet their needs. In order to prevent people from spreading fake reviews which can be used to defame certain apps or manipulate the ranking lists of the app store, we propose a method based on Positive and Unlabeled (PU) learning and behavior density to detect fake reviews. To identify the trusted negative samples, the classifier is trained by the Biased-SVM algorithm. Then, the preliminary screening results of the classifier are combined with user behavior density to identify fake reviews. The traditional fully supervised detection method relies on manually labeled data, the quality of which directly affects the trained classifier. Our proposed method can overcome such a deficiency, and achieve effective learning when there are only a small number of positive samples and a large number of unlabeled samples. Through experiments and case analysis, we demonstrate that our method has high detection accuracy.

Liquid–solid fluidized beds have been applied in the field of bioreactors for artificial liver systems. This paper uses a coupling method of computational fluid dynamics and discrete element method to numerically study the fluidization dynamics of an impinging-jet-driven bioreactor. A long tube is placed at the centerline of a cylindrical container in the bioreactor. The liquid is discharged toward the bottom of the container to form an impinging jet to drive the fluidization process of the microcapsules. The bioreactor's bed expansion height, porosity distribution, and interaction between the liquid and microcapsules are analyzed at different microcapsule densities, sizes, and flow rates. It is found that the bed expansion is proportional to the inlet flow rate and inversely proportional to the microcapsule density and size. The distribution of porosity indicates overall even fluidization, except for a dead region near the bottom periphery of the bioreactor. The dead region shrinks with increasing flow rate and expands with increasing density and size of the microcapsules. It is found that the interaction of microcapsules may play an essential role in the variations of the dead region. Understanding the fluidization dynamics of the impinging-jet-driven bioreactor is crucial in bioreactor design and optimization to improve its performance.

An updated one-dimensional two-layer coupled mathematical model is developed for landslide generated impulse wave (LGIW) from wave formation to long-duration travel. The coupled model is composed of a landslide model in a bed-fitted coordinate system and a shallow-water wave model in a global coordinate system. The coupling is realized through a reconstruction and interpolation algorithm. We successfully reproduced the experimental reproduction of 1958 Lituya LGIW for a total duration of 250 s at prototype scale, and well captured the three runups on the right bank and three runups on the left bank, together with the six wave crests and troughs in the channel. This seems to be the first reproduction of the Lituya LGIW for such a long duration. The predicted free surface elevation and runups are comparable with experimental results though with some deviations. However, the level of reproduction of the Lituya LGIW from wave formation to long-duration traveling achieved by numerical simulation is greatly enhanced as compared with the existing interface-capture models and mesh-free/particle-based models. This proposed one-dimensional two-layer coupled model could provide a unified framework for LGIWs from generation to long-duration propagation.

The interaction between fast shallow granular flow and obstacles on steep terrain is an important aspect of granular mechanics and defending against geological hazards. In this study, we used a depth-averaged model for granular flow facing obstacles on steep terrains in a bed-fitted coordinate system where the obstacle system is treated as a local bed deviation term. A second-order Riemann-free scheme is extended to compute the depth-averaged model with a wetting–drying technique, which is verified by several granular flow cases, such as aluminum bar collapse and granular flow runout on a steep slope. Numerical simulations were performed for the case of granular flow facing a (i) single hemispherical obstacle and (ii) system of three hemispherical obstacles to produce a dynamical process and deposit profile, and show good agreement with experimental results. Granular flow facing a single obstacle on a concave plane produces a detached shock wave that moves upstream and a tailing rapid transition zone that moves down, which will merge to form a new shock for deposition. Granular flows facing a three-hemisphere obstacle system produce a tailing rapid transition zone that moves downstream and a downstream wavy shock that results from the interaction of three bow shocks in front of each obstacle. The downstream wavy shock moves upstream and merges with the upstream transition zone to form a new curved shock, which later relaxes to a deposit owing to bed friction. These findings provide some supplemental understandings of flow structures of fast granular flow facing obstacles.

Vehicle detection is an important research topic for autonomous driving community. Since the great success of deep learning on object detection, almost all vehicle detection methods go along with this line. However, deep learning methods heavily rely on the training data, and the whole mechanism is like a “black box” Therefore, in this paper, we explore a vehicle detection method using traffic semantic context and human common sense instead of relying on the training data. To verify our idea, we compare our method with two classic machine learning methods as well as three state- of-the-art deep learning methods on a dataset collected in real traffics. The results show that our method outperforms others on this dataset. The deep learning methods may exceed ours after enlarging the training data or testing on more complicated datasets. However, the main contribution of this paper is providing inspiration for learning methods, and we believe their performance can be greatly improved after considering the idea of this paper.

4H-SiC junction-barrier Schottky (JBS) diodes blocking 1000 V have been fabricated. I-V characteristics have been evaluated at room temperature and 255 °C in comparison with the Schottky barrier (SB) and pin diodes fabricated on the same wafer. While the low reverse leakage confirms the functioning of JBS, the high forward current densities of 630 and 210 A cm-2 at 4.0 V at room temperature and 255 °C, respectively, with only ~20% reduction from those of the SB diodes, clearly demonstrate that the SiC JBS diodes can be fabricated with acceptable sacrifice in the forward current capacities.