Detection of crowdedness in bus compartments based on ResNet algorithm and video images

Multimedia Tools and Applications - The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding...     

A multilayer recognition model for twitter user geolocation

Wireless Networks - Geolocation is important for many emerging applications such as disaster management and recommendation system. In this paper, we propose a multilayer recognition model (MRM) to...     

Effect of high hydrostatic pressure pretreatment on flavour and physicochemical properties of freeze-dried carambola slices

Ripe carambolas are hard to store and transport, while freeze-dried ones are easy to store. However, its long production time leads to higher costs. This study shows that high hydrostatic pressure (HHP) treatment could shorten the freeze-drying time of carambola slices. After HHP treatment (25–250 MPa), the drying time of the fresh sample can be shortened by 33.3–44.4% and the distribution of water and pigment in tissues is much uniform. With the increment of the pressure, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl radical scavenging rate are increased. At 250 MPa, the total phenolic content (TPC) increased from 11.34 to 13.36 mg GAE g⁻¹, and the total flavonoid content (TFC) of the control sample was increased from 10.77 to 12.73 mg RE g⁻¹. Compared with the untreated sample, HHP treatment can enhance the flavour and shorten the freeze-drying time. This work guides the application of HHP technology for drying food processing.     

Theoretical investigation of intrinsic point defects and the oxygen migration behavior in rare earth hafnates

In this work, the composition-dependent point defect types and formation energies of RE₂Hf₂O₇ (RE = La, Ce, Pr, Nd, Pm, Sm, Eu and Gd) as well as the oxygen diffusion behavior are systematically investigated by first-principles calculations. The possible defect reactions and dominant defect complexes under stoichiometric and non-stoichiometric conditions are revealed. It is found that O Frenkel pairs are the predominant defect in stoichiometric pyrochlore hafnates. Hf-RE cation anti-site defects, accompanied by RE vacancies and/or oxygen interstitials, are stable in the non-stoichiometric case of HfO₂ excess. On the other hand, RE-Hf anti-site defects together with oxygen vacancies and/or RE interstitials are preferable in the case of RE₂O₃ excess. The energy barriers for the migration along the V_O48f - V_O48f pathway of pyrochlore hafnates were calculated to be between 0.81 eV and 0.89 eV. Based on these results, a defect engineering strategy is proposed and the pyrochlore hafnates investigated here are predicted to exhibit potential oxygen ionic conductivity.     

Low-Velocity Impact Damage Simulation of Biaxial Warp-Knitted Flexible Composite with Simplified Microstructure Model

Applied Composite Materials - In this paper, the low-velocity impact deformation behavior of biaxial warp-knitted flexible composite was investigated. A simplified finite element model (FEM) of the...     

Three dimensional reconstruction of brain tumor along with space occupying in lesions

Multimedia Tools and Applications - The three-dimensional models of brain tumors serve as diagnostic assistance for physicians, surgeons, and radiologists. The proposed system establishes an...     

Alkylated lignin with graft copolymerization for enhancing toughness of PLA

Journal of Materials Science - A green modification method for effectively enhancing toughness of PLA was established. Herein, alkaline lignin (LG) was firstly alkylated with dodecane, and then...     

Substrate temperature-controlled precursor reaction mechanism of PEALD-deposited MoOx thin films

Journal of Materials Science - Molybdenum oxide (MoOx) films had been grown by using plasma-enhanced atomic layer deposition (PEALD) with Mo(CO)6 precursor and O2 plasma reactant in a substrate...     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.