Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system

Multimedia Tools and Applications - Recently, many concepts in technology has been changed. According to the digital transformation trends, Internet of Things (IoT) represents an interested...     

低压静电场辅助冷冻对竹笋品质的影响

为研究低压静电场辅助冷冻对竹笋冻结特性的影响，以冻结曲线、硬度、水分损失率、水分迁移、冰晶形态和组织微观结构为指标，探究低压静电场辅助冷冻（－35 ℃）和普通冷冻（－35 ℃）条件下竹笋品质的变化规律。结果表明：低压静电场辅助冷冻提高了冻结效率，改变了冰晶形态及分布，减轻了组织微观结构破损程度，改善了解冻汁液流失情况。与静电板间距10、20、30、40 cm处的冷冻竹笋解冻后水分损失率分别为14.16%、12.58%、9.73%、10.44%，显著低于对照组（21.01%）（P＜0.05），硬度分别为461.19、507.48、496.65 g和455.31 g，显著高于对照组（350.70 g）（P＜0.05）。低场核磁共振分析结果表明，在低压静电场辅助冷冻下竹笋解冻后汁液流失减少，扫描电子显微镜观察结果显示，竹笋纤维排列整齐，组织微观结构保持较好。低压静电场辅助冷冻可有效改善竹笋品质，可为利用低压静电场进行果蔬的冷冻贮藏和冷链运输提供参考。     

Diluted chemical bath deposition of CdZnS as prospective buffer layer in CIGS solar cell

In this study, dilute chemical bath deposition technique has been used to deposit CdZnS thin films on soda-lime glass substrates. The structural, morphological, optoelectronic properties of as-grown films have been investigated as a function of different Zn²⁺ precursor concentrations. The X-ray diffractogram of CdS thin-film reveals a peak corresponding to (002) plane with wurtzite structure, and the peak shift has been observed with the increase of the Zn²⁺ concentration upon formation of CdZnS thin film. From morphological studies, it has been revealed that the diluted chemical bath deposition technique provides homogeneous distribution of film on the substrate even at a lower concentration of Zn²⁺. Optical characterization has shown that the transparency of the film is influenced by Zn²⁺ concentration and when the Zn²⁺ concentration is varied from 0 M to 0.0256 M, bandgap values of resulting films range from 2.42 eV to 3.90 eV while. Furthermore, electrical properties have shown that with increasing zinc concentration the resistivity of the film increases. Finally, numerical simulation validates and suggests that CdZnS buffer layer with composition of 0.0032 M Zn²⁺ concentration would be a promising candidate in CIGS solar cell.     

Modeling of Ignition and Combustion of a Coflowing Hydrogen Jet in a Supersonic Air Flow

Combustion, Explosion, and Shock Waves - Results of a numerical study of mixing, ignition, and combustion of a cold hydrogen jet propagating along the lower wall of a channel parallel to a...     

Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization

Multimedia Tools and Applications - In this work, a new fuzzy logic-based algorithm is proposed for the enhancement of low light color images. A generalization of a fuzzy set known as an...     

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...     

Low Temperature Characteristics of the Metal–Superconductor NIS Tunneling Thermometer

We discuss the temperature dependence of a common low temperature local thermometer, a tunnel junction between a superconductor and a normal metal (NIS junction). Towards the lowest temperatures its characteristics tend to saturate, which is usually attributed to selfheating effects. In this technical note, we reanalyze this saturation and show that the temperature independent subgap current of the junction alone explains in some cases the low temperature behavior quantitatively.

     

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.     

Effect of Drosophila suzukii on Blueberry VOCs: Chemical Cues for a Pupal Parasitoid,Trichopria anastrephae

Journal of Chemical Ecology - Biocontrol agents such as parasitic wasps use long-range volatiles and host-associated cues from lower trophic levels to find their hosts. However, this chemical...