Automated segmentation of brain tumor based on improved U-Net with residual units

Multimedia Tools and Applications - The study presents a new approach to automate segmentation of clinically significant brain tumor and, to a certain extent, addresses two major issues associated...     

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...     

Comparison of yield and metabolites according to the types of hilling materials utilized during Glehnia littoralis sprout vegetable cultivation

Food Science and Biotechnology - Various hilling materials (rice hulls, pine sawdust, and perlite) were compared to produce sprout vegetables using beach silvertop (Glehnia littoralis Fr. Schm. ex...     

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...     

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.

     

基于全U网络的混凝土路面裂缝检测算法

针对现有的混凝土裂缝检测算法在各种复杂环境中检测精度不够、鲁棒性不强的问题,根据深度学习理论和U-net模型,提出一种全U型网络的裂缝检测算法。首先,依照原U-net模型路线构建网络;然后,在每个池化层后都进行一次上采样,恢复其在池化层之前的特征图规格,并将其与池化之前的卷积层进行融合,将融合之后的特征图作为新的融合层与原U-net网络上采样之后的网络层进行融合;最后,为了验证算法的有效性,在测试集中进行实验。结果表明,所提算法的平均精确率可达到83.48%,召回率为85.08%,F1为84.11%,相较于原U-net分别提升了1.48%,4.68%和3.29%,在复杂环境中也能提取完整裂缝,保证了裂缝检测的鲁棒性。     

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...     

Polysorbate identity and quantity dictate the extensional flow properties of protein-excipient solutions

While protein medications are promising for treatment of cancer and autoimmune diseases, challenges persist in terms of development and injection stability of high-concentration formulations. Here, the extensional flow properties of protein-excipient solutions are examined via dripping-onto-substrate extensional rheology, using a model ovalbumin (OVA) protein and biocompatible excipients polysorbate 20 (PS20) and 80 (PS80). Despite similar PS structures, differences in extensional flow are observed based on PS identity in two regimes: at moderate total concentrations where surface tension differences drive changes in extensional flow behavior, and at small PS:OVA ratios, which impact the onset of weakly elastic flow behavior. Undesirable elasticity is observed in ultra-concentrated formulations, independent of PS identity; higher PS contents are required to observe these effects than in analogous polymeric excipient solutions. These studies reveal novel extensional flow behaviors in protein-excipient solutions, and provide a straightforward methodology for assessing the extensional flow stability of new protein-excipient formulations.