scholarly journals Combustion Synthesis and Characterization of Porous NiTi Intermetallic for Structural Application

2012 ◽  
Author(s):  
J. Vanterpool ◽  
O. J. Ilegbusi ◽  
N. Khatami
Keyword(s):  
Combustion Synthesis ◽  
Biomedical Application ◽  
Processing Conditions ◽  
Analysis Software ◽  
Porosity Distribution ◽  
Image Analysis Software ◽  
Input Processing ◽  
Diamond Saw ◽  
Cylindrical Samples

This paper describes experimental investigation of thermal and combustion phenomena as well as structure for self-propagating combustion synthesis of porous Ni–Ti intermetallic aimed for structural biomedical application. The objective is to correlate processing conditions with structure for the porous material. Ni–Ti mixture is prepared from elemental powders of Ni and Ti. The mixture is pressed into solid cylindrical samples of 1.1 cm diameter and 2–3 cm length, with initial porosity ranging from 30% to 42%. The samples are preheated to various initial temperatures and ignited from the top surface such that the flame propagates axially downwards. The flame images are recorded with a motion camera as well as the temperature profile. The samples were then cut using a diamond saw in both longitudinal and latitudinal directions. Image analysis software was then used to analyze the porosity distribution in each sample. The porosity distribution was then systematically correlated with the input processing conditions.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Biomedical application of photo-crosslinked gelatin hydrogels

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Lei Xiang ◽  
Wenguo Cui
Keyword(s):  
Drug Delivery ◽  
Tissue Engineering ◽  
Biomedical Application ◽  
Biomedical Field ◽  
Or Gene ◽  
Tunable Mechanical Properties ◽  
Bio Compatibility ◽  
Regeneration Of Bone ◽  
Development Prospects

Abstract During the past decades, photo-crosslinked gelatin hydrogel (methacrylated gelatin, GelMA) has gained a lot of attention due to its remarkable application in the biomedical field. It has been widely used in cell transplantation, cell culture and drug delivery, based on its crosslinking to form hydrogels with tunable mechanical properties and excellent bio-compatibility when exposed to light irradiation to mimic the micro-environment of native extracellular matrix (ECM). Because of its unique biofunctionality and mechanical tenability, it has also been widely applied in the repair and regeneration of bone, heart, cornea, epidermal tissue, cartilage, vascular, peripheral nerve, oral mucosa, and skeletal muscle et al. The purpose of this review is to summarize the recent application of GelMA in drug delivery and tissue engineering field. Moreover, this review article will briefly introduce both the development of GelMA and the characterization of GelMA. Finally, we discuss the challenges and future development prospects of GelMA as a tissue engineering material and drug or gene delivery carrier, hoping to contribute to accelerating the development of GelMA in the biomedical field. Graphical abstract

Manufacturing and Characterization of Tantalum Microplasma Coatings for Biomedical Application

2020 ◽  
Author(s):  
D. L. Alontseva ◽  
A. R. Khozhanov ◽  
S. S. Gert ◽  
A.L. Krasavin ◽  
N.V. Prokhorenkova ◽  
...  
Keyword(s):  
Biomedical Application

Image analysis software and sample preparation demands

1990 ◽  
Author(s):  
Karl n. Roth ◽  
Knut Wenzelides ◽  
Guenter Wolf ◽  
Peter Hufnagl
Keyword(s):  
Image Analysis ◽  
Sample Preparation ◽  
Analysis Software ◽  
Image Analysis Software
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