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Architecture using Jini technology for simulation of an agent-based transportation system

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Jini technology applied to railway systems

2000, Proceedings DOA'00. International Symposium on Distributed Objects and Applications

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Research on internal quality testing method of dry longan based on terahertz imaging detection technology

• Original Paper
• Published: 11 May 2024

Cite this article

• Jun Hu   ORCID: orcid.org/0000-0003-0027-7993 1 ,
• Hao Wang 1 ,
• Yongqi Zhou 1 ,
• Shimin Yang 1 ,
• Haohao Lv 1 &
• Liang Yang 1  

Longan is a kind of nut with rich nutritional value and homologous function of medicine and food. The quality of longan directly affects its curative effect, and its fullness is the key index to evaluate its quality. However, the internal information of longan cannot be obtained from the outside. Therefore, rapid non-destructive testing of internal quality of dry longan is of great significance. In this paper, rapid non-destructive testing of longan internal fullness based on terahertz transmission imaging technology was carried out. This study takes longan as the research object. Firstly, the terahertz transmission images of longans with different fullness were collected, and the terahertz spectral signals of different regions of interest were extracted for analysis. Then, three qualitative discriminant models, support vector machine (SVM), Random forest (RF) and linear discriminant analysis (LDA), were established to explore the optimal discriminant model and realize the distinction of different regional categories of longan. Finally, the collected longan terahertz transmission image is processed, and the number of white pixels in the connected domain is calculated by using Otsu threshold segmentation and image inversion. The fullness of longan can be achieved by calculating the ratio of core and pulp to the pixel of the shell. The LDA discriminant model had the best prediction effect. It could not only identify the spectral data of background region, shell region, core region, but also reach 98.57% accuracy for the spectral data of pulp region. The maximum error between the measured fullness and the actual fullness of the terahertz image processed by Otsu threshold segmentation is less than 3.11%. Terahertz imaging technique can realize rapid non-destructive detection of longan fullness and recognition of different regions. This study provides an effective scheme for selecting the quality of longan.

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Acknowledgements

National Youth Natural Science Foundation of China (32302261); Jiangxi Ganpo Talented Support Plan -Young science and technology talent Lift Project (2023QT04); Jiangxi Provincial Youth Science Fund Project (20224BAB215042); National Key R&D Program of China (2022YFD2001805).

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School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

Jun Hu, Hao Wang, Yongqi Zhou, Shimin Yang, Haohao Lv & Liang Yang

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Jun Hu: Investigation, Writing-review and editing, Experimental scheme design, Formal analysis. Hao Wang: Writing-original draft, Formal analysis. Yongqi Zhou: Experiment. Shimin Yang, Haohao Lv: Review and editing. Liang Yang: Formal analysis.

Corresponding author

Correspondence to Jun Hu .

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. Jun Hu, Hao Wang, Yongqi Zhou, Shimin Yang, Haohao Lv, Liang Yang declare that they have no conflict of interest.

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Hu, J., Wang, H., Zhou, Y. et al. Research on internal quality testing method of dry longan based on terahertz imaging detection technology. Food Measure (2024). https://doi.org/10.1007/s11694-024-02583-x

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Received : 29 December 2023

Accepted : 18 April 2024

Published : 11 May 2024

DOI : https://doi.org/10.1007/s11694-024-02583-x

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Google helped make an exquisitely detailed map of a tiny piece of the human brain

A small brain sample was sliced into 5,000 pieces, and machine learning helped stitch it back together.

• Cassandra Willyard archive page

A team led by scientists from Harvard and Google has created a 3D, nanoscale-resolution map of a single cubic millimeter of the human brain. Although the map covers just a fraction of the organ—a whole brain is a million times larger—that piece contains roughly 57,000 cells, about 230 millimeters of blood vessels, and nearly 150 million synapses. It is currently the highest-resolution picture of the human brain ever created.

To make a map this finely detailed, the team had to cut the tissue sample into 5,000 slices and scan them with a high-speed electron microscope. Then they used a machine-learning model to help electronically stitch the slices back together and label the features. The raw data set alone took up 1.4 petabytes. “It’s probably the most computer-intensive work in all of neuroscience,” says Michael Hawrylycz, a computational neuroscientist at the Allen Institute for Brain Science, who was not involved in the research. “There is a Herculean amount of work involved.”

Many other brain atlases exist, but most provide much lower-resolution data. At the nanoscale, researchers can trace the brain’s wiring one neuron at a time to the synapses, the places where they connect. “To really understand how the human brain works, how it processes information, how it stores memories, we will ultimately need a map that’s at that resolution,” says Viren Jain, a senior research scientist at Google and coauthor on the paper, published in Science on May 9 . The data set itself and a preprint version of this paper were released in 2021 .

Brain atlases come in many forms. Some reveal how the cells are organized. Others cover gene expression. This one focuses on connections between cells, a field called “connectomics.” The outermost layer of the brain contains roughly 16 billion neurons that link up with each other to form trillions of connections. A single neuron might receive information from hundreds or even thousands of other neurons and send information to a similar number. That makes tracing these connections an exceedingly complex task, even in just a small piece of the brain..  

To create this map, the team faced a number of hurdles. The first problem was finding a sample of brain tissue. The brain deteriorates quickly after death, so cadaver tissue doesn’t work. Instead, the team used a piece of tissue removed from a woman with epilepsy during brain surgery that was meant to help control her seizures.

Once the researchers had the sample, they had to carefully preserve it in resin so that it could be cut into slices, each about a thousandth the thickness of a human hair. Then they imaged the sections using a high-speed electron microscope designed specifically for this project. 

Next came the computational challenge. “You have all of these wires traversing everywhere in three dimensions, making all kinds of different connections,” Jain says. The team at Google used a machine-learning model to stitch the slices back together, align each one with the next, color-code the wiring, and find the connections. This is harder than it might seem. “If you make a single mistake, then all of the connections attached to that wire are now incorrect,” Jain says. 

“The ability to get this deep a reconstruction of any human brain sample is an important advance,” says Seth Ament, a neuroscientist at the University of Maryland. The map is “the closest to the  ground truth that we can get right now.” But he also cautions that it’s a single brain specimen taken from a single individual. 

The map, which is freely available at a web platform called Neuroglancer , is meant to be a resource other researchers can use to make their own discoveries. “Now anybody who’s interested in studying the human cortex in this level of detail can go into the data themselves. They can proofread certain structures to make sure everything is correct, and then publish their own findings,” Jain says. (The preprint has already been cited at least 136 times .) 

The team has already identified some surprises. For example, some of the long tendrils that carry signals from one neuron to the next formed “whorls,” spots where they twirled around themselves. Axons typically form a single synapse to transmit information to the next cell. The team identified single axons that formed repeated connections—in some cases, 50 separate synapses. Why that might be isn’t yet clear, but the strong bonds could help facilitate very quick or strong reactions to certain stimuli, Jain says. “It’s a very simple finding about the organization of the human cortex,” he says. But “we didn’t know this before because we didn’t have maps at this resolution.”

The data set was full of surprises, says Jeff Lichtman, a neuroscientist at Harvard University who helped lead the research. “There were just so many things in it that were incompatible with what you would read in a textbook.” The researchers may not have explanations for what they’re seeing, but they have plenty of new questions: “That’s the way science moves forward.” 

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