research paper on transfer of technology

Competence Centre on Technology Transfer

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What is technology transfer?

Research transforms money into knowledge … technology transfer transforms knowledge into money.” Geoffrey Nicholson, father of the Post-It

Technology transfer (TT) refers to the process of conveying results stemming from scientific and technological research to the market place and to wider society, along with associated skills and procedures, and is as such an intrinsic part of the technological innovation process.

Technology transfer is a complex process that involves many non-scientific and non-technological factors, and many different stakeholders. Good or high quality research results are not enough for successful technology transfer; general awareness and willingness both at the level of organisations and individuals, as well as skills and capacity related to specific aspects, such as access to risk finance and intellectual property (IP) management, are also necessary components.

The fundamental steps of the technology transfer process are depicted in the figure below.

Technology transfer covers the complex value chain linking research to its eventual societal deployment. This begins with the discovery of novel technologies at research institutions, followed by the disclosure, evaluation and protection of these technologies. The next steps include marketing, potential licencing agreements and the development of products based on the technical inventions. The financial returns of these products can then, for instance, be used for further research.

Where can I find more detailed information on technology transfer?

We invite you to go through our repository which combines numerous documents and publications on this topic.

You can also learn more by following the activities of the European Commission's  Competence Centre on Technology Transfer .

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Study explains why the brain can robustly recognize images, even without color

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Even though the human visual system has sophisticated machinery for processing color, the brain has no problem recognizing objects in black-and-white images. A new study from MIT offers a possible explanation for how the brain comes to be so adept at identifying both color and color-degraded images.

Using experimental data and computational modeling, the researchers found evidence suggesting the roots of this ability may lie in development. Early in life, when newborns receive strongly limited color information, the brain is forced to learn to distinguish objects based on their luminance, or intensity of light they emit, rather than their color. Later in life, when the retina and cortex are better equipped to process colors, the brain incorporates color information as well but also maintains its previously acquired ability to recognize images without critical reliance on color cues.

The findings are consistent with previous work showing that initially degraded visual and auditory input can actually be beneficial to the early development of perceptual systems.

“This general idea, that there is something important about the initial limitations that we have in our perceptual system, transcends color vision and visual acuity. Some of the work that our lab has done in the context of audition also suggests that there’s something important about placing limits on the richness of information that the neonatal system is initially exposed to,” says Pawan Sinha, a professor of brain and cognitive sciences at MIT and the senior author of the study.

The findings also help to explain why children who are born blind but have their vision restored later in life, through the removal of congenital cataracts, have much more difficulty identifying objects presented in black and white. Those children, who receive rich color input as soon as their sight is restored, may develop an overreliance on color that makes them much less resilient to changes or removal of color information.

MIT postdocs Marin Vogelsang and Lukas Vogelsang, and Project Prakash research scientist Priti Gupta, are the lead authors of the study, which appears today in Science . Sidney Diamond, a retired neurologist who is now an MIT research affiliate, and additional members of the Project Prakash team are also authors of the paper.

Seeing in black and white

The researchers’ exploration of how early experience with color affects later object recognition grew out of a simple observation from a study of children who had their sight restored after being born with congenital cataracts. In 2005, Sinha launched Project Prakash (the Sanskrit word for “light”), an effort in India to identify and treat children with reversible forms of vision loss.

Many of those children suffer from blindness due to dense bilateral cataracts. This condition often goes untreated in India, which has the world’s largest population of blind children, estimated between 200,000 and 700,000.

Children who receive treatment through Project Prakash may also participate in studies of their visual development, many of which have helped scientists learn more about how the brain's organization changes following restoration of sight, how the brain estimates brightness, and other phenomena related to vision.

In this study, Sinha and his colleagues gave children a simple test of object recognition, presenting both color and black-and-white images. For children born with normal sight, converting color images to grayscale had no effect at all on their ability to recognize the depicted object. However, when children who underwent cataract removal were presented with black-and-white images, their performance dropped significantly.

This led the researchers to hypothesize that the nature of visual inputs children are exposed to early in life may play a crucial role in shaping resilience to color changes and the ability to identify objects presented in black-and-white images. In normally sighted newborns, retinal cone cells are not well-developed at birth, resulting in babies having poor visual acuity and poor color vision. Over the first years of life, their vision improves markedly as the cone system develops.

Because the immature visual system receives significantly reduced color information, the researchers hypothesized that during this time, the baby brain is forced to gain proficiency at recognizing images with reduced color cues. Additionally, they proposed, children who are born with cataracts and have them removed later may learn to rely too much on color cues when identifying objects, because, as they experimentally demonstrated in the paper, with mature retinas, they commence their post-operative journeys with good color vision.

To rigorously test that hypothesis, the researchers used a standard convolutional neural network, AlexNet, as a computational model of vision. They trained the network to recognize objects, giving it different types of input during training. As part of one training regimen, they initially showed the model grayscale images only, then introduced color images later on. This roughly mimics the developmental progression of chromatic enrichment as babies’ eyesight matures over the first years of life.

Another training regimen comprised only color images. This approximates the experience of the Project Prakash children, because they can process full color information as soon as their cataracts are removed.

The researchers found that the developmentally inspired model could accurately recognize objects in either type of image and was also resilient to other color manipulations. However, the Prakash-proxy model trained only on color images did not show good generalization to grayscale or hue-manipulated images.

“What happens is that this Prakash-like model is very good with colored images, but it’s very poor with anything else. When not starting out with initially color-degraded training, these models just don’t generalize, perhaps because of their over-reliance on specific color cues,” Lukas Vogelsang says.

The robust generalization of the developmentally inspired model is not merely a consequence of it having been trained on both color and grayscale images; the temporal ordering of these images makes a big difference. Another object-recognition model that was trained on color images first, followed by grayscale images, did not do as well at identifying black-and-white objects.

“It’s not just the steps of the developmental choreography that are important, but also the order in which they are played out,” Sinha says.

The advantages of limited sensory input

By analyzing the internal organization of the models, the researchers found that those that begin with grayscale inputs learn to rely on luminance to identify objects. Once they begin receiving color input, they don’t change their approach very much, since they’ve already learned a strategy that works well. Models that began with color images did shift their approach once grayscale images were introduced, but could not shift enough to make them as accurate as the models that were given grayscale images first.

A similar phenomenon may occur in the human brain, which has more plasticity early in life, and can easily learn to identify objects based on their luminance alone. Early in life, the paucity of color information may in fact be beneficial to the developing brain, as it learns to identify objects based on sparse information.

“As a newborn, the normally sighted child is deprived, in a certain sense, of color vision. And that turns out to be an advantage,” Diamond says.

Researchers in Sinha’s lab have observed that limitations in early sensory input can also benefit other aspects of vision, as well as the auditory system. In 2022, they used computational models to show that early exposure to only low-frequency sounds, similar to those that babies hear in the womb, improves performance on auditory tasks that require analyzing sounds over a longer period of time, such as recognizing emotions. They now plan to explore whether this phenomenon extends to other aspects of development, such as language acquisition.

The research was funded by the National Eye Institute of NIH and the Intelligence Advanced Research Projects Activity.

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Environmental and economic analysis of the transformation of paper mill sludge treatment technologies in China

• Research Article
• Published: 27 May 2024

Cite this article

• Yanfei Lin 1 ,
• Guoxia Wei 2 ,
• Hanqiao Liu 1 ,
• Yuwen Zhu   ORCID: orcid.org/0000-0003-2759-0656 1 ,
• Qianlong Han 1 ,
• Yunzhen Yang 1 &
• Yi Lian 3  

Paper mill sludge (PMS) is featured with a high content of cellulose and hemicellulose, and using its characteristics to make paperboard can achieve a high-value utilization of PMS, which has attracted growing interest. In this study, currently prevalent landfill, incineration technologies (generating heat and electricity by incineration), and three paperboard technologies (medium density fiberboard, pulp board, and corrugated paper) were evaluated and compared via life cycle assessment (LCA) and life cycle costing (LCC) methods. LCA results show that the PMS-to-pulp board outperforms others with an energy conservation and emission reduction (ECER) value of − 2.86 × 10 −8 , while the landfill exhibits the highest overall environmental impact with an ECER value of 4.80 × 10 −9 . LCC results reveal that the PMS-to-pulp board delivers the highest economic profit with $257.357, while the landfill is the lowest with $ − 35.63. The PMS paperboard technologies are more economically friendly than the incineration technologies due to additional electricity/steam consumption during the PMS pre-drying process in incineration. In addition, different scenarios were set up to explore national GHG emission reduction potential by increasing paperboard technologies application rate and reducing the proportion of landfill and incineration. The scenario analysis suggests that replacing 90% of landfill and incineration ratio with PMS paperboard technologies could tremendously improve the overall emission reduction performance with − 9.08 × 10 10 kg CO 2 eq. This result indicates that the PMS treatment technology transformation has a significant favorable impact on the achievement of the “carbon neutrality” target.

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The data that supports the findings of this study are available in the Supplementary Information.

Abbreviations

• Paper mill sludge

Medium density fiberboard

• Life cycle assessment
• Life cycle costing

Life cycle inventory

Chinese life cycle database

Energy conservation and emission reduction

Greenhouse gas

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The authors would like to acknowledge the foundation of Tianjin Research Innovation Projects for Postgraduate Students (Grant no. 2022SKYZ208 and Grant no. 2022SKYZ180).

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Yanfei Lin, Hanqiao Liu, Kai Li, Yuwen Zhu, Qianlong Han & Yunzhen Yang

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Contributions

Yanfei Lin: conceptualization, methodology, data curation, writing—original draft. Guoxia Wei: writing—review and editing. Hanqiao Liu: conceptualization, methodology, writing—review and editing. Kai Li: methodology and editing. Yuwen Zhu: conceptualization, writing—review and editing. Qianlong Han: conceptualization, methodology, writing—review and editing. Yunzhen Yang: writing—review and editing. Yi Lian: writing—review and editing.

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Correspondence to Yuwen Zhu .

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Lin, Y., Wei, G., Liu, H. et al. Environmental and economic analysis of the transformation of paper mill sludge treatment technologies in China. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33708-w

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Received : 07 January 2024

Accepted : 13 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1007/s11356-024-33708-w

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