A Ministry of Road Transport and Highways chair is being created at IIT Madras, to focus on research and development (R&D), teaching and training in traffic and highway engineering.

The Indian Institute of Technology (IIT), Madras has collaborated with the Ministry of Road Transport and Highways (MoRTH) for research in areas of pavement engineering and intelligent transportation systems.

According to officials, their collaborative work will include research on novel pavement materials and technologies, hydrogen cell transportation, automatic vehicle classification, novel toll systems, incident management systems, traveller information systems, FastTAG data analytics and traffic simulations besides transportation safety.

A MoRTH chair is being created at IIT Madras, to focus on research and development (R&D), teaching and training in traffic and highway engineering. The chair professor will act as a strategic advisor to the ministry.

The chair professor will interface with the Standard and Research (S&R) wing of the ministry, facilitating need-based research in the area of ‘traffic and highway engineering’.

“IIT Madras should concentrate on developing less polluting pavement engineering technologies as well as intelligent transportation systems. We are already engaged with IIT Madras in a major way in terms of consultancies,” said Giridhar Aramane, Secretary, Road Transport and Highways.

“This chair professorship as well as this entire programme can be directed towards important and innovative research and development work,” Mr. Aramane said.

The chair professor will also undertake R&D activities in pavement engineering, including emerging materials and technologies such as use of coir and other bio-derived materials, use of alternative aggregates such as recycled concrete and asphalt pavement and use of environmentally sustainable materials for highway pavements.

“This collaboration will also facilitate PhD research programmes on areas selected by MoRTH. IIT Madras will train 8 to 10 students including MoRTH’s nominated officers in the field of highway engineering,” said IIT Madras Director Bhaskar Ramamurthi.

“The chair will will provide an impetus for application-oriented research. We are already active in pavement engineering and intelligent transportation systems. A lot of our know-how already reaches the field through industry collaboration. Our efforts have also reduced accidents on highways across the country. We are well-placed to take up interesting projects and seed them with the proceeds of this endowment,” he added.

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The technique exploits the quantum emission properties of erbium-doped nanoparticles

Researchers from IIT Madras and IISER Kolkata have developed a method to detect minute quantities of chemicals in solution. They use a variation of absorption spectroscopy that surpasses the systemic limits imposed by conventional absorption spectroscopy. With this technique, they can, in principle, illuminate the insides of cells and detect minuscule quantities of substances present there. The work was published in Nanoscale. Spectroscopy, the tool Absorption spectroscopy is a tool to detect the presence of elements in a medium. Light is shone on the sample, and afterit passes through the sample is examined using a spectroscope.

Dark lines are seen in the observed spectrum of the light passed through the substance, which correspond to the wavelengths of light absorbed by the intervening substance and are characteristic of the elements present in it. In usual methods, about a cubic centimetre of the sample is needed to do this experiment.In the method developed here, minute amounts of dissolved substances can be detected easily.
Usually in absorption spectroscopy, the principle used is that light because of its wavelike nature, shows diffraction patterns, that is, dark and light fringes, when it scatters off any object.

A related concept called the Abbe criterion sets a natural limit on the size of the object being studied. According to this criterion, the size of the observed object has to be at least of the order of the wavelength of the light being shone on it.”If you want to perform absorption spectroscopy using visible light, namely, blue, green and red, the wavelengths [of these colours] are about 400 nm, 500 nm and 600 nm,respectively. the diffraction limit is typically half of that, about 200 nm forthe blue light,” explains Basudev Roy, from the Department of Physics of IIT Madras and one of the corresponding authors of the study along with Ayan Banerjee of IISER Kolkata.

In the method used by the researchers here, tiny, nano-sized particles that can absorb light being shone on them and re-emitred, blue and green light were employed.”We use a nanoparticle of sodium yttrium fluoride (a kind of glass) with some dopants, which has the special property that when you excite this with infra-red light at 975 nm, it emits blue, green and red light from the particle itself,” says Dr Roy. These particles were made by M. Gunaseelan at Department of Physics, University of Madras.

Like a bar magnet
The particles emit electric fields that are analogous to how a tiny magnet would give off magnetic lines of force – this is called a dipole, and the particle is like a tiny mobile phone’s antenna.”Our dipole… generates an electromagnetic field depending upon the quantum properties of the erbium dopants in the glass. Our emission pattern is typically limited to a cone of 45 degrees, starting from a diameter of the size of the particle,” he adds.

The absorption leaves a gap in the reflected light, which is what is observed and used to analyse the nature of the absorbing material. Since this works at the level of photons, this surpasses the limit on the size of the substance or sample being studied.
Inside living cells. There are many potential applications.”We are ourselves going to put these particles inside living cells, and the emission can be used as a tiny flash lamp to look for absorption from individual molecules in the close proximity to the particle,” he says.”This is way in which small molecules almost ten-millionth of a mm in diameter can be detected while these pass the emission region of the glass particle… The future is to use it to measure individual molecules, see an absorption spectroscopy of a single DNA or protein molecule.”

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Two teams that come up with best winning ideas will receive mentorship support and prize money.

The Indian Institute of Technology-Madras and Capgemini, a multinational IT company, are hosting a contest “Engineering Technologies” for students of polytechnics in the State.

The Centre for Social Innovation and Entrepreneurship (CSIE) that focuses on teaching and research related to social enterprise is coordinating the contest. Participants must submit their ideas for the online contest by August 21.

The organisers are looking for ideas that will enhance livelihood among farmers and artisan communities and create more employment opportunities.

The teams that come up with the two best innovative ideas will receive a‚¹50,000 each and receive mentorship support from the CSIE and IIT-Madras faculty to develop it to prototype stage.

CSIE project coordinator R. Nagarajan said the alumni-driven initiative CSIE@IITM “focuses on promoting social and entrepreneurship through various incentives and activities. IDEA SPARK, in particular, has been successful over the years in motivating college students to ideate and innovate in this context,” he said.

Polytechnic students of second and third year can participate in the contest. Each group may have a maximum of four students. There is no restriction on the number of groups and ideas an institution can send. Ideas related to IoT, IT and AI will not be accepted.

All participants will receive a certificate. Registration forms are available at CSIE’s website http://csie.iitm.ac.in

For details, email subaramesh.9@gmail.com

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Active and neutral mutations are distinguished by studying their neighbourhoods.

Researchers at IIT Madras have developed an AI tool called NBDriver(neighbourhood driver) for use in analysing cancer-causing mutations in cells. By looking at the neighbourhood, or context, of a mutation in the genome, it can look at harmful”driver” mutations and distinguish them from neutral”passenger” mutations.
This technique of looking at the genomic neighbourhood to make out the nature of the
mutation is a novel and largely unexplored one. In a paper published in the journal Cancers,

the researchers explain that the nature of the mutation depends on the neighbourhood, and how this tool may be used to draw the line between driver and passenger mutations.

B. Ravindran, head of the Robert Bosch Centre for Data Science and AI at IIT Madras and one of the corresponding authors, said in a press release that one of the major challenges faced by cancerresearchers involves the differentiation between the relatively small number of “driver” mutations that enable the cancer cells to grow and the large number of”passenger” mutations that do not have any effect on the progression of the disease.

In previously published techniques,researchers typically analysed DNA sequences from large groups of cancer patients, comparing sequences from cancer as well as normal cells and determined whether a particular mutation occurred more often in cancer cells than random, said Prof. Karthik Raman, from the biotechnology department of IIT Madras and another corresponding author.”However, this ‘frequentist’ approach often missed out on relatively rare driver mutations,” he noted, adding that some studies have also looked at the changes caused by the driver mutations in the production of essential biological products such as proteins.

Statistical modelling
The method of distinguishing between driver and passenger mutations solely by looking at the neighbourhood is novel.”Through robust statistical modelling, we show that there is a significant difference in the pattern of sequences (or context) surrounding the driver and passenger mutations,” said Shayantan Banerjee, who is a master’s student in the Department of Biotechnology, IIT Madras, and the lead author of the paper.

Accuracy of tool
The researchers studied a dataset containing 5,265 mutations to derive the model. According to Prof. Raman, NBDriver, had an overall accuracy of 89% and ranked second out of 11 prediction algorithms. In comparison, he said that the top performing tool, or FATHMM, achieved an accuracy of 91% on the same dataset.

For the future, the group aims to develop an easy-to-use drag-and-drop web interface that will enable cancerresearchers with limited computational or programming skills to get predictions and extract genomic information on their preferred set of mutations.”We will also be pursuing further studies on the context [or neighbourhood] of these mutations, and how they impact the evolution of cancer. Why do we see differences in the context between the driver and passenger mutations in the first place?” said Prof Raman.

The group also plans that NBDriver will be a part of a broader cancer genomic sequence analysis “pipeline” being developed at the centres.

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