24 January 2018

Research Picks Weekly – 24 Jan 2018

Green nanoparticles
An extract of Cannabis sativa, commonly known as hemp, can be used in a “green” (meaning environmentally benign) synthetic scheme for the fabrication of antimicrobial gold nanoparticles. The team used a fixed ratio of ethanolic plant extract and metal ions from gold chloride solution and saw a colour change associated with nanoparticle formation. These were further characterized using spectroscopy and scanning microscopy methods, which revealed the gold nanoparticles to be spherical, as hoped. Tests against various bacteria showed the powerful antimicrobial properties of the particles.
“Phytofabrication, characterisation and antimicrobial studies of gold nanoparticles using Cannabis sativa (Bhang), leaf extract”, Neeraj Sharma, Shailendra Pratap Singh, and Uma Rani Sharma, Int. J. Nanoparticles, 2017, 9, 143 – 152; DOI: 10.1504/IJNP.2017.10010010

Plastic not so fantastic

Plastic waste is high on the environmental agenda at the moment, not least the amount of packaging and other waste that is contaminating the world’s oceans. Researchers in India point out that plastic waste contributes a major proportion of the total municipal solid waste. However, the desirable properties of plastics such as its ability not to degrade under a wide range of conditions and its durability make it even more of a problem once it is disposed of if it is not recycled or reused. Priyanka Gupta emphasizes the 3R approach we should be taking to plastic waste management – reduce, reuse and recycle.
“Management of plastic waste: a step towards clean environment”, Priyanka Gupta, Int J Renewable Energy, 2018, 8, 387-392; DOI: 10.1504/IJRET.2017.088986

Catching phish

Phishing attacks are the most insidious and perhaps the most common way in which security systems are breached by malware users. They generally involve disguising an email or website as a legitimate source but tricking the reader into clicking a link or connecting to a server that harvests their private or personal data including passwords, bank details, and other information useful to criminals. Researchers in India are now working on an intelligent way to detect “phish” and prevent users from being reeled in by such scams. The detection system works by comparing the hidden style code, the CSS within the phish with the legitimate code used by the website or other system the phishing email is attempting to spoof. The team claims close to 100% detection rates and very few false positives with the approach.
“Intelligent phishing detection system using similarity matching algorithms”, Ankur Mishra and B.B. Gupta, Int. J. Information Communication Technol, 2018, 12, 51-73; DOI: 10.1504/IJICT.2018.10008898

Stroke prediction
Early diagnosis and prognosis of cardiovascular accident (CVA) colloquially known as “stroke” are crucial for timely prevention and cure. Machine learning and intelligence are now being investigated for their potential to a weigh the various physiological parameters and risk factors for CVA and so offer healthcare workers a predictive diagnostic for their patients. Data was collected from international stroke trial database and successfully used to train and test an algorithm for stroke predication.  The team tested both support vector machines (SVM) and neural networks as machine intelligence paradigms. The former revealed 91% accuracy while the neural network was 98% accurate in proof of principle tests on the historic data.
“Machine intelligence in stroke prediction”, R.S. Jeena and Sukesh Kumar, Int. J. Bioinformat Res Applic, 2018 14, 29-48; DOI: 10.1504/IJBRA.2018.10009163

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