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(Volume: 4, Issue: 3)
Li-Fi For Radiation-Free Secure Wireless Data Transmission
Can the light that we use to perceive something in the dark be used to transmit audio, image or video data too? Obviously, yes with a phenomenon, termed as Light Fidelity (Li-Fi). Li-Fi is a revolutionizing wireless data communication technology that can achieve bidirectional data transmission using only visible light from LED bulbs or similar other light sources. So, what makes Li-Fi revolutionizing than Wi-Fi, which has been effectively used over years for data transmission? The first of it is that the visible light covers a wider spectrum than the Radio Frequency (RF) spectrum used in Wi-Fi. It simply implies that Li-Fi could provide increased bandwidth and data rates in a congestion-free manner to the high-speed internet demanding real-time applications or users. The second revolutionizing fact is the radiation-free transmission of data using the harmless visible light spectrum. The third and the fourth facts are related to the security and cost, respectively. Usually, visible light does not travel across obstacles like, walls as RF waves. Hence, data transmission is restricted within the vicinity of light, improving security. Moreover, the data transmission capacity can be increased or decreased by just installing an optimal number of lighting sources, preventing the use of additional expensive hardware. In fact, there are theoretical findings in the past that Visible Light Communication (VLC) could achieve a massive data transmission of 10 GB/s. However, a real-time implementation to support long-range free-space communication of audio data was achieved by Omar Faruq, Kazi Rubaiyat Shahriar Rahman, Nusrat Jahan, Sakib Rokoni and Mosa Rabeya in International Review of Applied Sciences and Engineering, 14(3). Generally, a Li-Fi-based data transmit system employs a light source and a modulator at the transmitter end to transmit data via visible light spectrum, serving as the channel. In contrast, the receiver end has a photodetector that receives the optical signals, followed by a demodulator and a data interface. The authors have actually employed Laser as the light source for long-distance transmission of audio using a narrow spectrum. Specifically, the use of audio amplifiers and the highly light-sensitive photovoltaic array as the photodetector in their Li-Fi hardware have increased lossless transmission of high-quality audio. The authors assure data transfer rates of upto 2GB/s, setting up a new milestone of radiation-free, user-friendly, non-hazardous and high-data rate wireless transmission for the upcoming researchers to work upon. Image courtesy: www.freepik.com
Green-based Graphene for Enhancing Aluminium Nanocomposite Properties
Nowadays, aluminium nanocomposites have attracted the automotive, aircraft and electronics industry for their lightweight, durable and excellent electrical conductivity properties. Yet, their mechanical stability and wearing resistance degrade at high-stress or friction-prone applications, requiring some kind of self-lubrication. Graphene, a single atomic layer of carbon atoms from Graphite, looks promising to serve this purpose. With its large surface area of ~2630 m²/g, hydrophobic nature and high Young’s modulus value, the light-weight Graphene layer actually amplifies the stability, corrosion or wear resistance and other electrical or optical conductivity properties of aluminium nanocomposites. However, graphene manufacture with conventional methods like, mechanical, chemical or liquid-phase exfoliation, chemical vapor decomposition and epitaxial growth might end up in non-uniform or agglomerative graphene bonds with aluminium, liberating hazardous chemicals at times even!!! Hence, Balakrishnan Somasekaran, Alwarsamy Thirunarayanaswamy and Ilamathi Palanivel from Government College of Technology, Coimbatore, India have aimed to find a green and cost-efficient means of synthesizing graphene with natural agricultural wastes like, Rice Husk Ash (RHA). The authors state that RHA was useful in producing smooth-edge, few-layered graphene with various characterization techniques in Materiale Plastice, 58(3). Moreover, the researchers in their article in Materials Research Express have proved the tribological ability of RHA-based graphene in self-lubricating the aluminium nanocomposites. However, chemical activation is still required to produce RHA-based graphene. Additionally, being a natural source, the availability of RHA in adequate amounts to enhance the nanocomposite stability for a specific application needs consideration. Hence, the upcoming researchers should find similar innovative, chemical-free, cost-effective and abundantly-available green-based simple graphene nanocomposites to aid various nanotechnology-based applications. Image courtesy: www.vecteezy.com
Machine Learning Approaches For Gestational Diabetes Prediction
Obesity, unhealthy diet-style and hereditary factors have massively increased the number of diabetic victims across the globe, affecting individuals of any age. Pregnant women too are not exempted from this illness, owning the name as “Gestational Diabetes Mellitus (GDM)”. This hyperglycemic disorder needs special attention in its diagnosis or its treatment, as it affects the lives of two- a mother and the fetus in her womb. In fact, GDM is the reason behind the onset of type-II diabetes in the mother, neonatal hypoglycemia affecting the growing fetus and pre-eclampsia witnessing fatal seizures during delivery. Generally, the traditional GDM screening is done halfway through pregnancy. Yet, its diagnosis at an earlier stage is more vital to suggest dietary or lifestyle changes and to prevent any future pregnancy-related complications. So, this is where the Machine Learning (ML) approaches could become most valuable. The glucose/insulin levels tested during every month of pregnancy, the mother’s age, the fetal growth measurements and few other pregnancy-related observations are just enough for an ML approach to diagnose the gestational diabetes in less time, even for a large number of patients. Knowing the importance of earlier diagnosis, this ML research domain has attracted so many researchers recently. Uthaya Kumar. J, Saritha P.S, Reshma P and Dr. Ramasamy. S too have comparatively analyzed the efficacy of ML approaches, such as Naive Bayes Models, Random Forest and Logistic Regression in predicting the gestational diabetes. As per their research in The Bioscan, 19(2), the authors have utilized the Healthcare Diabetes Dataset to make the predictions. Though their research aided in diagnosing GDM, especially in rural areas devoid of proper medical facilities, there are also challenges associated with it. Notably, high data quality is required to train the ML algorithms, which is not always acquirable due to incompleteness of health records from different clinics. Secondly, the algorithmic bias and the interpretability of results needs to be carefully assessed to prevent any mispredictions and mortality of the mother or unborn baby. Hence, future researchers should crack these challenges with proper data collection and novel Artificial Intelligence (AI) or ML approaches, which are already revamping the real-time healthcare systems. \
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Towards Smart Investment Decisions Based on Behavioral Bias
Investment choices have an immense impact on the trading profits. It is a known fact that the rational decisions made after reviewing the market trends, the associated risks or returns and the personalized financial goals ensure steady growth and capital protection. However, behavioral biases connected with emotional psychology, like overconfidence bias, representativeness bias and herding bias too influence the investment choices, productively at occasional instants and adversely most often. This could be understood by the research on behavioral bias-dependent investment decisions by Anurag Shukla, Manish Dadhich, Dipesh Vaya and Anuj Goel. These researchers from Sir Padampat Singhania University, Udaipur, Rajasthan, India, eventually emphasize the need to incorporate big data analytics and machine learning approaches for preventing the huge loss incur from behavioral biases. This can be taken as an initiative by the fintech engineers to develop a trading portal, which could give precise marketing trends or a profit/loss prediction based on massive data patterns and human-like intelligence. Not only that, imagine a smart trading portal that denies selling or buying of a stock with a message “You are emotionally-biased”. This is certainly possible with sophisticated Human-Machine Interfaces and Artificial Intelligence (AI). So, researchers have a great way ahead towards designing smarter, user-friendly, green and secure fintech trading systems, which have the potential to productively shape the global financial future.
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Eco-friendly and Strength-Enhanced Geopolymer Concrete Manufacture with Waste Glass Powder
Geopolymer concrete is gradually replacing Portland Cement, which the construction works has employed for so many years. There are reasons to support this context and they are: (i) Geopolymer concrete is an inorganic cementless binder made by reacting aluminosilicate sources in alkalizing liquids, while Portland Cement involves high heating and depletion of non-renewable earthly resources, like limestone, clay and other related minerals; (ii) Carbon emissions and ecosystem damage from Geopolymer concrete manufacture is quite smaller than those achieved from Portland Cement production and (iii) Geopolymer concrete provides better physical and mechanical properties, on comparing the hydration-based hardening Portland Cement. However, the regionally-distributed and limited availability of aluminosilicate sources is a shortcoming of Geopolymer concrete production. Hence, researchers are focusing to find aluminosilicate-abundant sources, without disturbing the ecosystem. Waste Glass Powder (WGP) is a material of that kind being produced from waste glass objects, which remain as slowly-recyclable and hardly-biodegradable major pollutant, heaped up in almost all landfills!!! The main characteristic of WGP that is attractive to geopolymer concrete manufacture is its high silica content and substantial calcium proportions with reduced carbon emissions. However, the brittle-natured WGP needs investigation on its workability, flexural strength, compressive strength and Split-Tensile strength, when used alone or in combination with other aluminosilicate materials to ensure long-lasting infrastructures. P. Manikandan and V. Vasugi in Silicon, vol. 13, Springer have performed a detailed investigation on the use of WGP as an aluminosilicate material. “WGP could be utilized as an innovative and promising eco-friendly aluminosilicate source material to manufacture geopolymer concrete”, the researchers say. However, an optimal proportion of WGP can only render high strength concrete, triggering further research. Moreover, as the increasing population and natural hazards demand inexpensive, secure and sturdy buildings, the responsibility to convert even an eco-threatening trash into environment-friendly, high-strength concrete substitute lies in the hands of young civil or structural engineers.
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Disclaimer
The discussions in 'Trending Research' are purely based on the already-published articles received from the respective authors for the easy portrayal of trending research themes to young researchers
This magazine does not accept or publish any kind of novel research and it does not create copyright infringements to the discussed journal articles
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