National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Corrosion & Cloud computing. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

Kinetics of Esterification of Acetic Acid with Methanol in the Presence of Ion Exchange Resin Catalysts

Abstract: Esterification kinetics of acetic acid with methanol was studied with solid acid catalyst in an isothermal batch reactor at 333–353 K. Different types of ion exchange catalyst (Indion 130, Indion 190, and Amberlyst 15 wet) were used for the esterification of acetic acid. It was found that Indion 130 was an effective catalyst for acetic acid esterification. The effects of stirrer speed, reaction temperature, initial reactant concentration, and catalyst loading on reaction rate were investigated and optimized. Temperature dependence of the reaction rates and activation energies was determined by an Arrhenius plot. A complete kinetic equation for describing the reaction catalyzed by Indion 130 was developed. This equation can be used in the simulation and design of the catalytic distillation column for the synthesis of methyl acetate.

Removal of metal ions and humic acids through polyetherimide membrane with grafted bentonite clay

Abstract: Functional surfaces and polymers with branched structures have a major impact on physicochemical properties and performance of membrane materials. With the aim of greener approach for enhancement of permeation, fouling resistance and detrimental heavy metal ion rejection capacity of polyetherimide membrane, novel grafting of poly (4-styrenesulfonate) brushes on low cost, natural bentonite was carried out via distillation-precipitation polymerisation method and employed as a performance modifier. It has been demonstrated that, modified bentonite clay exhibited significant improvement in the hydrophilicity, porosity, and water uptake capacity with 3 wt. % of additive dosage. SEM and AFM analysis showed the increase in macrovoides and surface roughness with increased additive concentration. Moreover, the inclusion of modified bentonite displayed an increase in permeation rate and high anti-irreversible fouling properties with reversible fouling ratio of 75.6%. The humic acid rejection study revealed that, PEM-3 membrane having rejection efficiency up to 87.6% and foulants can be easily removed by simple hydraulic cleaning. Further, nanocomposite membranes can be significantly employed for the removal of hazardous heavy metal ions with a rejection rate of 80% and its tentative mechanism was discussed. Conspicuously, bentonite clay-bearing poly (4-styrenesulfonate) brushes are having a synergistic effect on physicochemical properties of nanocomposite membrane to enhance the performance in real field applications.

Effect of electrode material in wire electro discharge machining characteristics of Ti50Ni50−xCux shape memory alloy

Abstract: TiNiCu alloy belongs to new class of shape memory alloy (SMA), which exhibits superior properties like shape memory effect, super elasticity and reversible martensitic transformation phase and thus find broad applications in actuators, micro tools and stents in biomedical components. Even though, SMA demonstrates outstanding property profile, traditional machining of SMAs is fairly complex and hence non-traditional machining like wire electric discharge machining (WEDM) has been performed. Hence, there is a need to investigate the WEDM performance characteristics of shape memory alloys due to excellent property profile and potential applications. In the present investigation, various machining characteristics like material removal rate (MRR), surface roughness, surface topography and metallographic changes have been studied and the influence of wire material on TiNiCu alloy machining characteristics has also been evaluated through ANOVA. Ti 50 Ni 50− x Cu x =10, 20 was prepared by vacuum arc melting process. The proposed alloy as-cast material exhibits austenite property (B2 phase) and having higher hardness when compared to TiNi alloy. The investigation on WEDM of Ti 50 Ni 50− x Cu x alloy reveals that the machining parameters such as servo voltage, pulse on time and pulse off time are the most significant parameters affecting MRR as well as surface roughness using both brass and zinc coated brass wires. However, machining with zinc coated brass wire yields reduced surface roughness and better MRR and also produces less surface defects on the machined surface of Ti 50 Ni 50− x Cu x alloys.

Applicability of machine learning in spam and phishing email filtering: review and approaches

Abstract: With the influx of technological advancements and the increased simplicity in communication, especially through emails, the upsurge in the volume of unsolicited bulk emails (UBEs) has become a severe threat to global security and economy. Spam emails not only waste users’ time, but also consume a lot of network bandwidth, and may also include malware as executable files. Alternatively, phishing emails falsely claim users’ personal information to facilitate identity theft and are comparatively more dangerous. Thus, there is an intrinsic need for the development of more robust and dependable UBE filters that facilitate automatic detection of such emails. There are several countermeasures to spam and phishing, including blacklisting and content-based filtering. However, in addition to content-based features, behavior-based features are well-suited in the detection of UBEs. Machine learning models are being extensively used by leading internet service providers like Yahoo, Gmail, and Outlook, to filter and classify UBEs successfully. There are far too many options to consider, owing to the need to facilitate UBE detection and the recent advances in this domain. In this paper, we aim at elucidating on the way of extracting email content and behavior-based features, what features are appropriate in the detection of UBEs, and the selection of the most discriminating feature set. Furthermore, to accurately handle the menace of UBEs, we facilitate an exhaustive comparative study using several state-of-the-art machine learning algorithms. Our proposed models resulted in an overall accuracy of 99% in the classification of UBEs. The text is accompanied by snippets of Python code, to enable the reader to implement the approaches elucidated in this paper.