Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Recent trends and advancements in microbial tannase-catalyzed biotransformation of tannins: a review.

Abstract: The outburst of green biotechnology has facilitated a substantial upsurge in the usage of enzymes in a plethora of industrial bioconversion processes. The tremendous biocatalytic potential of industrial enzymes provides an upper edge over chemical technologies in terms of safety, reusability, and better process control. Tannase is one such enzyme loaded with huge potential for bioconversion of hydrolysable tannins to gallic acid. Tannins invariably occur in pteridophytes, gymnosperms, and angiosperms and predominately cumulate in plant parts like fruits, bark, roots, and leaves. Furthermore, toxic tannery effluents from various tanneries are loaded with significant levels of tannins in the form of tannic acid. Tannase can be principally employed for debasing the tannins that predominately occur in the toxic tannery effluents thus providing a relatively much cheaper measure for their biodegradation. Over the years, microbial tannase-catalyzed tannin degradation has gained momentum. The plentious availability of tannin-containing agro- and industrial waste paves a way for efficient utilization of microbial tannase for tannin degradation eventually resulting into gallic acid production. Gallic acid has received a great deal of attention as a molecule of enormous therapeutic and indusrial potential. The current worldwide demand of gallic acid is 8000 t per annum. As a matter of fact, bioconversion of tannins into gallic acid through fermentation has not been exploited completely. This necessitates further studies for development of more efficient, economical, productive processes and improved strains for gallic acid production so as to meet its current demand.

Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms

Abstract: The fog computing models are getting popular as the demand and capacity of data processing is rising for the various applications every year. The fog computing models incorporate the various task scheduling algorithms for the resource selection among the given list of virtual machines (VMs). The task scheduling models are designed around the various task metrics, which include the task length (time), energy, processing cost etc. for the various purposes. The cost oriented scheduling models are primarily built for the customer's perspectives, and saves them a handful amount of money by efficiently assigning the resources for the tasks. In this paper, we have worked upon the multiple task scheduling models based upon the Local Regression (LR), Inter Quartile Range (IQR), Local Regression Robust (LRR), Non-Power Aware (NPA), Median Absolute Deviation (MAD), Dynamic Voltage and Frequency Scheduling (DVFS) and The Static Threshold (THR) methods using the ifogsim simulation designed with the 50 nodes and 50 virtual machines, i.e. 1 virtual machine per node. All of the models have been implemented using the standard input simulation parameters for the purpose of performance assessment in the various domains, specifically in the time domain and effective consumption of energy. The results obtained from the experiments have shown the overall time of 86,400 seconds during the simulation, where the DVFS has been recorded with the 52.98 kWh consumption of energy, which shows the efficient processing in comparison to the 150.68 kWh of energy consumption in the NPA model. Also, there are no SLA violations recorded during both of the simulation, because no VM migration model has been utilized among both of the implemented models, which clearly shows that the VM migrations are the major cause of SLA violation cases. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs).

Post-processing of ABS Replicas with Vapour Smoothing for Investment Casting Applications

Abstract: The need of customized products with tight dimensional tolerances, lower production cost and shorter lead times led to the development of additive manufacturing techniques like fused deposition modelling (FDM). The digitally fabricated ABS patterns prepared on FDM needs to be processed by vapour smoothing (VS) in order to reduce the surface roughness. The post-processing of FDM-based patterns/replicas with VS increases their density, which increases heat input and complexities in ash removal during burnout stage from ceramic shell in investment casting (IC). This study highlights the step-by-step procedure for controlling the density of master patterns/replicas after processing with VS for IC applications.

Evaluation of physicochemical characteristics of Mahua (Madhuca indica) and Jatropha (Jatropha curcas) dual biodiesel blends with diesel

Abstract: In the present research, biodiesels were prepared from Mahua and Jatropha oil by two-step transesterification. The physicochemical characteristics (viz. density, acid value, iodine value, kinematic viscosity, cloud-pour point, fire-flash point) of prepared biodiesels were evaluated. Finally, the prepared biodiesels were blended with mineral diesel in different proportion and physicochemical characteristics of blended dual biofuel sample blends were also evaluated in order to determine the best blend. The results revealed that the dual biofuel samples met the requirements of EN-14214 (Europe standards), ASTM D-6751 (USA standards) and IS-15607 (India standards). A set of regression equations were also formulated, this mathematical model revealed that different physicochemical properties had high values of the regression coefficient (R2) up to 0.986 for various proportions of blend samples. The obtained results have shown that the pre-treatment process can effectively reduce the free fatty acid value to less than 1% for biodiesels. Further, the values of density and kinematic viscosity were found to be better for sample blends B10, B20, and B30, while B40 was on the upper-end limit and B50 was found to be out of limit range. Furthermore, it was concluded that the sample blends B30 (with 15% blending of each biodiesel) and B40 (with 20% blending of each biodiesel) had better properties and can be used further to test their fuel combustion characteristics on a diesel engine.