Ajay Binay Institute of Technology is an ISO 9000:2000 certified institution in Cuttack, Odisha, India, affiliated to the Biju Patnaik University of Technology, Rourkela. The institute was established in 1998 and is affiliated to AICTE. The campus is located within the city limits of cuttack and has a total student strength of over 2000. The main campus houses the Administrative block, Engineering, MBA and Architecture wings. The ITC wing is located in a second campus within a distance of 5 km from the main campus. The post graduate courses are conducted from the main campus.The Piloo Mody College of Architecture is Council of Architecture (COA) recognised and is also a registered NATA test centre. The department of architecture is currently in its 21st year since its inception back in 1993..
Ultrasonic methods can be used as a sensitive and non-destructive approach to investigate molecular interactions, structural features, and thermo-physical properties of complex multi-component liquid systems. In this study, a detailed comparative ultrasonic analysis has been conducted for three coating formulatons consisting of a constant 5% concentration of polyethylene glycol (PEG) as follows, (i) PEG and ethanol, (ii) PEG, ethanol, and epoxy, and (iii) PEG, ethanol, and epoxy reinforced with TiO2 nanoparticles. Ultrasonic properties such as ultrasonic velocity, acoustic impedance, adiabatic compressibility, and free length of intermolecular interaction have been determined at a frequency of 4 MHz using an ultrasonic interferometer at four temperatures (298, 308, 318, and 328) K. In this way, a clear interaction hierarchy has been observed: PEG-ethanol < PEG-ethanol-epoxy < PEG-ethanol-epoxy-TiO2. Acoustic insights strongly correlate with a wide range of coating performance indicators, which are adhesion, hardness, thermal resistance, and mechanical durability. The study concludes that 5% PEG-ethanol-epoxy-TiO2 systems indeed represent the most promising characteristics for advanced coating applications where stability, strength, and functional performance have high demands. Ultrasonic characterization thus proves to be an effective predictive tool for optimizing polymeric and nano-composite coating formulations. Temperature elevation weakened interactions in all systems; however, the TiO2-containing formulation preserved its superior structural stability even at higher temperatures, which showed its robustness.
This study employs ultrasonic techniques within the temperature range of 298-328 K to examine the molecular interactions and structural dynamics of a novel PEG-ethanol-epoxy-TiO2 nanocomposite system, which consists of 5 % (w/w) PEG-6000, 10 % (w/w) epoxy resin, and 2 % (w/w) TiO2 nanoparticles. Thermoacoustic parameters, including free volume, available volume, molar volume, internal pressure, Rao's constant, and Wada constant, were obtained through experimental measurements of ultrasonic velocity, viscosity, and density. The findings indicate that as temperature rises, there is a progressive increase in free volume, available volume, molar volume, Rao's constant, and Wada constant, while internal pressure decreases. This suggests an enhancement in molecular mobility and a reduction in cohesive forces within the composite matrix. The uniform distribution of TiO2 nanoparticles greatly enhances structural consistency and acoustic responsiveness, indicating robust interfacial interactions among PEG, ethanol, epoxy, and TiO2 via hydrogen bonding and dipole-dipole mechanisms. The findings confirm that ultrasonic analysis demonstrates a high sensitivity to molecular rearrangements and thermal behavior in polymer nanocomposites. The optimized PEG-epoxy-TiO2 formulation demonstrates significant potential for advanced coating applications, including thermally adaptable, UV-resistant, and acoustically responsive materials for biomedical and electronic devices.
Subject to rapid, ongoing depletion of fossil fuels, giving rise to energy crisis in the future power network, renewable power generations are playing very important role to make up the energy demand. There are several issues with renewable power integrations to the present energy sector. Out of these issues, a prime issue discussed in this paper is mitigation of electromechanical oscillations, which are raised by renewable penetrations. Out of different renewable sources, solar and wind sources have been taken account in this study. Different experimentations have been conducted in the study for single machine and 39 bus multimachine systems with sudden and random solar and wind sources. To mitigate the oscillations, UPFC-based parallel, lead leg control action has been implemented in the present work, where the parameters of the UPFC-based controller actions are tuned by a modern multi objective walrus optimization technique (WOT).The performance of WOT has been compared with PSO and GWO techniques, with time domain simulations, eigen value analysis along with system responses. The system responses predict that the proposed WOT tuned parallel UPFC controller can mitigate the electro mechanical oscillations resulting from random solar and wind penetrations effectively as compared to PSO and GWO technique. It has also been verified with eigen value distributions. As per the results of present work, the WOT tuned UPFC control action is efficient for implementation at the point of common coupling in the power system.
Oscillations in power system aggravations day by day with uncertainty in load variations and disturbances such as renewable penetrations. The present study proposes deployment of steam governors to supper oscillations in power system. The steam governor is equipped with fractional compensated lead lag action, where the gains are set by improved salp swarm algorithm (ISSA). The ITAE based objective function being considered with the remote signal speed deviation of generator taken as input for controller action. Sudden and discrete variations in reference voltage to generator, load and plug in electric vehicles (PEV) have been the source of low frequency oscillations and proposed steam governor being employed to suppress these oscillations. The tunning efficacy of ISSA has been contrasted with PSO and SSA techniques. The variations in speed have been analyzed for different disturbances with system eigen distributions and justification of proposed action has been established. Low frequency modelling of power system along with steam governor has been taken at an initial operating state. Time domain simulations have been carried out to analyses system behavior and it has been observed that proposed approach is much effective to oscillation suppression in power system.
Electroencephalography (EEG) based automated diagnosis of brain disorders plays is increasingly important in clinical decision support systems of the future. Conventional feature extraction techniques are confronted with a significant challenges due the non-stationary, and complex nature of EEG signal. A hybrid multi-scale CNNs integrated with RNNs as introduced in this study as a mixed solution to effectively extract a variety of temporal features from EEG signals. The parallel convolutional paths with different kernel sizes perform feature extraction at different temporal resolutions. Subsequently, these features are combined and given to a LSTM model to learn and understand the causal dependencies. Experimental results on the University of Bonn’s Epileptic Seizure Recognition dataset show training and validation accuracy metrics of 99.08% and 98.26%, respectively. The system's high sensitivity confirmed by correctly classifying 456 seizure events out of 465 demonstrates its capability to provide diagnostic accuracy, consequently enabling clinical practitioners to offer better care.
