Anil Neerukonda Institute of Technology and Sciences (ANITS), was established in the Academic Year 2001–02 with the approval of the All India Council for Technology Education (AICTE), New Delhi and the Government of Andhra Pradesh and is affiliated to Andhra University (AU), Visakhapatnam. All the eligible courses are Accredited by NBA in the year 2013. It is given Permanent Affiliation in the year 2010 by Andhra University. ANITS received autonomous status in the year 2015.The Institute is accredited by NAAC with A grade and valid up to 9 Dec 2019 with A grade. The institute CGPA is 3.01..
The high-pressure torsion (HPT) process is one of the most powerful methods of severe plastic deformation, capable of significantly refining the microstructure and altering the functional properties of high-strength aluminum alloys. In this work, the effects of HPT on the microstructure, residual stresses, hardness, and damping characteristics of the AA7075 alloy were comprehensively studied. Microstructural investigation revealed that the average grain size of the starting material was around 95 µm, but after HPT, it decreased to 6.1 µm, indicating continuous dynamic recrystallization. The secondary-phase particles were fragmented and uniformly distributed, as observed by SEM. The lattice strain was evident from the broadening of the XRD peaks. High compressive residual stresses were found near the surface ( − 600 MPa), whereas at greater depths the stresses were tensile due to strain gradients. Microhardness rose about 35–40
An organic single crystal of 2-aminobenzylaminium picrate (ABAP) was grown by the slow-evaporation solution method and structurally characterized to investigate proton-transfer-driven supramolecular organization and its influence on optical properties. Single-crystal X-ray diffraction confirms the formation of a charge-assisted hydrogen-bonded ionic framework composed of protonated 2-aminobenzylaminium cations and picrate anions. The crystal packing is stabilized by cyclic N–H•••O hydrogen-bond motifs and π•••π stacking interactions that promote cooperative donor–acceptor coupling within the lattice. Vibrational analysis shows good agreement between experimental and calculated spectra, supporting the optimized molecular geometry. UV–diffuse reflectance spectroscopy reveals an optical band gap of 2.80 eV, indicating semiconducting behaviour, while TD-DFT calculations confirm intramolecular charge-transfer transitions. Frontier molecular orbital analysis gives a HOMO–LUMO gap of 3.756 eV and demonstrates spatial separation of donor and acceptor regions. The results establish a clear correlation between proton-transfer-induced polarization, supramolecular organization and optical response, highlighting ABAP as a useful model system for structure–property relationship studies in hydrogen-bonded organic optoelectronic materials.
Owing to the outstanding material quality and improved resistance to heating effects, the ultra-wide-bandgap semiconductor of β-Ga2O3 is being intensely assessed for the future generation of vertical high electron mobility transistors (HEMT). However, for channel temperatures above 450 K, Al2O3/β-Ga2O3 still demonstrates considerable limitations owing to the self-heating and trapping effect, and to address this problem, various technological approaches are being extensively explored. In this work, we report the analytical self-heat spreading model for the structure of the stacked AIN/β-Ga2O3 HEMT. The self-heat spreading model was realized by the stacked AIN-beside-gate epilayer. The proposed device’s self-heating effect only became evident at VGS = 3 V while the self-heating was mitigated from −1 V to 2 V owing to the trap densities, which stood at 2 × 1011 over a range of 300–492 K. Direct-current (DC) and radiofrequency (RF) improved significantly. Transconductance was enhanced by 100 ∼ 1014, and the channel temperature dropped by 35
Kidney stone disease is a prevalent urological disorder that can result in severe pain, obstruction, and long-term complications if not detected and managed promptly. Traditional diagnostic approaches, particularly those relying on manual assessment of ultrasound images, often suffer from limitations such as subjective interpretation, dependency on radiologist expertise, and challenges in identifying small or complex stones. These constraints can lead to diagnostic delays and inconsistencies, especially in time-sensitive or resource-limited clinical settings. Therefore, the need for an intelligent, automated solution that enhances diagnostic accuracy and efficiency is more critical than ever. To address these issues, we propose a novel deep learning-based model called the Kronecker Self-Organizing Map Forward Harmonic Network (KSOMFHNet) for kidney stone classification using ultrasound imagery. The model begins with an image preprocessing phase, where a double bilateral filter is applied to effectively denoise the ultrasound images. Following this, the Deep Recursive Residual Network (DRRN) is employed to segment the kidney region accurately. Feature extraction is then performed using a combination of Binary Robust Independent Elementary Features (BRIEF), shape-based features, and Gray Level Co-Occurrence Matrix (GLCM) texture descriptors. These features are then used for classification via the KSOMFHNet, a hybrid architecture integrating the Deep Kronecker Neural Network (DKN) and Self-Organizing Map Network (SOMNet). This fusion enhances the model's learning capacity and spatial representation abilities. Experimental results demonstrate that KSOMFHNet achieves high performance, with an accuracy of 91.984%, a True Positive Rate (TPR) of 90.543%, a True Negative Rate (TNR) of 92.248%, a precision of 90.179%, and an F1-score of 90.360% for training data is 90%, highlighting its potential for clinical deployment.
Permanent magnet brushless DC motors (PMBLDCMs) are widely used in vehicles due to their high efficiency, better power density, and reliability. Precise motor parameter estimation is significant in improving the performance of the vehicle drive. This paper presents a comparative investigation of advanced parameter estimation techniques-namely Monte Carlo (MC), Markov Chain Monte Carlo (MCMC), Kalman Filter (KF), Extended Kalman Filter (EKF), Sequential Monte Carlo/Particle Filter (SMC/PF), and Unscented Kalman Filter (UKF) applied to a 250W PMBLDCM. With these advanced parametric estimation methods, PM-BLDC motors can be fine-tuned, resulting in more precise control over the motor's torque, speed, and position of the drive. This leads to the development of fault-tolerant systems that can detect and respond to motor abnormalities in real-time, even under dynamic operating states. The motor model includes key parameters such as stator resistance (2.875Q),stator inductance (8.5mH), inertia (0.8 & lowast;10(-3)kg.m(2)), viscous damping (1 & lowast;10(-3)N.m.s), flux linkage (0.175wb) and torque constant (0.8N.m/A) by considering electric vehicle (EV) uncertainty quantifications. By incorporating uncertainty quantification techniques, the proposed approach enhances the reliability and accuracy of motor control, which is crucial for optimizing dynamic performance. Key outcomes shows that the UKF achieves superior accuracy and stability across all estimated parameters, making it highly effective for real-time PMBLDCM control in EV applications.
