Dr. A.P.J. Abdul Kalam Technical University (AKTU), before 2015 as the Uttar Pradesh Technical University (UPTU), is a state government run affiliating university in Lucknow, Uttar Pradesh, India. It was established as the Uttar Pradesh Technical University through the Government of Uttar Pradesh on 8 May 2000. To reduce workload and to ensure proper management, the university was bifurcated into separate universities, Gautam Buddh Technical University (GBTU) and Mahamaya Technical University (MTU), with effect from 1 May 2010. In 2013, as a new government came into power, the university was formed again by combining the two on 5 January 2013.It is an affiliating university, with approximately 800 colleges affiliated to it. The university was earlier on the IET Lucknow campus. Now it is in its newly inaugurated campus in Jankipuram, Lucknow. Additionally, the university had a Centre and Regional Office in Noida, Uttar Pradesh.Dr. A.P.J. A.P.J. A.P.J..
Undoped, 0.1%, 0.3%, 1%, and 3% Tb-doped Ca2Al2SiO7 (CAS) single crystals were prepared by the floating zone technique, and their photoluminescence (PL) and thermoluminescence (TL) properties were investigated. Several PL and TL emission peaks were observed in all the Tb-doped CAS, which would be attributed to 4f-4f transitions of Tb3+. The undoped CAS showed the glow peak centered at around 200-300 degrees C, and the glow peaks were seen centered at around 80-150 degrees C and 250-350 degrees C in the Tb-doped CAS. In the TL dose response properties, the 0.3% Tb-doped CAS exhibited higher TL intensity than the other Tb-doped CAS and a commercial TL dosimetric material of Tb-doped Mg2SiO4. In addition, when using 3 sigma method, the 0.3% Tb-doped CAS and the Tb-doped Mg2SiO4 reference sample exhibited the lower detection limit of 0.11 mu Gy and 0.17 mu Gy, and the 0.3% Tbdoped CAS had a better lower detection limit than Tb-doped Mg2SiO4.
The Tb: 24BaO-4Y2O3-72B2O3 (BYB) glasses containing 0.1-10 mol% Tb were successfully prepared by the melt-quenching technique. The photoluminescence (PL) and thermally stimulated luminescence (TSL) properties were systematically investigated. In the PL and TSL spectra, Tb: BYB glasses exhibited emission bands attributable to the 4f-4f transitions of Tb3+ ions. The lowest detectable dose, estimated from the TSL dose response function of the 5 mol% Tb: BYB glass, was 0.1 mGy. Furthermore, the 5 mol% Tb: BYB glass achieved a spatial resolution of 8.00 LP/mm under X-ray irradiation at a dose of 3 Gy.
This study investigates the electrochemical corrosion behavior of X80 pipeline steel immersed in soil leachates from Hami and Yingtan regions. The corrosion behavior of X80 steel in alkaline (Hami) and acidic (Yingtan) soil leachates was evaluated by electrochemical tests (Polarization and WBE), surface analysis (SEM/EDS, XRD) and SKP mapping. Results indicate Hami soil’s alkaline leachate promotes protective films and iron oxide/hydroxide formation, while Yingtan soil’s acidic leachate induces hydrogen evolution corrosion. Hami-exposed samples show substantial corrosion product accumulation in 7 days. The heat-affected zone (HAZ) initially exhibits the highest corrosion tendency, decreasing with time, contrasting with accelerated corrosion in the base metal (BM) and intermediate response in weld metal (WM). This study offers insights into X80 pipeline steel’s corrosion mechanisms in different soils, emphasizing local soil conditions’ crucial role in corrosion behavior.
This study aims to translate recent advancements in hybrid artificial intelligence (AI) modeling into a functional tool for assessing individual financial well-being. The objective is to develop a system that aids organizations in understanding employees’ financial stress, with broader implications for enhancing workplace productivity and societal economic resilience. A deep learning pipeline was developed to classify individuals into three financial well-being categories: Financially Secure, Moderately Stable, and Financially At-Risk. The approach utilizes a structured dataset of 20,000 Indian individuals and implements 15 advanced deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Wide Deep networks. Model performance was assessed using standard evaluation metrics, including validation accuracy and ROC-AUC scores. Among the tested models, the hybrid Wide Deep + CNN configuration yielded the highest performance, achieving a validation accuracy of 99.44
With the rapid adoption of smart home solutions and related technologies, edge computing has emerged as a key enabler by offering low-latency data processing, increased efficiency and improved scalability. However, this integration in IoT systems introduces complex security challenges in smart home edge environments, increasingly susceptible to cyber threats such as denial-of-service (DoS), malware injection, passive surveillance, and unauthorized access. This paper investigates intelligent intrusion detection and attack classification strategies specifically designed for smart home edge systems. Using the comprehensive ML-EdgeIIoT dataset, this study designs and evaluates a machine learning-based intrusion detection framework for multiclass classification of eight categories of IoT network attacks, namely Backdoor, MITM, DDoS, Ransomware, Password Attack, SQL Injection, Prob-attacks, and Normal traffic while minimizing false positives and false negatives. The framework incorporates data cleaning, correlation- and feature importance-based feature selection, hyperparameter optimization using gridsearchCV, model training, and ensemble learning. A set of machine learning models comprising Artificial Neural Network, Balanced Random Forest, K-Nearest Neighbours, Random Forest, and Logistic Regression was implemented and comparatively evaluated. Two ensemble techniques were subsequently developed using the three best-performing classifiers: (1) a stacking ensemble with Logistic Regression as the meta-learner and (2) a Top-3 majority voting ensemble. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC. Robustness and generalization of the individual machine learning models were assessed through stratified 10-fold cross-validation for the three best-performing classifiers. The Top-3 voting ensemble subsequently achieved the highest performance on the independent test set, with accuracy of 99.24%, average precision of 98.75%, recall of 99.00%, and an F1-score of 99.00% for all attack classes, while reducing misclassification compared with individual classifiers. The findings of this study significantly enhance the understanding of smart home edge computing security, which will pave the way for more robust and intelligent threat detection frameworks.
