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..
Abstract Cognitive radio wireless sensor networks (CR-WSNs) are particularly susceptible to routing vulnerabilities arising from dynamic-spectrum availability and sophisticated adversarial attacks, emphasizing the need for secure and efficient routing mechanisms. Current solutions address routing optimization, spectrum management, and security as independent tasks, leading to suboptimal performance and susceptibility to Byzantine jamming, spectrum sensing data falsification (SSDF), and primary user emulation attacks (PUEA). This article introduces DRL-SecRoute (deep reinforcement learning-based secure routing), a new unified secure routing framework that synergistically integrates deep reinforcement learning with adaptive spectrum sensing to address the multi-dimensional optimization problem of secure routing in dynamic CR-WSN environments. The key contributions are fourfold: (1) a twin-delayed deep deterministic policy gradient (TD3) algorithm enhanced with prioritized experience replay (PER), specifically designed for continuous state-action spaces in CR-WSNs, achieving 40
COVID-19 all over the world has given an option to employees to work from home. As a result, the number of computer users has increased drastically. According to international market tracker Data Corporation, in 2020, the sales of computer devices exceeded 302 million. The survey conducted on computer users indicates that there was increase in neck pain and back pain. The increase in musculoskeletal disorder is mainly due to bad ergonomic design of computer workstation. The present work is focused on design of computer user chair based on Indian anthropometric standard data. Three different kinds of chair have been modeled in CATIA V5. The bio-mechanical analysis and rapid upper limb assessment analysis were carried out. The structural analyses of chairs have been carried out in ANSYS. The results showed that the chairs were structurally strong for static condition.
(Pr3+, Er3+)-codoped Li2O-ZnO-B2O3 (LZBPrEr) glasses were fabricated via melt quenching and examined for their structural and spectroscopic characteristics. XRD analysis confirmed an amorphous structure of the glasses. Physical property measurements revealed that higher Er3+ concentrations increased molar volume while reducing density, indicating a more open glass network. Raman spectroscopy showed reduced total scattering strength with increasing Er3+, accompanied by systematic shifts in B-O-B (BO4) and BO3 vibrational bands, reflecting structural rearrangements in the borate network. Optical absorption spectra identified characteristic Er3+ and Pr3+ transitions, with overlapping bands enabling energy transfer (ET) between the ions. The indirect band gap decreased from 3.14 eV to 3.08 eV with rising Er3+ content, while Urbach energy dropped from 0.28 eV to 0.24 eV, indicating reduced structural disorder. Under 444 nm excitation, Pr3+ emissions at 602 nm and 643 nm were observed, with the dominant 602 nm peak intensifying at higher Er3+ levels due to ET from Pr3+ -> Er3+. Excitation at 980 nm produced strong Er3+ near-infrared emission at similar to 1500 nm, with LZBPr(0.1)Er(0.5) yielding the highest intensity, suggesting an optimal dopant concentration. For this optimal composition, the absorption and emission cross-sections at similar to 1515 nm and similar to 1535 nm were 2.08 x 10(-20) cm(2) and 2.48 x 10(-20) cm(2), respectively, with a broad 99 nm FWHM. Gain analysis indicated that positive gain is achievable near 1535 nm under high population inversion, highlighting potential for laser applications. Fluorescence decay followed single-exponential behaviour, with lifetimes increasing from 4.19 mu s to 4.70 mu s as Er3+ content rose. These results demonstrate that (Pr3+, Er3+)-codoped LZB glasses offer dual visible and NIR emissions, making them promising candidates for integrated photonic systems, including free-space optical communication, where visible alignment and NIR data transfer can be achieved within a single material.
This study reports the successful green synthesis of copper nanoparticles (CuNPs) using Canna indica flower extract (CIFE) for the first time. The CIFE is rich in phytochemicals with significant levels of polyphenols and flavonoids (TPC: 193.57 +/- 7.26 mu g GAE/mL; TFC: 117.13 +/- 5.91 mu g CE/mL), effectively serving both as a reducing and stabilizing agent. UV-Visible spectral studies confirmed nanoparticle formation, evidenced by a distinct surface plasmon resonance (SPR) peak at around 540 nm. Fourier Transform Infrared (FTIR) analysis revealed that functional groups, such as hydroxyl and carboxyl, played a crucial role in the reduction and capping processes. Transmission electron microscope (TEM) analysis revealed spherical CuNPs with an average diameter of around 25.6 nm. XPS analysis confirmed the presence of a Cu@Cu2O core-shell structure due to the presence of Cu0 and Cu+ mixed valences. FCC-crystalline structure. The CuNPs exhibited impressive antimicrobial activity, exhibiting significant zones of inhibition against Escherichia coli, Staphylococcus aureus, and Aspergillus flavus. Moreover, they demonstrated low cytotoxicity toward MCF7 and HeLa cancer cell lines, with IC50 values of 142.6 and 162.32 mu g/mL, respectively. Photoluminescence studies highlighted emissions at 441, 487, and 544 nm, indicating surface oxidation and partial semiconductor-like behavior. Furthermore, Density Functional Theory (DFT) calculations confirmed the metallic nature of CuNPs, characterized by a pronounced density of states at the Fermi level and the absence of a bandgap in the electronic band structure. These findings firmly establish CIFE-synthesized CuNPs as potential candidates for applications in antimicrobial treatment and nanomedicine.
Gastrointestinal diseases have been on the rise, increasing the mortality rate at a high pace if they remain untreated at their earlier stages. Gastrointestinal diseases range from ulcerative colitis, polyps, esophagitis and ulcers, which are diagnosed by using WCE. Identification and diagnosis by manual supervision are tedious and prone to error, leading to the development of automated procedures. To perform accurate data pre-processing and feature extraction, which play a key role in detection, is crucial. However, existing works focusing on GI tract diseases suffer from problems such as high computation time and low accuracy. In order to tackle these problems, a new gastrointestinal tract disease detection and classification using Taylor spotted hyena optimization algorithm with deep maxout networks (GTSHO-DMN) has been proposed. The proposed work involves different processes, such as data pre-processing and log scaling for pattern transformation. ReliefF, being the feature extraction technique, is employed on the pre-processed data sent to the deep maxout network trained by the Taylor spotted hyena optimization algorithm in combination with gradient descent optimization. The advantages of the proposed optimization are that it offers a better exploration-exploitation tradeoff, adaptive search strategy, faster convergence, and robustness compared to other existing approaches. The proposed model performs better when evaluated in terms of various performance metrics such as precision, accuracy, recall and F-score. The proposed method has been compared with traditional approaches with a high accuracy rate of 98.36 %.
