8 research outputs found
Analysis of Carrier Frequency Offset Distribution on Efficiency of Multicarrier Spread Spectrum Techniques
No full text
Analysis of Frequency Offset, its Distribution and its Impact on Multicarrier Spread Spectrum Techniques
No full textArtificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives
No full textUsing artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies’ design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions for future research and development. This paper examines some of the recent advances of AI for CAR-based therapies, for example, using deep learning (DL) to design CARs that target multiple antigens and avoid antigen escape; using natural language processing to extract relevant information from clinical reports and literature; using computer vision to analyze the morphology and phenotype of CAR cells; using reinforcement learning to optimize the dose and schedule of CAR infusion; and using AI to predict the efficacy and toxicity of CAR-based therapies. These applications demonstrate the potential of AI to improve the quality and efficiency of CAR-based therapies and to provide personalized and precise treatments for cancer patients. However, there are also some challenges and limitations of using AI for CAR-based therapies, for example, the lack of high-quality and standardized data; the need for validation and verification of AI models; the risk of bias and error in AI outputs; the ethical, legal, and social issues of using AI for health care; and the possible impact of AI on the human role and responsibility in cancer immunotherapy. It is important to establish a multidisciplinary collaboration among researchers, clinicians, regulators, and patients to address these challenges and to ensure the safe and responsible use of AI for CAR-based therapies
MD-EGAN: Evolutionary GAN with dynamic latent sampling and relative adaptive discriminator for improved performance
No full textTraining instability, mode collapse, vanishing gradients, and high computational cost are significant challenges in generative adversarial networks (GANs), particularly in evolutionary-based GANs. To address these issues, we propose the multi-distribution evolutionary GAN (MD-EGAN), a method aimed at improving training stability, enhancing sample diversity, and accelerating convergence. MD-EGAN leverages multiple latent space priors—including Gaussian, Uniform, Poisson, and Truncated Gaussian distributions paired with a relative adaptive discriminator (RAD) that provides dynamic and comparative feedback. By exploring diverse latent distributions and RAD feedback, MD-EGAN enables more robust population-based generator evolution. Experimental evaluations on the CIFAR-10 and STL-10 demonstrate that MD-EGAN outperforms several baseline GANs models in both image quality and diversity. Specifically, MD-EGAN achieves inception score (IS) = 8.92, and Fréchet inception distance (FID) = 10.08 on CIFAR-10 and IS = 10.31, and FID = 21.93 on STL-10. Meanwhile, MD-EGAN reduces convergence time by up to 43.16 % when compared with cooperative dual evolutionary GAN, demonstrating significant improvement in computational efficiency. These results validate the effectiveness of multi-distribution latent modeling and relative feedback in addressing key limitations of GAN training, leading to improved generative performance
Author Correction: Phytochemical screening, HPLC analysis, antimicrobial and antioxidant effect of Euphorbia parviflora L. (Euphorbiaceae Juss.)
No full textStudies on the magnetic, magnetostrictive and electrical properties of sol–gel synthesized Zn doped nickel ferrite
No full textStudies on the magnetic, magnetostrictive and electrical properties of sol-gel synthesized Zn doped nickel ferrite
M. Atifa, Corresponding Author Contact Information, E-mail The Corresponding Author, M. Nadeemb, R. Grössingera, R. Sato Turtellia
a Institut für Festkörperphysik, Technische Universität Wien, Wiedner Hauptstrasse 8-10, A-1040 Vienna, Austria
b EMMG, Physics Division, PINSTECH, P.O. Nilore, Islamabad, Pakistan
Received 28 December 2010; revised 24 February 2011; Accepted 27 February 2011. Available online 5 March 2011.
Abstract
Zinc doped nickel ferrite i.e., Ni1−xZnxFe2O4 (0 ≤ x ≤ 0.6) have been prepared by using sol-gel method. X-ray diffraction of these samples shows the presence of single-phase cubic spinel structure. The room temperature magnetic measurements showed that saturation magnetization (Ms) increases with the substitution of Zn2+ ions up to x = 0.4 and thereafter it begins to decrease, whereas magnetostriction (λ) value decreases with the addition of Zn2+ in the Ni-Zn ferrite. Dielectric permittivity (ɛ′), dielectric loss tangent (tan δ) and AC conductivity (σAC) for all the prepared samples have been studied as a function of frequency and composition in the range from 0.05 Hz to 10 MHz at room temperature. It has been observed that initially ɛ′, tan δ and σAC decreases with the substitution of Zn2+ up to x = 0.4 and then increases with the further addition of Zn2+ ions. Variation in the slope parameter s with zinc contents indicates the presence of different type of conduction mechanism in different compositions. The dielectric loss curves exhibit relaxation peaks which shift with the addition of Zn contents. The results have been explained on the basis of space charge polarization according to Maxwell-Wagner's two-layer model and the hopping of charges between Fe2+ and Fe3+ as well as between Ni3+ and Ni2+ ions at the octahedral sites
