The provided research paper offers a thorough analysis of the semiotic analysis present in tobacco-free initiative advertisements from the year 2021. The study delves into the intricate process of decoding the diverse signs, symbols, and visual components integrated into these anti-smoking campaigns. The core aim of this investigation is to comprehend and explore the semiotic tactics that underlie these advertisements, with a particular emphasis on visual communication as a pivotal tool in shaping the public's attitudes and behaviors towards tobacco usage. The research introduces a significant theoretical framework, the "Taxonomy of Image-Text Relations and Functions" theory, as proposed by Emily E. Marsh and Marilyn Dom

BN RASHİD, 2022

patterns of utterance stress in discourse direct attention to specific themes and reactions, controlling the flow and coherence of conversation. this study examines the utterance stress in Steve Harvey's selected episodes from a phono-stylistic perspective. this study is hoped to improve understanding of linguistic mechanism in talk show communication, highlighting the importance of phonetic features in transmitting meaning and increasing broadcast conversation participation. the researcher concentrates on the types of focus functions of utterance stress of some episodes available on YouTube. to conduct the analysis, the researcher adopts (Carr, 2013; Davenport& Hannahs 2005) to analyze utterance stress and Leech and Short (2007

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina

The researchers wanted to make a new azo imidazole as a follow-up to their previous work. The ligand 4-[(2-Amino-4-phenylazo)-methyl]-cyclohexane carboxylic acid as a derivative of trans-4-(aminomethyl) cyclohexane carboxylic acid diazonium salt, and synthesis a series of its chelate complexes with metalions, characterized these compounds using a variety technique, including elemental analysis, FTIR, LC-Mass, ¹H-NMRand UV-Vis spectral process as well TGA, conductivity and magnetic quantifications. Analytical data showed that the Co (II) complex out to 1:1 metal-ligand ratio with square planner and tetrahedral geometry, respectively while 1:2 metal-ligand ratio in the Cu(II), Cr(III), Mn(II), Zn(II), Ru(III)and Rh(III)complexes

The Dirichlet process is an important fundamental object in nonparametric Bayesian modelling, applied to a wide range of problems in machine learning, statistics, and bioinformatics, among other fields. This flexible stochastic process models rich data structures with unknown or evolving number of clusters. It is a valuable tool for encoding the true complexity of real-world data in computer models. Our results show that the Dirichlet process improves, both in distribution density and in signal-to-noise ratio, with larger sample size; achieves slow decay rate to its base distribution; has improved convergence and stability; and thrives with a Gaussian base distribution, which is much better than the Gamma distribution. The performance depen