BN RASHİD, 2022

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

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

    The present study is concerned with the writer's ideologies towards violence against women. The study focuses on analyzing violence against women in English novel to see the extent the writers are being affected and influenced by their genders. It also focuses on showing to what extent the writer's ideologies are reflected in their works. Gender influences social groups ideologies; therefore, when a writer discusses an issue that concerns the other gender, they will be either subjective or objective depending on the degree of influence, i.e., gender has influenced their thoughts as well as behaviors. A single fact may be presented differently by different writers depending on the range of a

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