Objective: This study aims to investigate the impact of artificial intelligence (AI) on digital marketing performance, considering the mediating role of dynamic organizational capabilities (DOC) and big data analytics capability (BDAC). The research seeks to provide a comprehensive understanding of the mechanisms through which AI enhances digital marketing outcomes and the contextual factors that facilitate this impact. Methodology: The study employs a structural equation modeling (SEM) approach to analyze data collected from  samplle size 226 professionals working in e-commerce, digital media, and marketing services companies in  Mashhad city of Iran. . The research utilizes standardized questionnaires to measure AI, digital marketing performance, DOC, and BDAC. The validity and reliability of the measurement instruments are assessed using content validity ratio (CVR), content validity index (CVI), and Cronbach’s alpha coefficient. The study uses proportionate stratified random sampling to ensure representativeness of the sample. Findings: The results provide empirical evidence that AI has a positive and significant impact on digital marketing performance, both directly (β = 0.37, p < 0.001) and indirectly through the mediating effects of DOC (β = 0.12, p < 0.01) and BDAC (β = 0.15, p < 0.001). The findings also demonstrate that AI positively influences DOC (β = 0.42, p < 0.001) and BDAC (β = 0.45, p < 0.001), which in turn enhance digital marketing performance (β = 0.29, p < 0.001 and β = 0.33, p < 0.001, respectively). Conclusion: This study contributes to the literature on AI and digital marketing by providing empirical evidence on the impact of AI on digital marketing performance and the underlying mechanisms through which AI enhances marketing outcomes. The findings highlight the importance of developing organizational capabilities and leveraging big data analytics to fully realize the potential of AI in digital marketing. The study offers practical insights for managers and practitioners, emphasizing the need to invest in AI technologies, develop DOC and BDAC, and undertake broader organizational transformations to effectively harness the power of AI in digital marketing.

Age estimation from facial images is crucial in security, healthcare, and entertainment for applications like age-restricted content filtering and targeted advertising. However, this task is challenging due to pose, illumination, and aging variations. Traditional methods using handcrafted features often lack discriminative power. Deep learning, with its ability to learn complex features, has sparked interest in age estimation. This paper presents a deep learning-based approach for age estimation from frontal face images. It focuses on facial components like eyes, nose, and mouth to capture age-related changes. Augmentation techniques enhance robustness against variations, and a Binary Cascaded CNN with binary weights and activations reduces model complexity. The model transforms augmented components into multimodal features, allowing it to discern age-related changes across domains. Evaluated on FGNET and IMDB-WIKI datasets, the model achieves 99.5% accuracy with an MAE of 1.26 across all age groups. This high accuracy demonstrates the model's effectiveness and potential for real-world age estimation scenarios in diverse applications.

Nanoarchitecture as the new contemporary architectural style of the 21st century that will revolutionize the architecture world in every way either the way architects think or how they inspire their ideas, the used materials in building, finishing materials, or the way we demonstrate to the world and building users. It is not a secret that the teaching of a nanotechnology course will soon be required in most engineering and architecture curricula. The intent of this paper is therefore to connect science and technology under the umbrella of nanotechnology in order to design and build practical and innovative materials and devices from the nanoscale upward. The findings of this paper are expected to benefit nanotechnology in architecture and building materials which will directly or indirectly contribute to its suitability and modern materials use.

A finite volume based Eulerian-Lagrangian model has been created within OpenFOAM® in order to predict the behavior of particle clouds as well as particle deposition thicknesses on substrates under the influence of electro-static effects. The model resolves close to electrode effects as well as phenomena within the entire deposition chamber. It considers fluid dynamic effects, particle inertia, gravity, electric- as well as mechanic particle-particle interaction, corona formation, dynamic particle charging mechanisms, and coupling of particle motion to Reynolds-Averaged Navier-Stokes (RANS) based flow simulations. Resulting deposition pattern predictions were experimentally validated. It is demonstrated qualitatively and quantitatively that the measured deposition thicknesses and patterns vary by; i) applied voltage, ii) airflow rate, pistol-substrate iii) distance and iv) angle. Furthermore, the software has been prepared such that it works on the cloud computing software KaleidoSim®, which enables the simultaneous browser-based running of hundreds of cases for large parameter studies.