The Fourth Industrial Revolution represents an advanced stage of technological development, characterized by the integration of digital, physical, and biological technologies, with a strong focus on smart connectivity and advanced data analysis. At the core of this revolution stands Artificial Intelligence (AI), which enables the processing of vast amounts of data, decision-making with speed and accuracy, automation of processes, and enhancement of productivity and quality. This research examines the transformative role of AI in the humanities, particularly in archaeological, historical, and geographical studies, where traditional methods face limitations in handling complex and extensive datasets.The study aims to highlight these limitations, explore the potential of AI in analyzing diverse archaeological data to uncover new patterns, examine its role in processing large historical sources, and investigate its application in spatial data analysis for environmental and urban changes. The main hypothesis posits that AI can reveal patterns and insights unattainable by conventional methods, thereby enhancing understanding of past civilizations, historical trends, and geographical dynamics.Using a descriptive-analytical methodology supported by practical examples, the research illustrates AI applications such as satellite image analysis, artifact examination, and archival data processing. The findings indicate that integrating AI with humanities research not only improves data analysis efficiency but also maintains the critical and interpretive framework of the humanities, enabling a deeper understanding of society, history, and collective awareness in the digital era.The study concluded that integrating artificial intelligence into humanities research not only improves the efficiency and speed of data analysis but also contributes to the development of the research methodology itself, by shifting from descriptive to analytical and predictive studies, while preserving the critical and interpretive dimensions of the humanities. The study also recommends strengthening collaboration between researchers in the humanities and computer science, establishing open-source Arabic digital archives, and training researchers in the use of artificial intelligence tools. This will contribute to broadening the horizons of humanities research and connecting the past with the present within an integrated scientific framework that meets the demands of the digital age.
In this research, deposition of titanium oxide (TiO2) and vanadium oxide (V2O5) thin film in different mixing percentage (0, 25 ,50, 75 and100)% on the substrate of glass .The coating thickness was ( 50 nm ).
In this research contact angle was measured and the effect of weather conditions. Results showed that the value of the contact angle of the prepared films reached its highest value at 50% (TiO2+V2O5) was 160º.
The results showed that the optical transmittance of TiO2 and V2O5 thin film decrease with increasing the deposition angle and decrease with increasing V2O5 pro
... Show MoreBackground and Objectives: Dyspepsia is a disorder characterized by difficulty in digestion and represents a major health concern. Therefore, it is crucial to identify functional dyspepsia linked to Helicobacter pylori (H. pylori). This research aimed to determine the prevalence of H. pylori among patients with dyspepsia and to examine the potential risk factors associated with the infection. Materials and Methods: From August 14th to September 21st, 2024, a total of 105 patients with dyspepsia, who attended the Central Laboratory of Baghdad Medical City Complex (Iraq), were enrolled in this study. Data on nonsteroidal anti-inflam- matory drugs (NSAIDs), smoking, family history, fasting habits and frequent fast food consumption wer
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreNew evidence on nanotechnology has shown interest in the creation and assessment of nanoparticles for cancer treatment. Worldwide, a wide range of tumor-targeted approaches are being developed to reduce side effects and boost the efficacy of cancer therapy. One strategy that shows promise is the use of metallic nanoparticles to increase the radio sensitization of the cancer cells while reducing or maintaining the normal tissue complication probability during radiation therapy. In this study, atmospheric plasma was created using argon gas to create Au NPs using the plasma jet scheme, and their ability to induce apoptosis as an anticancer mechanism was tested. Aqueous gold tetrachloride salts (HAuCl4·3H2O) ere used to produce gold nanopartic
... Show MoreThe introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop
... Show MoreNanofluids, liquid suspensions of nanoparticles (NPs) dispersed in deionized (DI) water, brine, or surfactant micelles, have become a promising solution for many industrial applications including enhanced oil recovery (EOR) and carbon geostorage. At ambient conditions, nanoparticles can effectively alter the wettability of the strongly oil-wet rocks to water-wet. However, the reservoir conditions present the greatest challenge for the success of this application at the field scale. In this work, the performance of anionic surfactant-silica nanoparticle formulation on wettability alteration of oil-wet carbonate surface at reservoir conditions was investigated. A high-pressure temperature vessel was used to apply nano-modification of oil-wet
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