Artificial intelligence (AI) offers significant benefits to biomedical research and academic writing. Nevertheless, using AI-powered writing aid tools has prompted worries about excessive dependence on these tools and their possible influence on writing proficiency. The current study aimed to explore the academic staff’s perspectives on the impact of AI on academic writing. This qualitative study incorporated in-person interviews with academic faculty members. The interviews were conducted in a semi-structured manner, using a predetermined interview guide consisting of open-ended questions. The interviews were done in person with the participants from May to November 2023. The data was analyzed using thematic analysis. Ten academics aged

Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algo

This work deals with the effect of adding aluminum nanoparticles on the mechanical properties, micro-hardness and porosity of memory-shape alloys (Cu-Al-Ni). These alloys have wide applications in various industrial fields such as (high damping compounds and self-lubricating applications). The samples are manufactured using the powder metallurgy method, which involved pressing in only one direction and sintered in a furnace surrounded by an inert gas. Four percentages (0%, 5%, 10%, and 15%) of aluminum nanoparticles were fabricated, which depended on the weight of aluminum powder (13%) in the sample under study. To find out which phase is responsible for the reliability of the formation of this type of alloy and its porosity, X-ray diffr

In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal

Carrageenan extract is a compound of sulfated polyglycan that is taken out from red seaweeds. Being hydrocolloid in nature, carrageenan has gelling, emulsifying and thickening properties allowing it to be commonly used in the oral healthcare products and cosmetics. Due to its bioactive compounds, carrageenan has been shown to have antimicrobial, antiviral, and antitumor properties. The purpose of this work is to study the probable use of carrageenan on the diseases that are related to oral cavity and on the genomic DNA in in vitro experimental model

In this study, the effects of k-carrageenan on four different cell lines related to the cancer and normal cells which cultured on selective media were done. Moreover, the eff

This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security