Omnidirectional mobile robots are considered for many research applications due to their maneuverability in tight spaces and smoothness of motion. This paper presents a novel design of a T-model mecanum wheeled mobile robot (3-MWMR), the derivation of the inverse and forward kinematics model of the proposed robot, the physical body design of the proposed T-model shape, the simulation of the system dynamics and omnidirectional capabilities of the robot on CoppeliaSim V-rep and MATLAB. The experimental results obtained validates the proof of concept of the proposed model for reduced power losses

The development of artificial intelligence (AI) provided powerful assistant tools for humans in various aspects. Healthcare is rapidly evolving, with AI playing a crucial role in improving patient care. The extensive use of AI in Clinical Decision Support Systems (CDSS) enables providing real-time evidence-based recommendations to healthcare professionals at the point of Care. The AI chatbot ChatGPT proved its ability to solve several natural language processing tasks. One notable advancement is the integration of ChatGPT into Clinical Decision Support Systems. ChatGPT, despite not being

Uni-Buddy is an advanced AI system developed to simplify university life at Nile University. It efficiently handles questions in everyday language, accesses real-time university databases, and simultaneously provides accurate responses for multiple users. Its goals include assisting with course registration, academic advising, financial inquiries, campus navigation, and research support. The evaluation demonstrates Uni-Buddy's user-friendly design, effective navigation, language comprehension, and database connectivity proficiency. Compared to similar studies, it stands out for its ease of use

Segmentation of nuclei in histopathology images with high accuracy is crucial for the diagnosis and prognosis of cancer and other diseases. Using Artificial Intelligence (AI) in the segmentation process enables pathologists to identify and study the unique properties of individual cells, which can reveal important information about the disease, its stage, and the best treatment approach. By using AI-powered automatic segmentation, this process can be significantly improved in terms of efficiency and accuracy, resulting in faster and more precise diagnoses. Ultimately, this can potentially lead

X-ray has a huge popularity around the world. This is due to its low cost and easy to access. Most of lung diseases are diagnosed using Chest X-ray (CXR). So, developing computer aided detection (CAD) provided with automatic lung segmentation can improve the efficiency of the detection and support the physicians to make a reliable decision at early stages. But when the input image has image artifacts, then any lung segmentation model will introduce suboptimal lung segmentation results. In this paper, a new approach is proposed to make the lung segmentation model robust and boost the basic

The automatic generation of textual descriptions from images, known as image captioning, holds significant importance in various applications. Image captioning applications include accessibility for the visually impaired, social media enhancement, automatic image description for search engines, assistive technology for education, and many more. While extensive research has been conducted in English, exploring this challenge in Arabic remains limited due to its complexity. Arabic is one of the world's most widely spoken languages. Around 420 million native people speak this language. It is also

Social media platforms have emerged as major sources of false information, particularly regarding health topics. like COVID-19 vaccines. This rampant dissemination of inaccurate content contributes significantly to vaccine hesitancy and undermines vaccination campaigns. This research addresses the pressing need for automated methods to distinguish between factual and fabricated Arabic tweets concerning vaccines, aiming to mitigate the spread of misinformation on these platforms. The proposed approach utilizes deep learning techniques, leveraging pre-trained Arabic language models (Arabert)

Arabic is one of the world's richest languages, with a diverse range of dialects based on geographical origin. In this paper, we present a solution to tackle subtask 1 (Country-level dialect identification) of the Nuanced Arabic Dialect Identification (NADI) shared task 2022 achieving third place with an average macro F1 score between the two test sets of 26.44%. In the preprocessing stage, we removed the most common frequent terms from all sentences across all dialects, and in the modeling step, we employed a hybrid loss function approach that includes Weighted cross entropy loss and Vector

Non-Structural Protein 6 (NSP6) has a protecting role for SARS-CoV-2 replication by inhibiting the expansion of autophagosomes inside the cell. NSP6 is involved in the endoplasmic reticulum stress response by binding to Sigma receptor 1 (SR1). Nevertheless, NSP6 crystal structure is not solved yet. Therefore, NSP6 is considered a challenging target in Structure-Based Drug Discovery. Herein, we utilized the high quality NSP6 model built by AlphaFold in our study. Targeting a putative NSP6 binding site is believed to inhibit the SR1-NSP6 protein-protein interactions. Three databases were

Purpose: Petroleum companies have various goals in light of high energy prices, uncertainty and potential fluctuations in demand in the current digital age, including making a profit while maintaining long-term sustainability and lowering their environmental impacts. The purpose of this paper is to explore the impact of technology management (TM) and its practices through process and maintenance technologies on sustainability performance (SP) for petroleum refineries and petrochemical companies in terms of economic, environmental and social sustainability. Design/methodology/approach: A new