ABSTRACT: This paper focuses on using natural language processing (NLP) in chatbots to manage stress in war-affected countries. A Java-based chatbot was designed to alleviate stress using two algorithms: TextRank and Stanford_CoreNLP. The problem was solved by integrating different languages using a plugin. The chatbot was tested with fifteen people and received positive feedback. Modifications were made based on user feedback, with journaling being a winner. However, the chatbot faced limitations like a lack of Arabic language support and voice chat features.

This study provides a comprehensive evaluation of the compliance of key Libyan government websites with the Web Content Accessibility Guidelines (WCAG) 2.2, the latest international standard for digital accessibility published in October 2023. The assessment focuses on the nine new success criteria introduced in WCAG 2.2, which aim to improve accessibility for users with low vision, cognitive, and motor disabilities. By conducting thorough automated and manual testing, this research identifies the specific strengths and weaknesses of the evaluated websites in meeting WCAG 2.2 requirements at the A, AA, and AAA levels. The findings reveal significant areas for improvement across the government's online presence and provide actionable recommendations for Libyan institutions to enhance their digital accessibility efforts and create a more inclusive online environment for all citizens. KEYWORDS: digital accessibility, web accessibility, compliance, Libyan government websites

Daily incidents significantly impact the workflow of ambulance and healthcare personnel, whose critical role involves providing immediate medical treatment and facilitating transportation to hospitals. This study presents the design of a medical expert system aimed at enhancing first-aid response in ambulances and educating users on fundamental first-aid principles. The proposed system integrates a comprehensive knowledge base that catalogs disease symptoms and corresponding treatments, functioning similarly to a medical professional's guidance. While the system relies on pre-programmed symptoms, it allows for the continuous addition of new symptoms and diseases, ensuring adaptability in emergencies. This expert system is particularly beneficial for novice healthcare providers, equipping them with reliable diagnostic support and improving patient outcomes during medical emergencies.

Abstract

This investigation examines the performance of tension and compression connections in steel beam-column assemblies and concrete slabs, with a particular focus on end-plate joints employing four bolts. By utilising advanced finite element modelling (FEM) and simulation techniques, the study aims to elucidate the behaviour of these joints under both service and extraordinary load conditions. While these connections exhibit favourable flexibility and resilience during typical use, they present challenges in transmitting exceptional loads without incurring joint failure and potential structural collapse, particularly when subjected to unexpected loading scenarios. The research employs a meticulous analytical approach utilising ABAQUS/CAD software. This analysis incorporates a comprehensive evaluation of various parameters, including inherent structural imperfections, material properties, the interplay between steel and concrete, and the influence of non-linear material behaviour. The findings indicate that while these joints perform adequately under standard loading conditions, they may exhibit susceptibility to failure under extreme stresses. This underscores the critical need for the development of adaptable and robust steel beam-column connections to ensure paramount structural safety and stability. Furthermore, the study emphasises the significance of continuous advancements in modelling and simulation techniques, enabling the effective resolution of intricate structural challenges. This investigation offers valuable insights that can be harnessed to develop more efficient and secure composite steel-concrete structures. Furthermore, the study emphasises the significance of continuous advancements in modelling and simulation techniques, which can be employed to mitigate potential structural hazards and enhance building practices, ultimately leading to safer and more resilient structures. © 2024 by authors, all rights reserved.

Author keywords

ABAQUS/CADComposite JointsDurabilityEnd Plate ConnectionsExceptional LoadsFlexibilitySimulation

• ISSN: 23321091
• Source Type: Journal
• Original language: English
• DOI: 10.13189/cea.2024.120437
• Document Type: Article
• Publisher: Horizon Research Publishing

  Saleh, B.; Department of Civil Engineering, School of Science Engineering, Libyan Academy, State of Libya

© Copyright 2024 Elsevier B.V., All rights reserved.

The Libyan healthcare system faces significant challenges in medication management, with high rates of medication errors posing serious risks to patient safety. Digital transformation, particularly through the adoption of electronic medical prescriptions, offers a promising solution to enhance prescription accuracy, improve patient outcomes, and streamline healthcare processes. This technical paper examines the implementation of digital medical prescriptions in Libya, focusing on the role of health informatics, the validation of prescriptions, and the potential barriers to success. The paper also highlights efforts in Arab and African countries similar to Libya, showcasing best practices and lessons learned.

Keywords: Digital prescriptions, Libya healthcare system, Medication errors, Patient safety, Digital transformation, Prescription practices, Prescription accuracy, Handwritten prescriptions, Prescription software, electronic health record systems (EHRS).

The article discusses exploring the Photovoltaic (PV) emulator for simulating photovoltaic systems for Renewable Energy Analysis (REA) focuses on creating and applying a PV emulator to simulate photovoltaic systems and concludes by emphasizing the value of precise simulation tools in comprehending and enhancing photovoltaic system performance. The first portion of the article emphasizes how renewable energy sources especially solar energy are becoming more and more important in tackling the world's energy problems. As a result, the pv output along with the voltage and current have been presented

This paper employs Monte Carlo Simulation (MCS) to assess how randomly power flow is distributed in Vehicle-to-Grid (V2G) operations. V2G technology facilitates a bidirectional flow of electricity by allowing Electric Vehicles (EVs) to feed excess power back into the grid in addition to drawing power from it. The researchers used Monte Carlo simulation as a computational technique that involves generating random samples to analyze the behaviour of a system, to assess the impact of random variations in power flow on V2G operations. They considered various factors such as EV charging and discharging patterns, grid conditions, and user behaviour to create a realistic simulation environment. The main objective function in this study wants to achieve is to reduce the dependency on the grid and develop a stochastic framework for the efficient management of microgrids with the presence of EVs. reduce cost. As a result, the analysis of the MCS shows the performance of the bidirectional operation, and the output power of the integrated sources is obtained.

Buildings exert a profound influence on the environment, with the design phase recognized as the pivotal determinant of a building’s overall performance. Green building design, in particular, introduces heightened complexity, where the attributes of the design team play a pivotal role in shaping performance outcomes. Consequently, the characteristics of the design team emerge as crucial factors in the enhancement of both green building design performance and client attributes. This study aims to empirically examine a model formulated to gauge the extent to which Effective Design Team Attributes contribute to the enhancement of performance in designing green buildings and influencing client attributes. To achieve this objective, a comprehensive questionnaire survey was administered to professionals within the architecture and engineering domains actively engaged in the design and consulting sectors of the building industry. The collected data underwent meticulous scrutiny for authenticity and dependability using the WINSTEPS 5.2.5 software before undergoing subsequent analysis. Statistical analyses were conducted using SPSS version 19, with Principal Components Analysis (PCA) and the Structural Equation Modeling (SEM) approach implemented through Amos version 18 to derive the most robust model. The findings underscore the pivotal role of an adeptly managed design team in significantly improving both the performance of green building designs and the qualities of clients. Rasch’s analysis confirmed the validity of our 5-point Likert scale for Design Green Building Performance (DGBP), Effective Design Team Attributes …

Alzheimer's disease (AD) is a complex neurological illness that has several deep reasons. According to recent research, the use of machine learning techniques (ML) on MRI images can assist in identify the brain regions and the connections between them that are implicated in dementia. The study aims to review literature from 2017 to 2023 on the use of machine learning algorithms to identifying and categorize AD. The precision of each machine learning model is assessed, and mind map models are employed to illustrate the study and compare the outcomes.