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Dynamic Path Planning for Autonomous Vehicles Using Adaptive Reinforcement Learning
No full textThis paper focuses on local dynamic path planning for autonomous vehicles, using an Adaptive Reinforcement Learning Twin Delayed Deep Deterministic Policy Gradient (ARL TD3) model. This model effectively navigates complex and unpredictable scenarios by adapting to changing environments. Testing, using simulations, shows improved path planning over static models, enhancing decision-making, trajectory optimization, and control. Challenges such as vehicle configuration, environmental factors, and top speed require further refinement. The model’s adaptability could be enhanced by integrating more data and exploring a fusion between supervised reinforcement learning and adaptive reinforcement learning techniques. This work advances autonomous vehicle path planning by introducing an ARL TD3 model for real-time decision-making in complex environments
Design of hospitals for the post-pandemic era: an international survey of design professionals on infection control design strategies
No full textThe COVID-19 pandemic placed unpreceded stress on healthcare systems, highlighting their deficiencies and vulnerabilities. The risk of cross-infection among patients and healthcare workers increased significantly, hindering the overall healthcare process. Hospital design should meet the challenges that will be posed by future pandemics, especially infection control. This paper reports on the results of an online survey for healthcare design professionals and academics from across the globe. The questionnaire addressed how hospital design can contribute to the infection control during future pandemic situations. Respondents agreed that the benefits of designing hospitals to meet the needs of pandemic/emergency situations outweigh the additional cost, and that infection control should play a more pronounced role when designing hospitals for the coming era. Results indicate that the overall organization of hospitals should allow for the compartmentation of critical/isolation departments/units and the creation of separate circulation routes to suit this compartmentation during pandemic times. The circulation system of the emergency departments and Intensive Care Units (ICU) should allow for the separation of infectious patients from others during pandemic times. 20–30% of the ICU beds of tertiary level care hospitals should be designed as single patient rooms. 53% of these rooms should be easily convertible to Air-Borne-Infection Isolation (AII) rooms, 44% should have an additional space for adding anterooms during pandemics, and 55% of these rooms should have permanent storage spaces for Personal Protective Equipment. The inpatient units should also be designed to support infection control. AII rooms should be grouped in designated inpatient units
The Story of an Egyptian Cat Mummy Through CT Examination â€
No full textMuch of the fascination surrounding Egyptian civilization is linked to the practice of mummification. In fact, to ensure the preservation of the body, the ancient Egyptians mummified both human and animal subjects. However, mummified animal remains are less well studied, although they represent a significant part of the material culture and history of ancient Egypt. The introduction of non-invasive imaging methods has allowed researchers to study the material hidden within the wrappings of mummies. In this article, the cat mummy currently exhibited at the Museo Etnologico Missionario di San Francesco di Fiesole (Florence, Italy), originating from Luxor and legally acquired during an expedition in the 20th century, was analyzed using computed tomography (CT). The CT enabled the identification of the casing content, showing the presence of an entire cat skeleton. The cat had several fractures, some of which were identified in the cervical region, possibly related to the cause of death. Furthermore, the zooarcheological analysis allowed the identification of the age at death of the cat, providing further information about the story of the mummy. This research provides a further contribution to the analysis of mummies, with a case study of a cat mummy that emphasizes the importance of CT scans in humanistic studies and museum environments
Improving access to evidence-based interventions for trauma-exposed adults in low- and middle-income countries
No full textIn low- and middle-income countries (LMICs), the mental health consequences of trauma exposure pose a substantial personal, societal, and economic burden. Yet, the significant need for evidence-based mental health treatment remains largely unmet. To unlock the potential for mental health care for trauma survivors in lower-resource contexts, it is critical to map treatment barriers and identify strategies to improve access to evidence-based, culturally appropriate, and scalable interventions. This review, based on an International Society for Traumatic Stress (ISTSS) briefing paper, describes the treatment gap facing adults with traumatic stress in LMICs and identifies the barriers that contribute to this gap. We then highlight strategies for enhancing access to effective treatments for these populations, including task-sharing, the use of culturally adapted and multiproblem interventions, and digital tools to scale access to appropriate care. Finally, we offer recommendations for policymakers, researchers, and service providers to guide an agenda for action to close the treatment gap for trauma survivors in LMICs
International ownership and SMEs in Middle Eastern and African economies
Get PDFEmpirical evidence on the benefits of international ownership for small and medium-sized enterprises (SMEs) financial performance is either not available for most African and Middle Eastern countries or presents mixed results. In this paper, we investigate this further by examining the effects of ownership structure on firm performance, using financial data covering SMEs in 60 African and Middle Eastern countries, for the years 2006–2015. Results from pooled ordinary least squares and random-effects estimations indicate that international ownership is significantly positively correlated with firm performance for (most of) Africa and the Middle East. Examining the interaction of international ownership with capital resources, we find that internationally owned firms do not use capital more efficiently than locally owned firms, implying that internationally owned firms use international resources—other than capital—more efficiently
Arabic Gender Podcasts: A Genre Analysis of Content, Forms, and Agendas Challenging Traditional Narratives
No full textThis thesis examines the genre forms and agendas of Arabic gender podcasts through qualitative analysis of 100 episodes from 20 podcasts combined with creator interviews. The research reveals distinctive cultural adaptations of global podcast formats, characterized by a preference for co-hosted (46%) and panel discussion (32%) formats over solo hosting. This adaptation creates intimate spaces for challenging traditional gender narratives through advocacy (24%), education (23%), and empowerment (21%) content genres. The study introduces the concept of “cultural genre adaptation” in podcasting and proposes a “layered agenda-setting” model that extends McCombs\u27 traditional theory. The findings demonstrate how Arabic gender podcasts maintain cultural sensitivity while advancing progressive gender discourse. A key aspect of this is the careful crafting of parasocial relationships, which helps the audience feel connected and engaged. While the study\u27s scope is limited by the technical difficulty in obtaining a comprehensive list of Arabic gender podcasts and the frequent discontinuation of shows, affecting sample representativeness, this research contributes to understanding the intersection of podcasting, gender studies, and cultural adaptation in digital media, particularly in the Arab context. The results suggest an integrated model showing how podcast genres evolve through cultural specificity rather than purely technological affordances
CASAR Student Discussion Series #6: \u27The 2024 US Election results are out!The 2024 US Elections results will affect the Middle East Region positively\u27
Get PDFEvery semester, CASAR AUC holds miscellaneous student debates discussing the hot controversial topics in the US, Egypt and from around the world in hopes of raising awareness about these topics & broadening the perspectives of our students as they learn to engage in fruitful discussions as they share different opinion on the topic being deliberated. At this CASAR Student Discussion event #6 in light of the current 2024 US Elections, we debated this statement: \u27The 2024 US Elections results will affect the Middle East Region positively\u27 A brief into was given followed by short videos to watch before opening the floor to the attending audience to speak their minds as they began the debate. The winning student with the best argument will get the following: A certificate of appreciation from CASAR A special feature on the CASAR website, FB & IG accounts An honorary special feature on the \u27Speaker of the month\u27 board at the center. The winner of this debate was Islam Nadim.
Date: 10 Nov. 2024https://fount.aucegypt.edu/events_and_performances/1052/thumbnail.jp
Reliable Outdoor Ai-Based Autonomous Uav Localization and Trajectory Tracking Using Cellular Networks
Get PDFUnmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. Existing localization and trajectory planning techniques, which rely mainly on the Global Positioning System (GPS), do not provide an effective real-time solution for self-localization and path planning, particularly in dense urban environments. The purpose of this thesis is to study the localization and trajectory planning of UAVs independent of GPS systems or other detectable mobile signals. We propose to utilize the broadcast signals from existing cellular networks to localize and navigate the UAV from a given source to a destination. This simply implies that the drone needs to rely on the signals of the surrounding cellular base stations without having to interact with these base stations. The applications of this include, but are not limited to, mission-critical applications in which the unreliable GPS signal detection may compromise the mission. The use of AI-based techniques will provide near-optimal location and path determination, while providing a practical real-time calculation that is needed in such dynamic applications. For this purpose, we first address the cellular network-based autonomous 3-D UAV localization problem. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real-time calculation that is needed in such dynamic applications. Machine-learning based techniques are strong candidates as an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature. Next, we address the autonomous UAV trajectory planning problem. For this purpose, we formulate the UAV trajectory planning problem as a joint objective optimization problem to minimize a composite cost metric that we also introduce. The computational complexity involved in exact optimization techniques hinders obtaining the real-time calculation requirement that is needed due to the dynamic nature of the UAV operation and the environment. To overcome this complexity, we utilize machine-learning based techniques to solve the formulated trajectory planning problem. Specifically, we propose two machine learning-based techniques, namely, reinforcement learning and the deep supervised learning-based approaches. We then analyze the performance of each of the proposed techniques as compared to the optimization-based approaches and other solutions from the literature.
Moreover, we propose a reliable cellular network supported trajectory planning solution independent of transmissible detectable signals and the GPS system under realistic channel propagation conditions assumptions. The reliability of the UAV trajectory planning solutions is crucial for mission critical applications. As such, we derive a UAV trajectory reliability model as pertains to UAV motion uncertainty along the trajectory. We then formulate the reliable UAV navigation problem as an optimization problem to maximize the probability of minimum error along the path of the UAV. We utilize conventional optimization methods to determine the optimal bound of the solution of the reliable UAV navigation problem. To navigate the UAV reliably and autonomously in real time, we propose a machine learning based technique. Specifically, we propose double deep Q-Learning (DDQN) as a framework to solve the formulated reliable UAV navigation problem. We provide an in-depth evaluation to assess the performance of our proposed DDQN-based solution as compared to the presented optimization methods and other representative techniques from the literature. Finally, we address the smoothness of our proposed trajectory planning approaches to realize a practical algorithm implementation. We provide a comparative evaluation of the set of algorithms that we proposed and demonstrate the recommended use case scenario for each of these trajectory planning solutions. Our simulation results show that our proposed machine learning-based approaches provide near optimal solutions to the formulated UAV localization and trajectory planning problems, with comparable accuracy to the optimal bound while meeting the real-time calculation requirement
The Effect of ESG on Indices’ and Firms’ Performance. A Global and an Egyptian Context.
Get PDFOver the years, ESG (environmental, social, and governance) investing has been increasingly adopted by the financial markets. In this paper, we aim to study the effect of ESG on Indices’ and Firms’ performance using Morgan Stanley Capital International (MSCI) Indices as a global reference and Egypt’s indices as a national reference. We also study the effect of ESG scores on the Egyptian Firms’ performance over the past 16 years using econometrical models and Fama- French Five Factors Model. Our research shows mixed results which are aligned with the existing literature review. However, one can conclude that there is a positive significant relationship between the total ESG score and the excess return of the Egyptian stocks, using Fama-French five factors model
UNHCR Egypt\u27s Impact on Refugees and Asylum Seekers: 2000-2020
Get PDFThis thesis examines the shifts in UNHCR Egypt’s practice and policy and their impacts on refugees and asylum seekers in Egypt. It focuses on procedures of reception, registration, refugee status determination (RSD), and resettlement. It also examines the changes in services provided to refugees and asylum seekers, such as health care, education, residency permits, and future change. In addition, the study explores the major reasons for these shifts and whether they are stimulated by the global refugee regime or other factors such as domestic legislation. The thesis attempts to answer the following two questions: 1) what are the shifts in UNHCR Egypt’s practice and policy that influences refugees and asylum seekers\u27 situation in Egypt and why? 2) How does UNHCR Egypt\u27s practice and policy shift affect the situation of refugees and asylum seekers? Structured interviews were conducted with 12 participants from UNHCR staff and partner organizations, academia, and community leaders of refugees and asylum seekers, as well as undertaking participant observation.
The research findings show that despite some efforts by UNHCR to improve its policy and practice, it is working in an increasingly difficult context with constraints imposed by the government and the global refugee regime. The effects on the refugees and asylum seekers seem to be largely negative, with support declining and serious difficulties getting responses, delays in registrations, RSD interviews, and resettlement, and an inability to reach them on the phone or physically. Yet, there are a few positive changes, yet, some of them seem to be quite temporary or change to a negative impact after a short time, such as the change in residency permits.
Keywords: UNHCR, practice, policy, shift, refugees, asylum seekers, registration, resettlement, protectio