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| 1 | The cloud computing: the future of BI in the cloud显示文摘 | Ouf S Nasr M | 2011 | International Journal of Computer Theory and Engineering2011,3,6: | 1 |
| 2 | Isolation of antifungal compounds from someZygophyllum species and their bioassay against two soil-borne plant pathogens显示文摘 | S. A. Ouf F. K. Abdel Hady M. H. ElGamal K. H. Shaker | 1994 | Folia Microbiologica1994,,3: | 1 |
| 3 | Heterocyclic synthesis with 4-hydrazinopyridothienopyrimidines:Synthesis of pyridothienotriazolopyrimidines and heterocyclylpyridothienopyrimidines with biological Interest显示文摘 | Gaber H M Elgemeie G E H Ouf S A | 2005 | Heteroat Chem2005,16,4: | 1 |
| 4 | Authenticity and the Sense of Place in Urban Design显示文摘 | Ahmed M. Salah Ouf | 2001 | Journal of Urban Design2001,,1: | 1 |
| 5 | Portal vein thrombosis following splenectomy显示文摘 | Hassn AM Al-Fallouji MA Ouf TI | 2000 | Br J Surg2000,87,3: | 1 |
| 6 | Phytochemical and mycological investigation of Artemisia monosperma 显示文摘 | ELGAMAL M H A OUF S A HANNNA A G | 1997 | Folia Microbiol1997,42,3: | 1 |
| 7 | Prevalence of elevated thyroid-stimulating hormone levels in obese children and adolescents显示文摘 | Dekelbab BH Abou Ouf HA Jain I | 2010 | End0cr Pract2010,16,: | 1 |
| 8 | Measurement of the nanoparticles distribution in flat and pleated filters during clogging显示文摘 | Bourrous S Bouilloux L Ouf F X | 2014 | Aerosol Science and Technology2014,48,4: | 1 |
| 9 | Dischargeantithromboticstrategies among patients with acute coronary syndromepreviouslyon warfarinanticoagulation:physicianpracticeintheCRUSADEregistry显示文摘 | WangTY RobinsonLA OuFS etal | 2008 | Am HeartJ2008,155,2: | 1 |
| 10 | Microbialcommunityevolutionduringsimulatedmanagedaquiferrechargeinresponsetodifferentbiodegradabledissolvedorganiccarbon(BDOC)concentrations显示文摘 | DONG L MazahiraliAlidina Mohamed Ouf etal | 2013 | WaterResearch2013,47,7: | 1 |
| 11 | An Approach to Improve Quality of Life and Sustainability in the Centers of Old Cities显示文摘The centers of old cities have a special design with a unique urban fabric to record cultural messages with their current citizens or their visitors from the different regions of the city or from outside. It creates spaces that strongly promote social and cultural behavior. Meanwhile, life developments and the stunning technological acceleration along with high population densities have led to many problems for the old cities and their centers, which were not prepared to address these problems. Most of these problems have caused much visual deterioration and economic recession for the centers of old cities, which raise the importance of finding alternative and flexible solutions to prevent damage in urban performance, environmental pollution and lack of requirements that affect the quality of life. The paper aims to present a vision that opens up prospects for subsequent research contributions that may contribute to improving the quality of life, inspired by sustainability and humanization features. The research relies on an inductive analysis method to develop a framework that contributes to the restoration of the old centers for their role, their aesthetic value and their functional importance, while facilitating movement and supporting their valuable architectural features. | Tarek Abou Ouf Abeer Makram | 2019 | Journal of Civil Engineering and Architecture2019,13,7: | 0 |
| 12 | A reinforcement learning approach for thermostat setpoint preference learning显示文摘Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints accordingly.Many recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant comfort.These studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat interactions.This paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat overrides.The algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of EnergyPlus.The simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration events.While substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity. | Hussein Elehwany Mohamed Ouf Burak Gunay Nunzio Cotrufo Jean-Simon Venne | 2024 | Building Simulation2024,17,1: | 0 |
| 13 | Modelling occupant behaviour for urban scale simulation:Review of available approaches and tools显示文摘Urban building energy modelling(UBEM)is considered one of the high-performance computational tools that enable analyzing energy use and the corresponding emission of different building sectors at large scales.However,the efficiency of these models relies on their capability to estimate more realistic building performance indicators at different temporal and spatial scales.The uncertainty of modelling occupants'behaviours(OB)aspects is one of the main reasons for the discrepancy between the UBEM predicted results and the building's actual performance.As a result,research efforts focused on improving the approaches to model OB at an urban scale considering different diversity factors.On the other hand,the impact of occupants in the current practice is still considered through fixed schedules and behaviours pattern.To bridge the gap between academic efforts and practice,the applicability of OB models to be integrated into the available UBEM tools needs to be analyzed.To this end,this paper aims to investigate the flexibility and extensibility of existing UBEM tools to model OB with different approaches by(1)reviewing UBEM's current workflow and the main characteristics of its inputs,(2)reviewing the existing OB models and identifying their main characteristics and level of details that can contribute to UBEM accuracy,(3)providing a breakdown of the occupant-related features in the commonly used tools.The results of this investigation are relevant to researchers and tool developers to identify areas for improvements,as well as urban energy modellers to understand the different approaches to model OB in available tools. | Aya Doma Mohamed Ouf | 2023 | Building Simulation2023,16,2: | 0 |
| 14 | An Optimized Deep Learning Approach for Improving Airline Services显示文摘The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%. | Shimaa Ouf | 2023 | Computers, Materials & Continua2023,,4: | 0 |