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Now showing 1 - 20 of 850
  • Publication
    A catalytic enantioselective stereodivergent aldol reaction
    (2023)
    Rahman, Md. Ataur
    ;
    Cellnik, Torsten
    ;
    Ahuja, Brij Bhushan
    ;
    Li, Liang 
    ;
    Healy, Alan R.
    The aldol reaction is among the most powerful and strategically important carbon–carbon bond–forming transformations in organic chemistry. The importance of the aldol reaction in constructing chiral building blocks for complex small-molecule synthesis has spurred continuous efforts toward the development of direct catalytic variants. The realization of a general catalytic aldol reaction with control over both the relative and absolute configurations of the newly formed stereogenic centers has been a longstanding goal in the field. Here, we report a decarboxylative aldol reaction that provides access to all four possible stereoisomers of the aldol product in one step from identical reactants. The mild reaction can be carried out on a large scale in an open flask, and generates CO2 as the only by-product. The method tolerates a broad substrate scope and generates chiral β-hydroxy thioester products with substantial downstream utility.
      31  291Scopus© Citations 6
  • Publication
      6
  • Publication
    A Contrastive Study of Arabic and Persian Formulas Against the Evil Eye used by Women
    (2015)
    Ana Ramajo Cuesta 
    ;
    Yousefian, Saloomeh
    In Islamic societies, there exists the belief that a compliment can attract the'evil eye' unless it's accompanied by expressions that invoke God's protection. The purpose of this research is to analyze the use of these expressions and to conduct a contrastive study of Arabic and Persian formulas against the evil eye. In this research, the importance of these expressions and the use of courtesy formulas in responses to compliments about appearance and possessions are analyzed. Participants of the research are: 10 female native speakers of Arabic (between 19 and 24 years old) and 10 female native speakers of Persian (between 27 and 40 years old). Women were chosen in the study as they use more frequently formulas that invoke God's protection. All of them claimed to believe in the evil eye. Participants are requested to view two short videos in which the characters are talking about their appearance and possessions. Once they watch the videos, they recreate the dialogues in their own languages. The researchers analyze the use of formulas against the evil eye and the compliments responses and conduct a comparative study of the use of these formulas in Arabic and Persian.
      106  153
  • Publication
    A French university in the Arab-Persian Gulf: Paris Sorbonne-Abu Dhabi: «A bridge between civilizations»
    (2007)
    Philippe Boulanger 
    l'Université sz Paris Sorbonne s'implante à Abu Dhabi : un pays en pleine expansion; des universités qui évoluent dans la mondialisation; le projet d'ouverture de l'Université Paris Sorbonne-Abu Dhabi. Un pont entre les civilisations : la Sorbonne, 750 ans d'histoire; la diversité culturelle est source de richesse pour chacun; des savoirs enseignés en français. La Sorbonne dans la mondialisation : un marché des étudiants mondialisé; des étudiants provenant du monde entier. Les enjeux de demain.
      528  230
  • Publication
    A globalized higher education empowers Emirati women in their careers
    (2020)
    Alawadhi, Amier
    ;
    AlEbri, Maitha
    ;
    Mousa, Sara
    ;
    Albarri, Tyeb
      11
  • Publication
    A hybrid Harris Hawks optimizer for economic load dispatch problems
    (2023)
    Al-Betar, Mohammed Azmi
    ;
    Awadallah, Mohammed A.
    ;
    Makhadmeh, Sharif Naser
    ;
    Abu Doush, Iyad
    ;
    Abu Zitar, Raed 
    ;
    Alshathri, Samah
    ;
    Abd Elaziz, Mohamed
    This paper proposes a hybridized version of the Harris Hawks Optimizer (HHO) with adaptive-hill-climbing optimizer to tackle economic load dispatch (ELD) problems. ELD is an important problem in power systems that is tackled by finding the optimal schedule of the generation units that minimize fuel conceptions under a set of constraints. Due to the complexity of ELD search space, as it is rigid and deep, the exploitation of HHO is improved by hybridizing it with a recent local search method called adaptive-hill climbing. The HHO can navigate several potential search space regions, while adaptive-hill climbing is used to deeply search for the local optimal solution in each potential region. To evaluate the proposed approach, six versions of ELD cases with various complexities and constraints have been used which are the 6 generation units with 1263 MW of load demand, 13 generation units with 1800 MW of load demand, 13 generation units with 2520 MW of load demand, 15 generation units with 2630 MW of load demand, 40 generation units with 10500 MW of load demand, and 140 generation units with 49342 MW of load demand. Furthermore, the proposed algorithm is evaluated on two ELD real-world cases which are 6 units-1263 MW and 15units-2630 MW. The results show that the proposed algorithm can achieve a significant performance for the majority of the experimented cases. It can achieve the best-reported solution for the ELD case with 15 generation units when compared to 15 well-established methods. Additionally, it obtains the second-best for the ELD case with 140 generation units when compared to 10 well-established methods. In conclusion, the proposed method can be an alternative to solve ELD problems which is efficient.
      43  10Scopus© Citations 34
  • Publication
    A Low-Temperature-Resistant Flexible Organic Crystal with Circularly Polarized Luminescence
    (2022)
    Li, Liang 
    ;
    Pan, Xiuhong
    ;
    Zheng, Anyi
    ;
    Di, Qi
    ;
    Duan, Pengfei
    ;
    Ye, Kaiqi
    ;
    Naumov, Panče
    ;
    Zhang, Hongyu
    ;
    Yu, Xu
    Flexible organic crystals with unique mechanical properties and excellent optical properties are of paramount significance for their wide applications in various research fields such as adaptive optics and soft robotics. However, low-temperature-resistant flexible organic crystal with circularly polarized luminescence (CPL) ability has never been reported. Herein, chiral organic crystals with CPL activity and low-temperature flexibility (77 K) are fabricated by the solvent diffusion method from chiral Schiff bases, S(R)-4- b romo-2-(((1- p henyl e thyl)imino) m ethyl) p henol (S(R)-BPEMP). The corresponding chiroptical properties for the two enantiomeric crystals were thoroughly investigated, including the measurements of circular dichroism (CD) and CPL. To the best of our knowledge, this is the first report on low-molecular-weight flexible organic crystals with CPL activity, and we believe that the results will give a new impetus to the research of organic crystals.
    Scopus© Citations 34  43  272
  • Publication
    A Lower Complexity Deep Learning Method for Drones Detection
    (2023)
    Mohamad Kassab
    ;
    Amal El Fallah Seghrouchni
    ;
    Frederic Barbaresco
    ;
    Abu Zitar, Raed 
    Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities can deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep-learning techniques to benchmark real data sets of flying drones. A Deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the SSD paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning techniques such as SVM is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were either RGB or IR data. Comparisons were made between all these types and conclusions are presented.
      40Scopus© Citations 1
  • Publication
    A micromorphological assessment of anthropogenic features in pre-Columbian French Guiana dark soils (FGDS): First results
    (E. Schweizerbart'sche Verlagsbuchhandlung, 2014)
    Cammas, C.
    ;
    Eric Fouache 
    ;
    Todisco, D.
    ;
    Brancier, J.
    In order to document site formation processes at the microscale and to characterise pre-Columbian French Guiana dark soils (FGDS), micromorphology was performed at three sites. For the first time it was possible (i) to microscopically characterise pre-Columbian Anthrosols in different physical contexts and (ii), to identify anthropogenic features associated with past human occupation. Microfeatures of the Holocene alluvial terrace of the lower Maroni River witnessed (i) several episodes of clay enrichment and/or redistribution, (ii) seasonal waterlogging, and (iii), post-depositional biological activity. Clay enrichment and organic matter inputs together with biological activity processes might have alternated, probably in relation to vegetal cover and/or anthropogenic activities. On top of the alluvial terrace, bioturbated dark layers are enriched in fine brown organic matter and charcoals. Cumulic soil development was favoured when successive sediment inputs due to episodic flooding and/or overland flow was possible (Chemin Saint Louis site). On a lateritic hill, under rainforest, at the MC87 ring-ditched mountain (Montagnes Couronnées or Crowned Mountain), microscale identification of yellowish unburnt oxic B horizon aggregates together with anthropogenic features related to fire such as charcoals and burnt soil fragments (rubefied and dark brown aggregates) stress that lateritic soil acted as a support for activities in the enclosure, and as reworked material in the ditch. These components could result from clearance for settlement, agricultural management and cultivation, or domestic activities. The obtained results allow first comparisons to be drawn between pre-Columbian FGDS and Brazilian dark earths (BDE). With the exception of a similarity in colour, the former is revealed to be less rich in anthropogenic components with an absence of phosphatic elements such as bones. © 2014 Gebrüder Borntraeger Verlagsbuchhandlung, Stuttgart, Germany.
    Scopus© Citations 10  65
  • Publication
    A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem
    (2022)
    Abu Zitar, Raed 
    ;
    Mohammad Dalbah, Lamees
    ;
    Al-Betar, Mohammed Azmi
    ;
    Awadallah, Mohammed A.
    Capacitated Vehicle routing problem is NP-hard scheduling problem in which the main concern is to findthe best routes with minimum cost for a number of vehicles serving a number of scattered customersunder some vehicle capacity constraint. Due to the complex nature of the capacitated vehicle routingproblem, metaheuristic optimization algorithms are widely used for tackling this type of challenge.Coronavirus Herd Immunity Optimizer (CHIO) is a recent metaheuristic population-based algorithm thatmimics the COVID-19 herd immunity treatment strategy. In this paper, CHIO is modified for capacitatedvehicle routing problem. The modifications for CHIO are accomplished by modifying its operators to pre-serve the solution feasibility for this type of vehicle routing problems. To evaluate the modified CHIO, twosets of data sets are used: the first data set has ten Synthetic CVRP models while the second is an ABEFMPdata set which has 27 instances with different models. Moreover, the results achieved by modified CHIOare compared against the results of other 13 well-regarded algorithms. For the first data set, the modifiedCHIO is able to gain the same results as the other comparative methods in two out of ten instances andacceptable results in the rest. For the second and the more complicated data sets, the modified CHIO isable to achieve very competitive results and ranked the first for 8 instances out of 27. In a nutshell,the modified CHIO is able to efficiently solve the capacitated vehicle routing problem and can be utilizedfor other routing problems in the future such as multiple travelling salesman problem
    Scopus© Citations 28  151  107
  • Publication
    A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
    (2023)
    Ekinci, Serdar
    ;
    Izci, Davut
    ;
    Abualigah, Laith
    ;
    Abu Zitar, Raed 
    In this work, we propose a real proportional-integral-derivative plus second-order derivative (PIDD2) controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation. In this regard, this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system. We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism. We also propose a simple yet effective objective function to increase the performance of the proposed algorithm (CmOBL-AO) to adjust the real PIDD2 controller's parameters effectively. We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm, gravitational search algorithm, African vultures optimization, and the Aquila Optimizer using well-known unimodal, multimodal benchmark functions. CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. For the vehicle cruise control system, we confirm the more excellent performance of the proposed method against particle swarm, gray wolf, salp swarm, and original Aquila optimizers using statistical, Wilcoxon signed-rank, time response, robustness, and disturbance rejection analyses. We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective. The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds. Lastly, we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases. We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
      39Scopus© Citations 48
  • Publication
    A New Method for Generalizing Burr and Related Distributions
    (2022)
    Chakraborty, Tanujit 
    ;
    Das, Suchismita
    ;
    Chattopadhyay, Swarup
    A new method has been proposed to generalize Burr-XII distribution, also called Burr distribution, by adding an extra parameter to an existing Burr distribution for more flexibility. In this method, the exponent of the Burr distribution is modeled using a nonlinear function of the data and one additional parameter. The models of this newly introduced generalized Burr family can significantly increase the flexibility of the former Burr distribution with respect to the density and hazard rate shapes. Families expanded using the method proposed here is heavy-tailed and belongs to the maximum domain of attractions of the Frechet distribution. The method is further applied to yield three-parameter classical Pareto and generalized exponentiated distributions which shows the broader application of the proposed idea of generalization. A relevant model of the new generalized Burr family has been considered in detail, with particular emphasis on the hazard functions, stochastic orders, estimation procedures, and testing methods are derived. Finally, as empirical evidence, the new distribution is applied to the analysis of large-scale heavy-tailed network data and compared with other commonly used distributions available for fitting degree distributions of networks. Experimental results suggest that the proposed Burr distribution with nonlinear exponent better fits the large-scale heavy-tailed networks better than the popularly used Marhsall-Olkin generalization of Burr and exponentiated Burr distributions.
    Scopus© Citations 2  42  45
  • Publication
    A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and Levy flight methods
    (2023)
    Abu Zitar, Raed 
    ;
    Braik, Malik Sh.
    ;
    Awadallah, Mohammed A.
    ;
    Al-Betar, Mohammed Azmi
    ;
    Hammouri, Abdelaziz I.
    An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lévy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm’s performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it’s position updating process was slightly tweaked and took advantage of the chameleons’ randomization as well as adopting several time-varying functions. Second, the Lévy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and exploitation conducts. The optimization performance of ECSA is shown by testing it on five various real ELD cases with a generator having 3, 13, 40, 80 and 140 units, each with different constraints. The results of the ELD systems’ analysis depict that ECSA is better than the parent CSA and other state-of-the art methods. Further, the efficacy of ECSA was experimented on several benchmark test functions, and its performance was compared to other well-known optimization methods. Experimental results show that ECSA surpasses other methods on complex benchmark functions with modest computational burdens. The superiority and practicality of ECSA is demonstrated by getting new best solutions for large-scale ELD cases such as 40-unit and 140-unit test systems.
      37Scopus© Citations 14
  • Publication
    A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm
    (2021)
    Abu Zitar, Raed 
    ;
    Alkoffash, Mahmud Salem
    ;
    Awadallah, Mohammed A.
    ;
    Alweshah, Mohammed
    ;
    Assaleh, Khaled
    ;
    Al-Betar, Mohammed Azmi
    In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and β-hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the intensification and diversification of the ELD search space. ELD is an important problem in the power systems which is concerned with scheduling the generation units in active generators in optimal way to minimize the fuel cost in accordance with equality and inequality constraints. The proposed HSSA is evaluated using six real-world ELD systems: 3-unit generator, two cases of 13-unit generator, 40-unit generator, 80-unit generator, and 140-unit generator system. These ELD systems are well circulated in the previous literature. The comparative results against 66 well-regarded algorithms are conducted. The results show that the proposed HSSA is able to produce viable and competitive solutions for ELD problems.
    Scopus© Citations 33  117  24
  • Publication
    A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48
    (2023)
    Al-Manaseer, Hitham
    ;
    Abualigah, Laith
    ;
    Alsoud, Anas Ratib
    ;
    Abu Zitar, Raed 
    ;
    Ezugwu, Absalom E.
    ;
    Jia, Heming
    In this study, the possibility of using and applying the capabilities of artificial intelligence (AI) and machine learning (ML) to increase the effectiveness of Internet of Things (IoT) and big data in developing a system that supports decision makers in the medical fields was studied. This was done by studying the performance of three well-known classification algorithms Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree-J48 (J48), to predict the probability of heart attack. The performance of the algorithms for accuracy was evaluated using the Healthcare (heart attack possibility) dataset, freely available on kagle. The data was divided into three categories consisting of (303, 909, 1808) instances which were analyzed on the WEKA platform. The results showed that the RFC was the best performer.
      53Scopus© Citations 9
  • Publication
    A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education
    (2022)
    Abu Zitar, Raed 
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    AlZu’bi, Shadi
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    Hawashin, Bilal
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    Abu Shanab, Samia
    ;
    Zraiqat, Amjed
    ;
    Mughaid, Ala
    ;
    Almotairi, Khaled H.
    ;
    Abualigah, Laith
    Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.
    Scopus© Citations 23  63  8
  • Publication
    A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
    (2023)
    Raza, Ali
    ;
    Al Nasar, Mohammad Rustom
    ;
    Hanandeh, Essam Said
    ;
    Abu Zitar, Raed 
    ;
    Nasereddin, Ahmad Yacoub
    ;
    Abualigah, Laith
    Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.
      19Scopus© Citations 14
  • Publication
    A Preliminary Hazard Assessment of Kolumbo Volcano (Santorini, Greece)
    (2024)
    Katsigera, Anna
    ;
    Nomikou, Paraskevi
    ;
    Pavlopoulos, Kosmas 
    Volcanic eruptions stand as destructive threats to adjacent communities, unleashing multiple hazards such as earthquakes, tsunamis, pyroclastic flows, and toxic gases. The imperative for proactive management of volcanic risks and communities’ adaptation cannot be overstated, particularly in densely populated areas where the potential for widespread devastation looms large. Kolumbo, an active submarine volcano located approximately 7 km northeast of Santorini Island in Greece, serves as a pertinent case. Its historical record is characterised by an eruption in 1650 CE that produced a catastrophic tsunami. The aftermath witnessed havoc on neighbouring islands, coupled with casualties stemming from noxious gases in Santorini. Eyewitness accounts mention maximum water run-up heights of 20 m on the southern coast of Ios, inundation of an area of 240 m inland on Sikinos, and a flooding of up to 2 km2 inland on the eastern coast of Santorini. Recent studies suggest that a potential future eruption of Kolumbo poses a substantial hazard to the northern and eastern coasts of Santorini. Unfortunately, the absence of a concrete management protocol leaves these areas vulnerable to an impending threat that demands immediate attention. Therefore, it is recommended that a comprehensive approach be adopted, involving scientific research (active monitoring, hazard maps), community engagement, preparedness planning with government agencies, and the development of timely response strategies to reduce the associated risks, prevent casualties, and mitigate the potential consequences on the region’s economy and infrastructure.
      19  4