Now showing 1 - 10 of 74
  • Publication
    Comparative Study of Polyethylene Films Embedded with Oxide Nanoparticles of Granulated and Free-Standing Nature
    ( 2022) ; ; ;
    Le Guyon, Valerie
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    El-Rifai, Joumana
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    Angastiniotis, Nicos
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    Koutsokeras, Loukas
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    Duponchel, Benoît
    Nanocomposite polymer films are a very diverse research field due to their many applications. The search for low-cost, versatile methods, producing regulated properties of the final products, has thus become extremely relevant. We have previously reported a bulk-scale process, dispersing granulated metal oxide nanoparticles, of both unary and multi-component nature, in a low-density polyethylene (LDPE) polymer matrix, establishing a reference in the produced films’ optical properties, due to the high degree of homogeneity and preservation of the primary particle size allowed by this method. In this work, unmodified, free-standing particles, namely zinc oxide (ZnO) , titanium dioxide (TiO2), aluminum oxide (Al2O3), and silicon dioxide (SiO2) are blended directly with LDPE, and the optical properties of the fabricated films are compared to those of films made using the granulation process. The direct blending process evidently allows for control of the secondary particle size and ensures a homogeneous dispersion of the particles, albeit to a lesser extent than the granulation process. Despite the secondary particle size being comparatively larger than its granulated counterpart, the process still provides a regulated degree of deagglomeration of the free-standing oxide particles, so it can be used as a low-cost alternative. The regulation of the secondary particle size tunes the transmission and reflection spectra, in both unary and mixed oxide compositions. Finally, the direct blending process exhibits a clear ability to tune the energy band gap in mixed oxides.
      30  6
  • Publication
    The Entropic Braiding Index : A Robust Metric to Account for the Diversity of Channel Scales in Multi‐Thread Rivers
    ( 2022) ;
    Jon Schwenk
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    Maarten Kleinhans
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    Ajay B. Limaye
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    Lawrence Vulis
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    Paul Carling
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    Holger Kantz
    ;
    Efi Foufoula‐Georgiou
    The Braiding Index (BI), defined as the average count of intercepted channels per cross-section, is a widely used metric for characterizing multi-thread river systems. However, it does not account for the diversity of channels (e.g., in terms of water discharge) within different cross-sections, omitting important information related to system complexity. Here we present a modification of BI, the Entropic Braiding Index (eBI), which augments the information content in BI by using Shannon Entropy to encode the diversity of channels in each cross section. eBI is interpreted as the number of “effective channels” per cross-section, allowing a direct comparison with the traditional BI. We demonstrate the potential of the ratio BI/eBI to quantify channel disparity, differentiate types of multi-thread systems (braided vs. anastomosed), and assess the effect of discharge variability, such as seasonal flooding, on river cross-section stability
      17
  • Publication
    Multiclass feature selection with metaheuristic optimization algorithms: a review
    ( 2022) ;
    Olatunji O. Akinola
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    Absalom E. Ezugwu
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    Jeffrey O. Agushaka
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    Latih Abualigah
    Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.
      18
  • Publication
    A New Method for Generalizing Burr and Related Distributions
    ( 2022) ;
    Suchismita Das
    ;
    Swarup Chattopadhyay
    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.
      25  2
  • Publication
    An intelligent cybersecurity system for detecting fake news in social media websites
    ( 2022) ;
    Mughaid, Ala
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    Al-Zu'bi, Shadi
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    Arjan, A
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    Al-Amrat, Rula
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    Alajmi, Rathaa
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    Abualigah, Laith
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    Maalej, Ahmed
    People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy.
      19  8Scopus© Citations 2
  • Publication
    Ionic Liquid Engineering in Perovskite Photovoltaics
    ( 2022) ;
    Wang, Fei
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    Duan, Dawei
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    Singh, Mriganka
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    Sutter‐Fella, Carolin M
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    Lin, Haoran
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    Naumov, Panče
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    Hu, Hanlin
    Over the past decade, perovskite photovoltaics have approached other currently available technologies and proven to be the most prospective type of solar cells. Although the many-sided research in this very active field has generated consistent results with regards to their undisputed consistently increasing power conversion efficiency, it also produced several rather contradictory opinions. Among other important details, debate surrounding their proneness to surface degradation and poor mechanical robustness, as well as the environmental footprint of this materials class remains a moot point. The application of ionic liquids appears as one of the potential remedies to some of these challenges due to their high conductivity, the opportunities for chemical ‘tuning’ of the structure, and relatively lower environmental footprint. This article provides an overview, classification, and applications of ionic liquids in perovskite solar cells. We summarize the use and role of ionic liquids as versatile additives, solvents, and modifiers in perovskite precursor solution, charge transport layer, as well as for interfacial and stability engineering. Finally, challenges and the future prospects for the design and/or selection of ionic liquids with a specific profile that meets the requirements for next generation highly efficient and stable perovskite solar cells are proposed.
      64Scopus© Citations 1
  • Publication
    Review on COVID-19 diagnosis models based on machine learning and deep learning approaches
    COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
      95  2Scopus© Citations 48
  • Publication
    Light and Secure Encryption Technique Based on Artificially Induced Chaos and Nature-Inspired Triggering Method
    ( 2022) ;
    Muhammed J. Al-Muhammed
    Encryption is the de facto method for protecting information, whether this information is locally stored or on transit. Although we have many encryption techniques, they have problems inherited from the computational models that they use. For instance, the standard encryption technique suffers from the substitution box syndrome—the substitution box does not provide enough confusion. This paper proffers a novel encryption method that is both highly secure and lightweight. The proposed technique performs an initial preprocessing on its input plaintext, using fuzzy substitutions and noising techniques to eliminate relationships to the input plaintext. The initially encrypted plaintext is next concealed in enormously complicated codes that are generated using a chaotic system, whose behavior is controlled by a set of operations and a nature-inspired triggering technique. The effectiveness of the security of the proposed technique is analyzed using rigorous randomness tests and entropy.
      34  4
  • Publication
    Packing-Dependent Mechanical Properties of Schiff Base Crystals
    ( 2022) ;
    Lan, Linfeng
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    Di, Qi
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    Liu, Bin
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    Xu, Yu-xin
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    Naumov, Panče
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    Zhang, Hongyu
    Flexible luminescent crystals endowed with mechanical compliance are emerging as materials that could be the foundation of future lightweight single-crystal flexible optoelectronics. Multiple mechanical responses (for example, elastic and plastic deformation) are rarely observed with the same material among the reported examples of such materials. Here, we report a Schiff base, (Z)-3-(4-ethoxyphenyl)-2-(4-(((E)-2-hydroxy-5-methoxybenzylidene)amino)phenyl)acrylonitrile, which crystallizes as two polymorphs and one tetrahydrofuran solvate. All three forms are emissive, but they have different mechanical properties. Specifically, two of the forms that are unsolvated polymorphs (denoted A and B) were found to be brittle and plastic, respectively, while the third form, which is a solvate (denoted C), showed excellent elasticity. Notably, form C becomes plastic after the crystal is desolvated. Single-crystal X-ray diffraction (SCXRD) and mechanical testing were performed to obtain better insight into the root-cause for the observed difference in mechanical properties. Since crystals of forms B and C are mechanically compliant as well as optically transparent, they were tested as flexible single-crystal optical waveguides.
      6
  • Publication
    Economic load dispatch using memetic sine cosine algorithm
    ( 2022) ;
    Mohammed Azmi Al-Betar
    ;
    Mohammed A. Awadallah
    ;
    Khaled Assaleh
    In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality and inequality constraints. SCA is a recent population based optimizer turned towards the optimal solution using a mathematical-based model based on sine and cosine trigonometric functions. As other optimization methods, SCA has main shortcoming in exploitation process when a non-linear constraints problem like ELD is tackled. Therefore, β-hill climbing optimizer, a recent local search algorithm, is hybridized as a new operator in SCA to empower its exploitation capability to tackle ELD. The proposed hybrid algorithm is abbreviated as SCA-βHC which is evaluated using two sets of real-world generation cases: (i) 3-units, two versions of 13-units, and 40-units, with neglected Ramp Rate Limits and Prohibited Operating Zones constraints. (ii) 6-units and 15-units with Ramp Rate Limits and Prohibited Operating Zones constraints. The sensitivity analysis of the control parameters for SCA-βHC is initially studied. The results show that the performance of the SCA-βHC algorithm is increased by tuning its parameters in proper value. The comparative evaluation against several state-of-the-art methods show that the proposed method is able to produce new best results for some tested cases as well as the second-best for others. In a nutshell, hybridizing βHC optimizer as a new operator for SCA is very powerful algorithm for tackling ELD problems.
      25  4Scopus© Citations 4