Low RCS Multi-Bit Coding Metasurface Modeling and Optimization: Mom-Gec Method in Conjunction With Genetic Algorithm

Soltani, Imen; Soltani, Takoua; Aguili, Taoufik

doi:10.2528/pierm19053006

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…ERIC-PCR amplification of E. coli specifically has been shown to turn up a range of band sizes with reports of 170 to about 4000 bp, 232 to 2690 bp, 380 to 3280 bp and 300 bp to 4500 bp (Soltani et al, 2012;Ranjbar et al, 2017;Ramakrishna et al, 2022). Though these studies differed in their source of isolates, the variations did not appear to be species based.…”

Section: Discussionmentioning

confidence: 99%

Molecular typing of clinical and non-clinical <i>Escherichia coli</i> from Rivers State, Nigeria using amplification-based RAPD and ERIC-PCR typing methods

Dibia,

Otokunefor

2024

Nig. J. Biotechnol

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Due to the diverse nature of Escherichia coli, typing of these organisms is essential. This can provide key epidemiological information not just on the strains in circulation and their relationship, but also on the development and spread of drug resistant clones. Affordable PCR-based typing techniques have resulted in an increase in typing studies particularly in resource limited settings. But information from Nigeria on strain typing is limited. This study was therefore aimed at typing clinical and non-clinical strains of E. coli from Nigeria using the PCR based Random Amplification of Polymorphic DNA (RAPD) and Enterobacterial Repetitive Intergenic Consensus-PCR (ERIC-PCR) typing methods. E. coli was isolated from clinical and non-clinical sources using Eosin Methylene Blue agar. Characteristic green metallic sheen colonies associated with E. coli were purified, identities determined using standard biochemical tests and confirmed using molecular methods. Susceptibility testing was carried out using the modified Kirby-Bauer disc diffusion test and RAPD and ERIC-PCR typing carried out on 48 confirmed E. coli isolates as previously described. ERIC-PCR typing resulted in the generation of 38 unique patterns with a 0.94 diversity. Nine of these were singletons comprised of only a single isolate, while the remaining 29 patterns were grouped to 10 clusters. RAPD gave similar results of 37 patterns, 9 singletons, 0.94 diversity and 13 clusters. For ERIC-PCR, all isolates with identical patterns were from the same source. RAPD, however, had identical patterns present in isolates from different sources. Using the ERIC-PCR typing method, this study was able to identify non-clinical isolates as distinct from the clinical E. coli isolates as evidenced by 100% identical ERIC-PCR profiles of isolates from the same source and a lack of 100% relatedness in isolates from different sources.

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Section: Discussionmentioning

confidence: 99%

Molecular typing of clinical and non-clinical <i>Escherichia coli</i> from Rivers State, Nigeria using amplification-based RAPD and ERIC-PCR typing methods

Dibia,

Otokunefor

2024

Nig. J. Biotechnol

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“…The proposed topology presents a structure with a uniform partitioned distribution, flexible and redundant for the scope of service continuity and aims also to solve the problem of the high inrush currents of the ac/dc switching power supplies of LED luminaires. It requires to provide multiple supplying sources and to adopt different technological systems [12,13], in particular, allowing a digital, an analogic and a manual ON/OFF control. Therefore, the modular distribution is configured with a main AC distribution and a branch DC distribution that connects clusters of luminaires as double-dual corded equipment, with double power supply and double control type, digital and analogic.…”

Section: Discussionmentioning

confidence: 99%

“…The lighting system is equipped with an integrated control, regulation, supervision and monitoring system that is with 4 subsystems:-ON/OFF control system; -Regulation system; - Supervision system; -Monitoring system. The following communication standards are used (Figure 9): -Konnex as the main BUS; -DALI for controlling the LEDs adjustable via the LED dimmer device; -0-10V as a redundant analogue system for controlling the LEDs adjustable via LED dimmer device; -Modbus for the supervision part of the electrical switchboards; -TCP / IP for high level integration [12,13].…”

Section: B) Command Regulation Supervision and Monitoring Systemsmentioning

confidence: 99%

Topology of Continuous Availability for LED Lighting Systems

Parise

Allegri

Pennacchia³

et al. 2019

IEEE Trans. on Ind. Applicat.

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Lighting systems with a big number of luminaires in large halls are a case of distributed loads that need topologies with modularity, whenever possible to ensure a uniform distribution of the supplying circuits, an easier installation, management and maintenance. The LED luminaires give a great impact on the system operation due to their auxiliary series devices and to the high inrush currents of the ac/dc switching power supplies. This paper proposes a topology to design LED lighting systems, configured in a modular scheme of a main AC distribution and a branch DC distribution supplying luminaires clusters. Each cluster is provided as a "double-dual corded" equipment with double power supply and double control type, digital and analogic. The suggested topology aims to make available a system that allows overcoming fault situations by design and permits maintenance activities limiting and recovering degradation conditions. In this way, the lighting system of special locations, for which there is the willingness-to-accept greater financial costs against loss service risks, can satisfy the requirement of continuous availability system.To provide more details on the proposed design criteria this paper describes, as case study, the lighting system of a parliamentary hall with one thousands of luminaires.

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“…Application field: Electromagnetics 159 Li, Chen, Zeng, et al [368] 2017 Adaptive GA optimization framework 160 Zhang and Cuii [369] 2017 BPSO optimization framework 161 Ahmed, Chandra, and Al-Behadili [370] 2017 GA Inverse design 162 Han, Cao, Gao, et al [371] 2017 GA optimization framework 163 Pfeiffer and Tomasic [372] 2017 GA optimization framework 164 Pelluri and Appasani [373] 2017 GA optimization framework 165 Feng, Chen, and Huang [374] 2017 GA optimization framework 166 Allen, Dykes, Reid, et al [375] 2017 GA optimization framework 167 Ding, Zhang, Zhang, et al [376] 2017 GA optimization framework 168 Mahdi and Taha [377] 2017 GA Topology optimization 169 Su, Lu, and Li [378] 2017 PSO optimization framework 170 Orlandi [379] 2018 Differential evolution optimization framework (DE) algorithm 171 Bagmancı, Karaaslan, Altıntaş, et al [380] 2018 GA optimization framework 172 Lim, Song, Kim, et al [381] 2018 GA optimization framework 173 Corrêa, Resende, Bicalho, et al [382] 2018 GA optimization framework 174 Kumar, Behera, and Suraj [383] 2018 GA optimization framework 175 Clemens, Iskander, Yun, et al [189] 2018 Hybrid genetic programming optimization framework 176 Soltani, Soltani, and Aguili [384] 2019 GA Inverse design 177 Ibili, Karaosmanoglu, and Ergul [385] 2019 GA optimization framework 178 Seshadri and Gupta [386] 2019 GA optimization framework 179 Nanda, De, Sahu, et al [387] 2019 GA optimization framework 180 Assal, Benzerga, Sharaiha, et al [388] 2019 GA optimization framework 181 Karatzidis, Kantartzis, Pyrialakos, et al [389] 2019 GA optimization framework 182 Tian and Li [390] 2019 GA optimization framework 183 Yuan, Ma, Sui, et al [391] 2019 GA Topology optimization 184 Yanzhang and Jinghao [392] 2019 GA Topology optimization 185 Sui, Ma, Chang, et al [393] 2019 IAGA optimization framework 186 Steckiewicz and Choroszucho [394] 2019 PSO optimization framework 187 Hao, Du, and Zhang …”

Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

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Low RCS Multi-Bit Coding Metasurface Modeling and Optimization: Mom-Gec Method in Conjunction With Genetic Algorithm

Cited by 5 publications

References 15 publications

Molecular typing of clinical and non-clinical <i>Escherichia coli</i> from Rivers State, Nigeria using amplification-based RAPD and ERIC-PCR typing methods

Molecular typing of clinical and non-clinical <i>Escherichia coli</i> from Rivers State, Nigeria using amplification-based RAPD and ERIC-PCR typing methods

Topology of Continuous Availability for LED Lighting Systems

Machine Intelligence in Metamaterials Design

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