Enhancing the exploration and exploitation phases of the metaheuristic (MH) optimization algorithms is the key to avoiding local optima. The Manta ray foraging optimizer is a recently proposed MH optimizer. The MRFO showed a good performance in the simple optimization problems. However, it is trapped into the local optimum in the more elaborated ones due to the original algorithm’s low capability in exploiting the optimal solutions and its convergence. From this principle, in this work, a novel variant of the Manta ray foraging optimizer has been proposed for global optimization problems, engineering design optimization problems, and multi-threshold segmentation. In the proposed approach, the fractional calculus (FC) using Caputo fractional differ-sum operator has been adopted to enhance the manta rays movement in the exploitation phase via utilizing history dependency of FC to boost exploiting the optimal solutions via sharing the past knowledge over the optimization process. Moreover, to avoid premature convergence, the somersault factor has been adaptively tuned. The fractional-order Caputo Manta-Ray Foraging Optimizer (FCMRFO) has been proposed. The proposed algorithm’s sensitivity for the FC coefficients has been tested with ten-dimensional CEC2017 benchmarks. The scalability test of the proposed algorithm has been performed with 30, 50 and 100-dimensional CEC2017. Moreover, CEC2020 benchmarks with dimensions 5 and 20 have been applied for providing an extensive investigation, and the FCMRFO has been compared with recent state-of-the-art algorithms. Through utilizing the non-parametric statistical analysis and ranking test, the FCMRFO confirms its superiority and ability to avoid the local optimum in several cases. For the second part of the study, three constrained engineering design problems have been investigated; then, numerous natural images are applied to appraise the FCMRFO for multilevel threshold image segmentation. By performing several metrics, the FCMRFO proves its quality and efficiency compared to recent well-regarded algorithms in engineering applications and image segmentation. © 2021
Parameter Identification of Li-ion Batteries: A Comparative Study
Lithium-ion batteries are crucial building stones in many applications. Therefore, modeling their behavior has become necessary in numerous fields, including heavyweight ones such as electric vehicles and plug-in hybrid electric vehicles, as well as lightweight ones like sensors and actuators. Generic models are in great demand for modeling the current change over time in real-time applications. This paper proposes seven dynamic models to simulate the behavior of lithium-ion batteries discharging. This was achieved using NASA room temperature random walk discharging datasets. The efficacy of these models in fitting different time-domain responses was tested through parameter identification with the Marine Predator Algorithm (MPA). In addition, each model’s term’s impact was analyzed to understand its effect on the fitted curve. The proposed models show an average absolute normalized error as low as (Formula presented.). © 2023 by the authors.
A generalized framework for elliptic curves based PRNG and its utilization in image encryption
In the last decade, Elliptic Curves (ECs) have shown their efficacy as a safe fundamental component in encryption systems, mainly when used in Pseudorandom Number Generator (PRNG) design. This paper proposes a framework for designing EC-based PRNG and maps recent PRNG design techniques into the framework, classifying them as iterative and non-iterative. Furthermore, a PRNG is designed based on the framework and verified using the National Institute of Standards and Technology (NIST) statistical test suite. The PRNG is then utilized in an image encryption system where statistical measures, differential attack measures, the NIST statistical test suite, and system key sensitivity analysis are used to demonstrate the system’s security. The results are good and promising as compared with other related work. © 2022, The Author(s).
Modified fractional-order model for biomass degradation in an up-flow anaerobic sludge blanket reactor at Zenein Wastewater Treatment Plant
This paper presents a modified fractional-order model (FOM) for microorganism stimulation in an up-flow anaerobic sludge blanket (UASB) reactor treating low-strength wastewater. This study aimed to examine the famine period of methanogens due to biomass accumulation in the UASB reactor over long time periods at a constant organic loading rate (OLR). This modified model can investigate the substrate biodegradation in a UASB reactor while considering substrate diffusion into biological granules during the feast and famine periods of methanogens. The Grünwald-Letnikov numerical technique was used to indicate the effect of biomass degradation on the biogas production rate and substrate biodegradation in a UASB reactor installed at Zenein Wastewater Treatment Plant (WWTP) in Giza, Egypt. Several fractional orders were applied in the dynamic model at biomass concentrations of 20 and 4 kg/ m3 in the reactor bed and blanket zones, respectively. An OLR of 0.9 kgCOD/ m3/ day using the calibrated kinetic parameters at 11 ?C was applied to comply with the experimental outcomes. The simulation results indicate that the removal efficiency of chemical oxygen demand (COD) was maintained at approximately 55 – 65 % , whereas the biogas production rate declined from 0.35 to 0.05 m3CH4/ kgCODr in the reactor bed zone due to a decline in food to microorganism (F/M) ratio from 0.04 to 0.018 d- 1 during the sludge retention time (SRT) in the UASB reactor. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Artificial Neural Network Chaotic PRNG and simple encryption on FPGA
Artificial Neural Networks (ANNs) are remarkably able to fit complex functions, making them useful in various applications and systems. This paper uses ANN to fit the Pehlivan–Uyaroglu Chaotic System (PUCS) to produce an Artificial Neural Network Chaotic Pseudo-Random Number Generator (ANNC-PRNG). The proposed PRNG imitates the PUCS chaotic system’s properties and attractor shape. The proposed ANNC-PRNG is implemented in a simple image encryption system on the Xilinx Kintex-7 Genesys 2 Field Programmable Gate Array (FPGA) board. Hardware realization of an ANN trained on chaotic time series has not been presented before. The proposed ANN can be used for different numerical methods or chaotic systems, including fractional-order systems while keeping the same resources despite the methodsÂ’ complexity or chaotic systemsÂ’ complexity. Extensive testing for the ANNC-PRNG was done to prove the randomness of the produced outputs. The proposed ANNC-PRNG and the encryption system passed various well-established security and statistical tests and produced good results compared to recent similar research. The encryption system is robust against different attacks. The proposed hardware architecture is fast as it reaches a maximum frequency of 12.553 MHz throughput of 301 Mbit/s. © 2023 Elsevier Ltd
Review of activated carbon adsorbent material for textile dyes removal: Preparation, and modelling
Water contamination with colours and heavy metals from textile effluents has harmed the ecology and food chain, with mutagenic and carcinogenic effects on human health. As a result, removing these harmful chemicals is critical for the environment and human health. Various standard physicochemical and biological treatment technologies are used; however, there are still some difficulties. Adsorption is described as a highly successful technology for removing contaminants from textile-effluents wastewater compared to other methods. Several adsorbent materials, including nanomaterials, natural materials, and biological biomasses, are identified as effective adsorbents for textile effluents. Activated carbon preparation from these different adsorbents is an excellent pre-treatment to remove the adsorption capacity. Therefore, through this study various adsorbent types, especially activated carbon adsorbents will be discussed in addition to the factors affecting adsorption and models applied for optimising the adsorption process. © 2022
An Efficient Multi-Secret Image Sharing System Based on Chinese Remainder Theorem and Its FPGA Realization
Multi-Secret Image Sharing (MSIS) is important in information security when multiple images are shared in an unintelligible form to different participants, where the images can only be recovered using the shares from participants. This paper proposes a simple and efficient ( n,n )-MSIS system for colored images based on XOR and Chinese Remainder Theorem (CRT), where all the n share are required in the recovery. The system improves the security by adding dependency on the input images to be robust against differential attacks, and by using several delay units. It works with even and odd number of inputs, and has a long sensitive system key design for the CRT. Security analysis and a comparison with related literature are introduced with good results including statistical tests, differential attack measures, and key sensitivity tests as well as performance analysis tests such as time and space complexity. In addition, Field Programmable Gate Array (FPGA) realization of the proposed system is presented with throughput 530 Mbits/sec. Finally, the proposed MSIS system is validated through software and hardware with all statistical analyses and proper hardware resources with low power consumption, high throughput and high level of security. © 2013 IEEE.
Correction to: Modified fractional-order model for biomass degradation in an up-flow anaerobic sludge blanket reactor at Zenein Wastewater Treatment Plant (Environmental Science and Pollution Research, (2022), 29, 17, (25980-25986), 10.1007/s11356-022-18797-9)
The 6th Nomenclature should be CXin, CSin. The Original article has been corrected. © Springer-Verlag GmbH Germany, part of Springer Nature 2022.
CNTFET-Based Ternary Multiply-and-Accumulate Unit
Multiply-Accumulate (MAC) is one of the most commonly used operations in modern computing systems due to its use in matrix multiplication, signal processing, and in new applications such as machine learning and deep neural networks. Ternary number system offers higher information processing within the same number of digits when compared to binary systems. In this paper, a MAC is proposed using a CNTFET-based ternary logic number. Specifically, we build a 5-trit multiplier and 10-trit adder as building blocks of two ternary MAC unit designs. The first is a basic MAC which has two methods to implement, serial and pipeline. The second is an improved MAC design that optimizes the number of transistors, offers higher performance and lower power consumption. The designed MAC unit can operate up to 300MHz. Finally, a comparative study in terms of power, delay, and area variations is conducted under different supply voltages and temperature levels. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Potentials of algae-based activated carbon for the treatment of M.orange in wastewater
Activated carbon is a promising material with high efficiency in dye removal from polluted wastewater. However, commercial activated carbon is expensive and generates black color in the medium. Therefore, searching for low-cost, eco-friendly activated carbon sources such as agricultural wastes and algal biomasses is essential. Hence, this study is directed to prepare the physical and the H3PO4 chemical activated carbon from the algae ”Sargassum dent folium” and the raw algae itself and apply it for Methyl Orange (M. orange) removal from contaminated wastewater and compare its performance with the commercial activated carbon. First, adsorbent materials are prepared and involved in the optimization process for M. orange removal using some preliminary experiments, followed by Response Surface Method-ology (RSM) and Artificial Neural Network (ANN). Finally, Isotherm and kinetics are studied to explain the adsorption mechanism. In contrast to other materials, results show that physical algae-activated carbon achieves the maximum removal efficiency of 96.687%. These results are obtained from ANN combined with Moth Search Algorithm (MSA), representing the most effective model for achieving the highest M. orange removal efficiency from Physical algae activated carbon. In the algae case, the best experimental and predicted removal efficiencies are 85.9407 RE%, 88.5 indicated RSM RE%, and 85.9431 predicted ANN RE%. The best observed and predicted removal efficiencies for the H3PO4 chemical activated carbon are 89.6157 RE%, 82.38 predicted RSM RE%, and 89.5442 predicted ANN RE%. The best experimental and predicted removal efficiencies for the physical-activated carbon are 94.7935 RE%, 95.49 indicated RSM RE%, and 95.4298 predicted ANN RE%. The best observed and predicted removal efficiencies for the commercial-activated carbon are 92.2659 RE%, 96.65 predicted RSM RE%, and 92.2658 predicted ANN RE%. In the algae case, the best experimental and predicted removal efficiencies are 85.9407 %RE, 88.5 predicted RSM RE %, and 85.9431 expected ANN RE%. For the H3PO4 chemical activated carbon, the best experimental and predicted removal efficiencies are 89.6157%RE, 82.38 indicated RSM RE%, and 89.5442 predicted ANN RE%. For the physical-activated carbon, the best observed and predicted removal efficiencies are 94.7935 %RE, 95.49 predicted RSM RE%, and 95.4298 indicated ANN RE%. For the commercial-activated carbon, the best experimental and predicted removal efficiencies are 92.2659 %RE, 96.65 predicted RSM RE%, and 92.2658 predicted ANN RE%. This study intends to treat industrial wastewater contaminated with the anionic M. orange dye using raw algae and their generated activated carbon (physical and chemical forms), which are economical. It then compares the results to the effectiveness of commercial activated carbon. In the state of the raw algae, Temkin and Langmuir isotherm models best suit the data, while Temkin agrees well with the data from physical-activated carbon. Temkin and Freundlich’s models are fitted with the H3PO4 chemical activated carbon. The model that fits the raw algae physically activated carbon and H3PO4 chemical-activated carbon the best is pseudo-second-order kinetics. Future research could examine the produced activated carbon-based algae’s capacity to extract more contaminants from contaminated wastewater. This study intends to treat industrial wastewater contaminated with the anionic M. orange dye using raw algae and their generated activated carbon (physical and chemical forms), which are economical. It next compares the results to the effectiveness of commercial activated carbon. © 2023 The Authors