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The widespread utilization of direct current (DC) motors in real-life engineering applications has led to the need for precise speed control, making controllers a crucial aspect of DC motor systems. Proportional-integral-derivative (PID) controllers have been widely adopted due to their simplicity and effectiveness. However, recent advancements have introduced fractional order PID (FOPID) controllers that offer improved control performance for complex systems with nonlinear dynamics. To fully leverage FOPID controller’s benefits, an efficient tuning method is essential. In this study, we propose artificial rabbits optimization (ARO) algorithm with enhanced strategies, called IARO, to optimize the FOPID controller for DC motor speed regulation. The IARO algorithm incorporates an adaptive local search (ALS) mechanism and an experience-based perturbed learning (EPL) strategy, addressing the shortcomings of ARO and providing better exploration–exploitation balance. We validate the superiority of IARO over competitive algorithms on the CEC2020 benchmark functions, showcasing improved solution stability and consistency. The IARO algorithm is then applied to tune the FOPID controller for DC motor speed regulation. The problem is formulated as a constraint minimization task, optimizing the integral of time-weighted absolute error cost function while adhering to critical design requirements. Comparative simulations demonstrate the IARO algorithm’s ability to achieve superior cost function values and faster convergence compared to other algorithms' based FOPID controllers. The IARO-based FOPID controller exhibits enhanced stability, smoother speed response, larger gain margin, and wider bandwidth compared to other reported algorithms. Additionally, a hardware implementation is also conducted to further validate the practical applicability of IARO based design method. The IARO-based FOPID controller showed remarkable accuracy in tracking multi-step reference inputs and robustly rejected external disturbances, outperforming other recent optimization-based controllers. Additionally, the IARO-based PID controller achieved better performance in key time-domain metrics, including lower overshoot, faster rise time, shorter settling time, and minimized peak time.

Harnessing the potential of solar photovoltaic (PV) technology relies heavily on accurately estimating the model parameters of PV cells/modules using real current-voltage (I-V) data. Achieving optimal parameter values is essential for the performance and efficiency of PV systems, necessitating the use of advanced optimization techniques. In our endeavor, we introduce a multi-strategy improvement approach for the Runge Kutta (RUN) optimizer, a cutting-edge tool used for tackling this critical task in both single-diode and double-diode PV unit models. By aligning experimental and model-based estimated data, our approach seeks to reduce errors and improve the accuracy of PV system performance. We conduct meticulous analyses of two compelling case studies and the CEC 2020 test suite to showcase the versatility and effectiveness of our improved RUN (IRUN) algorithm. The first case study involves a standard dataset derived from the well-known R.T.C. France silicon solar cell, where IRUN performs favorably compared to competing methods, demonstrating its effectiveness. IRUN effectively manages the complex task of defining model parameters for an industrial PV module situated at the Engineering Faculty of Düzce University in Turkey. The real-world I-V data, obtained under optimal conditions with a temperature of 30°C and solar radiance of 1000W/m2, provide strong evidence of the practical applicability and real-world benefits of our innovative method. Additional analyses through three-diode and PV module models further confirm the efficacy of the IRUN. A mean absolute error of down to 6.5E-04 and root mean square error of down to 7.3668E-04 are achieved. Our approach provides a practical and efficient tool for improving the accuracy of PV systems, enhancing their performance when compared to existing methods.

Sustainability goals include the utilization of renewable energy resources to supply the energy needs in addition to wastewater treatment to satisfy the water demand. Moreover, hydrogen has become a promising energy carrier and green fuel to decarbonize the industrial and transportation sectors. In this context, this research investigates a wind-photovoltaic power plant to produce green hydrogen for hydrogen refueling station and to operate an electrocoagulation water treatment unit in Ostrava, Czech Republic's northeast region. The study conducts a techno-economic analysis through HOMER Pro® software for optimal sizing of the power station components and to investigate the economic indices of the plant. The power station employs photovoltaic panels and wind turbines to supply the required electricity for electrolyzers and electrocoagulation reactors. As an off-grid system, lead acid batteries are utilized to store the surplus electricity. Wind speed and solar irradiation are the key role site dependent parameters that determine the cost of hydrogen, electricity, and wastewater treatment. The simulated model considers the capital, operating, and replacement costs for system components. In the proposed system, 240 kg of hydrogen as well as 720 kWh electrical energy are daily required for the hydrogen refueling station and the electrocoagulation unit, respectively. Accordingly, the power station annually generates 6,997,990 kWh of electrical energy in addition to 85595 kg of green hydrogen. Based on the economic analysis, the project's NPC is determined to be €5.49 M and the levelized cost of Hydrogen (LCH) is 2.89 €/kg excluding compressor unit costs. This value proves the effectiveness of this power system, which encourages the utilization of green hydrogen for fuel-cell electric vehicles (FCVs). Furthermore, emerging electrocoagulation studies produce hydrogen through wastewater treatment, increasing hydrogen production and lowering LCH. Therefore, this study is able to provide practicable methodology support for optimal sizing of the power station components, which is beneficial for industrialization and economic development as well as transition toward sustainability and autonomous energy systems.

This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabilities, making it well-suited for tuning the controller. The proposed approach utilizes a master–slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control system's performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a comprehensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.

Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD2) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD2 controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD2 controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness.

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles.

Abstract In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.

Naglaa S Elabd,1 Safaa I Tayel,2 Moamena S Elhamouly,1 Shaimaa A Hassanein,3 Samar M Kamaleldeen,4 Fatma E Ahmed,5 Mahmoud Rizk,6 Abdelnaser A Gadallah,7 Soma E Ajlan,8 Ahmed S Sief9 1Tropical Medicine Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt; 2Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt; 3Diagnostic Radiology Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt; 4Clinical Pathology Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt; 5Clinical Pharmacology Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt; 6Internal Medicine Department, Faculty of Medicine, Banha University, Banha, Egypt; 7Internal Medicine Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt; 8Microbiology and Immunology Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt; 9Hepatology and Gastroenterology Department, Shebin Elkom Teaching Hospital, Menoufia, EgyptCorrespondence: Safaa I TayelMedical Biochemistry and Molecular Biology Department, Faculty of Medicine, Menoufia University, Shebin El-Kom, Menoufia, 32511, EgyptTel +20 1003383097Email drsafaa_tayel@yahoo.comBackground: Treatment response to antiviral drugs is a challenging issue in patients with chronic hepatitis C virus (HCV) infection. Although microRNA-122 represents the majority of the microRNA content in hepatic tissues, few studies have evaluated its role in the treatment response, so we aimed to study its role in chronic HCV patients and in predicting the treatment response to direct-acting antivirals (DAAs).Methods: The study included 125 chronic HCV patients (89 naïve and 36 with a prior failed peginterferon/ribavirin response) and 50 apparently healthy subjects. Complete blood count, liver function, α-fetoprotein, lipid profiles, serum creatinine, abdominal ultrasound, and FibroScan® were assessed. Viral markers, HCV antibodies, and hepatitis B surface antigen were measured by enzyme-linked fluorescent immunoassay, with quantitative estimation of HCV RNA and microRNA-122 levels by real-time PCR.Results: The microRNA-122 level in HCV patients (those with a sustained virologic response 12 weeks after finishing therapy [SVR12] and non-responders) was significantly increased compared with controls and expressed more in non-responders versus SVR12 (p=0.042). ROC curve analysis of microRNA-122 for differentiating HCV patients from healthy controls revealed that a cut-off point of > 1.45 had a sensitivity of 67.20%, specificity of 94.0%, AUC=0.861, and p< 0.001; and for predicting response to treatment a cut-off point ≤ 5.66 could significantly (p=0.022) predict the occurrence of SVR, with a sensitivity of 60.34%, specificity of 66.67%, and AUC=0.729. Logistic regression analysis showed significant values for microRNA-122 in multivariate and univariate analysis for the prediction of response to DAAs.Conclusion: The results demonstrated the possible function of microRNA-122 as an indicative tool for distinguishing chronic HCV patients from controls and in the assessment of the therapeutic reaction to DAAs.Keywords: CHC, FibroScan, microRNA-122, DAAs

Identifying the parameters of photovoltaic (PV) modules is significant for their design and simulation. Because of the instabilities in the weather action and land surface of the earth, which cause errors in measuring, a novel fuzzy represen-tation-based PV module is formulated and developed. In this paper, a novel locomotion-based hybrid salp swarm algorithm (LHSSA) is presented to identify the parameters of PV modules accurately and reliably. In the LHSSA, better leader salps based on particle swarm optimization (PSO) are incorporated to the traditional salp swarm algorithm (SSA) in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA. By this integration, the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region. The proposed LHSSA is investigated on different PV models, i. e., single-diode (SD), double-diode (DD), and PV module in crisp and fuzzy aspects. By comparing with different algorithms, the comprehensive results affirm that the LHSSA can achieve a highly competitive performance, especially on quality and reliability.

Motorcycles are a common mode of transport. They are used both for a conventional purpose - as a way to cover the distance from A to B, but more and more often they are a way of recreation. The choice of bikes is huge on the market: from scooters, threw small city bikes, to high-speed performance or racing machines. High speeds is closely related to high kinetic energy of movement. In case of emergency stopping, the brakes may overheat and cause fading. In this study, it was decided to check how the initial speed affects the motorcycle brake heating process. FEM was used for this purpose.

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.

Cryptography protects privacy and confidentiality. So, it is necessary to guarantee that the ciphers used are secure and cryptanalysis-resistant. In this paper, a new state recovery attack against the RC4 stream cipher is revealed. A plaintext attack is used in which the attacker has both the plaintext and the ciphertext, so they can calculate the keystream and reveal the cipher’s internal state. To increase the quality of answers to practical and recent real-world global optimization difficulties, researchers are increasingly combining two or more variations. PSO and EO are combined in a hybrid PSOEO in an uncertain environment. We may also convert this method to its binary form to cryptanalyze the internal state of the RC4 cipher. When solving the cryptanalysis issue with HBPSOEO, we discover that it is more accurate and quicker than utilizing both PSO and EO independently. Experiments reveal that our proposed fitness function, in combination with HBPSOEO, requires checking 104 possible internal states; however, brute force attacks require checking 2128 states.

Malak Rizk, Şükrü Tüzmen Molecular Biology and Genetics Program, Department of Biological Sciences, Faculty of Arts and Sciences, Eastern Mediterranean University (EMU), Famagusta, North Cyprus, Turkey Abstract: RNA interference (RNAi) is a naturally existing endogenous mechanism for posttranscriptional gene regulation, nowadays commonly utilized for functional characterization of genes and development of potential treatment strategies for diseases. RNAi-based studies for therapy, after being examined for over a decade, are finally in the pipeline for developing a potential treatment for the mutated transthyretin (TTR) gene, which gives rise to a dysfunctional TTR protein. This dysfunctional protein causes TTR amyloidosis (ATTR), an inherited, progressively incapacitating, and often fatal genetic disorder. TTR is a protein produced in the liver, and functions as a carrier for retinol-binding protein and also thyroxine. This protein facilitates the transport of vitamin A around the human body. A mutation or misprint in the code of this protein results in an abnormal folding of the protein. Therefore, not only does the transportation of the vitamin A become disabled, but also there will be formation of clusters called amyloid deposits, which attack the heart and the nerves causing some patients to be unconditionally bound to bed. ATTR is a hereditary autosomal dominant disease with a 50% chance of inheritance by offspring, even with just one of the parents having a single defective allele of this gene. Alnylam Pharmaceuticals worked on the concept of RNAi therapy for years, which led to the introduction of lipid nanoparticles encircling small interfering RNAs. The drug showed extremely positive results since the first trial, and a great percentage of defective protein reduction. This drug was later named Patisiran. Keywords: RNAi, TTR gene, TTR amyloidosis, siRNA, gene silencing, Patisiran

Hybrid rice parental lines with better combining abilities provide an efficient tool to increase rice production. In the current study, twenty hybrid combinations were generated from five aromatic cytoplasmic male sterile (CMS) lines and four restorer lines (three of them aromatic) using a line × tester mating design. The hybrids and their parental lines were evaluated under two water regimes: normal irrigation and water-stress. Ten yield-component traits were studied over a period of 2 years, and the significant differences between the parents and hybrids are reported in this investigation. Overall, all yield component traits were significantly affected by the water deficit and were governed by both additive and non-additive gene actions. More specifically, the grain yield (GY) was mainly controlled by non-additive gene action under both normal and water-stress conditions. The contribution of the additive variance (σ2 A) was more prominent in the genetic components of traits as compared to the dominance variance (σ2 D). The aromatic parental line CMS IR58025A and the restorer line PR2 were recorded as the best combiners for the GY and good combiners for many other characteristics under both growth conditions. The cross combinations Pusa12A/IR25571-31R and Pusa12A/Giza-Basmati-201 revealed significantly positive specific combining ability (SCA) effects for the GY under both normal and water-stress conditions. The inconsistent correlation between the general combining ability (GCA) and SCA manifested complex interactions among the positive and negative alleles of the genes controlling the yield traits. Generally, the findings of this investigation demonstrated the importance of the GCA and SCA for understanding the genetic components and gene actions of the yield characteristics in new aromatic hybrid rice parental lines. Therefore, we recommend considering these findings in the selection of elite parents for developing superior aromatic hybrid rice varieties under water-stress conditions.