In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks ...optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.
This paper presents a novel method for optimal tunning of a Fractional Order Proportional-Integral-Derivative (FOPID) controller for an Automatic Voltage Regulator (AVR) system. The presented method ...is based on the Yellow Saddle Goatfish Algorithm (YSGA), which is improved with Chaotic Logistic Maps. Additionally, a novel objective function for the optimization of the FOPID parameters is proposed. The performance of the obtained FOPID controller is verified by comparison with various FOPID controllers tuned by other metaheuristic algorithms. A comparative analysis is performed in terms of step response, frequency response, root locus, robustness test, and disturbance rejection ability. Results of the simulations undoubtedly show that the FOPID controller tuned with the proposed Chaotic Yellow Saddle Goatfish Algorithm (C-YSGA) outperforms FOPID controllers tuned by other algorithms, in all of the previously mentioned performance tests.
An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as ...GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.
The identification of parameters in solar cell models continue being an important issue in the simulation and design of the photovoltaic systems (PV). The models commonly used are based on diodes, ...the most important models are the three diode model; double diode model; and single diode model. Therefore, an optimization problem of the parameter extraction of these models can be treated with an objective function to minimize the difference between the calculated data and the measured data. In order to deal with parameter extraction in PV, several traditional numerical analytical and hybrid models have been developed. Recently, the meta-heuristic optimization algorithms (MHs) have been used to overcome the complex to find with proper accuracy, highly credible results quickly. Therefore, this paper introduces a modification of the basic three PV models and an innovative objective function is also considered. Moreover, a recent meta-heuristic algorithm called Heap-based optimizer (HBO) is applied for extracting the PV parameters of the traditional and the modified three PV models including three diode, double diode and single diode. Comparison between the traditional three photovoltaic models and the modified three photovoltaic models is performed in this work based on the innovative objective function. The experimental results revealed that the HBO superiority over other competitor algorithms. Based on the results, the values of estimated parameters that achieved by HBO are the optimal values with the smallest error between calculated data and measured data.
Solar radiation is increasingly used as a clean energy source, and photovoltaic (PV) panels that contain solar cells (SCs) transform solar energy into electricity. The current-voltage characteristics ...for PV models is nonlinear. Due to a lack of data on the manufacturer's datasheet for PV models, there are several unknown parameters. It is necessary to accurately design the PV systems by defining the intrinsic parameters of the SCs. Various methods have been proposed to estimate the unknown parameters of PV cells. However, their results are often inaccurate. In this article, a gradient-based optimizer (GBO) was applied as an efficient and accurate methodology to estimate the parameters of SCs and PV modules. Three common SC models, namely, single-diode models (SDMs), double-diode models (DDMs), and three-diode models (TDMs) were used to demonstrate the capacity of the GBO to estimate the parameters of SCs. The proposed GBO algorithm for estimating the optimal values of the parameters for various SCs models are applied on the real data of a 55 mm diameter commercial R.T.C-France SC. Comparison between the GBO and other algorithms are performed for the same data set. The smallest value of the error between the experimental and the simulated data is achieved by the proposed GBO. Also, high closeness between the simulated P-V and I-V curves is achieved by the proposed GBO compared with the experimental.
Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm defined by the presence of t(9;22) translocation whose origin has been associated with the tridimensional genome organization. This ...rearrangement leads to the fusion of BCR and ABL1 genes giving rise to a chimeric protein with constitutive kinase activity. Imatinib, a tyrosine kinase inhibitor (TKI), is used as a first‐line treatment for CML, though ~40% of CML patients do not respond. Here, using structured illumination microscopy (SIM) and 3D reconstruction, we studied the 3D organization patterns of the ABL1 and BCR genes, and their chromosome territories (CTs) CT9 and CT22, in CD34+ cells from CML patients that responded or not to TKI. We found that TKI resistance in CML is associated with high levels of structural disruption of CT9 and CT22 in CD34+ cells, increased CT volumes (especially for CT22), intermingling between CT9 and CT22, and an open‐chromatin epigenetic mark in CT22. Altogether our results suggest that large‐scale disruption of CT9 and CT22 correlates with the clinical response of CML patients, which could be translated into a potential prognostic marker of response to treatment in this disease and provide novel insights into the mechanisms underlying resistance to TKI in CML.
What's new?
The t(9;22) translocation and resulting encoding of a chimeric protein with constitutive tyrosine kinase activity is a hallmark of chronic myeloid leukaemia (CML). The genesis of this rearrangement is related to the topological organization of chromatin in the nucleus. Using super‐resolution microscopy, the authors studied the topological features of bone marrow stem cells from CML patients. They found that disruption of chromosome territories 9 and 22 associates with non‐response to tyrosine kinase inhibitors in CML. The results suggest a novel prognostic marker and provide new insights into the mechanisms underlying treatment resistance in CML and regulation of the 3D genome organization.
Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and ...removing irrelevant and redundant data to provide good classification results. FS aims to reduce the dimensionality and improve the classification accuracy that is generally utilized with great importance in different fields such as pattern classification, data analysis, and data mining applications. The main problem is to find the best subset that contains the representative information of all the data. In order to overcome this problem, two binary variants of the whale optimization algorithm (WOA) are proposed, called bWOA-S and bWOA-V. They are used to decrease the complexity and increase the performance of a system by selecting significant features for classification purposes. The first bWOA-S version uses the Sigmoid transfer function to convert WOA values to binary ones, whereas the second bWOA-V version uses a hyperbolic tangent transfer function. Furthermore, the two binary variants introduced here were compared with three famous and well-known optimization algorithms in this domain, such as Particle Swarm Optimizer (PSO), three variants of binary ant lion (bALO1, bALO2, and bALO3), binary Dragonfly Algorithm (bDA) as well as the original WOA, over 24 benchmark datasets from the UCI repository. Eventually, a non-parametric test called Wilcoxon’s rank-sum was carried out at 5% significance to prove the powerfulness and effectiveness of the two proposed algorithms when compared with other algorithms statistically. The qualitative and quantitative results showed that the two introduced variants in the FS domain are able to minimize the selected feature number as well as maximize the accuracy of the classification within an appropriate time.
This work addresses the optimal design of a flexible heat exchanger network using model-based optimization, applied to hydrogen production by means of an ethanol steam reforming process. High ...efficiencies are obtained at different hydrogen production levels ranging from 25 to 100% of a nominal output. System structure, heat exchanger sizing, and operation conditions are simultaneously settled, ensuring both operational feasibility and optimality. The system involves a reforming reactor, vaporization and reheating equipment, combustors, and a heat exchanger network system. A multi-period nonlinear optimization problem (NLP) was formulated to account for the production level distribution. Equipment sizing constraints and structural constraints link the different scenarios. The trade-off between area and efficiency is analyzed using a multi-objective epsilon-constraint approach. Models were developed in the GAMS environment. The resulting solutions, for the maximum area case, maintain alcohol combustion at low levels showing efficiencies around 63% in each operational level. Pareto Optimal diagram shows that a 1% reduction of efficiency allows a 50% decrease in total required heat exchanger area by 50%.
•Flexible design of the heat exchanger network for an Ethanol Fuel Processor.•Ethanol processor multi-period non-linear model formulation.•Optimization of operative variables and heat exchanger sizing.•Multi-objective epsilon-constraint optimization approach.•Heat transfer network with phase change.
Odontology, as a scientific discipline, continuously collaborates with biomaterials engineering to enhance treatment characteristics and patients' satisfaction. Endodontics, a specialized field of ...dentistry, focuses on the study, diagnosis, prevention, and treatment of dental disorders affecting the dental pulp, root, and surrounding tissues. A critical aspect of endodontic treatment involves the careful selection of an appropriate endodontic sealer for clinical use, as it significantly influences treatment outcomes. Traditional sealers, such as zinc oxide-eugenol, fatty acid, salicylate, epoxy resin, silicone, and methacrylate resin systems, have been extensively used for decades. However, advancements in endodontics have given rise to bioceramic-based sealers, offering improved properties and addressing new challenges in endodontic therapy. In this review, a classification of these materials and their ideal properties are presented to provide evidence-based guidance to clinicians. Physicochemical properties, including sealing ability, stability over time and space, as well as biological properties such as biocompatibility and antibacterial characteristics, along with cost-effectiveness, are essential factors influencing clinicians' decisions based on individual patient evaluations.
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