Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such ...as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed.
Effective adoptive T cell therapy (ACT) comprises the killing of cancer cells through the therapeutic use of transferred T cells. One of the main ACT approaches is chimeric antigen receptor (CAR) T ...cell therapy. CAR T cells mediate MHC-unrestricted tumor cell killing by enabling T cells to bind target cell surface antigens through a single-chain variable fragment (scFv) recognition domain. Upon engagement, CAR T cells form a non-classical immune synapse (IS), required for their effector function. These cells then mediate their anti-tumoral effects through the perforin and granzyme axis, the Fas and Fas ligand axis, as well as the release of cytokines to sensitize the tumor stroma. Their persistence in the host and functional outputs are tightly dependent on the receptor's individual components-scFv, spacer domain, and costimulatory domains-and how said component functions converge to augment CAR T cell performance. In this review, we bring forth the successes and limitations of CAR T cell therapy. We delve further into the current understanding of how CAR T cells are designed to function, survive, and ultimately mediate their anti-tumoral effects.
To improve the quality of service (QoS) needed by several applications areas, the Internet of Things (IoT) tasks are offloaded into the fog computing instead of the cloud. However, the availability ...of ongoing energy heads for fog computing servers is one of the constraints for IoT applications because transmitting the huge quantity of the data generated using IoT devices will produce network bandwidth overhead and slow down the responsive time of the statements analyzed. In this article, an energy-aware model basis on the marine predators algorithm (MPA) is proposed for tackling the task scheduling in fog computing (TSFC) to improve the QoSs required by users. In addition to the standard MPA, we proposed the other two versions. The first version is called modified MPA (MMPA), which will modify MPA to improve their exploitation capability by using the last updated positions instead of the last best one. The second one will improve MMPA by the ranking strategy based reinitialization and mutation toward the best, in addition to reinitializing, the half population randomly after a predefined number of iterations to get rid of local optima and mutated the last half toward the best-so-far solution. Accordingly, MPA is proposed to solve the continuous one, whereas the TSFC is considered a discrete one, so the normalization and scaling phase will be used to convert the standard MPA into a discrete one. The three versions are proposed with some other metaheuristic algorithms and genetic algorithms based on various performance metrics such as energy consumption, makespan, flow time, and carbon dioxide emission rate. The improved MMPA could outperform all the other algorithms and the other two versions.
Many countries are challenged by the medical resources required for COVID-19 detection which necessitates the development of a low-cost, rapid tool to detect and diagnose the virus effectively for a ...large numbers of tests. Although a chest X-Ray scan is a useful candidate tool the images generated by the scans must be analyzed accurately and quickly if large numbers of tests are to be processed. COVID-19 causes bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. In this work, we aim to extract rapidly from chest X-Ray images the similar small regions that may contain the identifying features of COVID-19. This paper therefore proposes a hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation. The ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations. RDR works on finding the particles that couldn't find better solutions within a consecutive number of iterations, and then moving those particles towards the best solutions so far. The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms: equilibrium optimizer (EO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), Harris-hawks algorithm (HHA), and salp swarm algorithms (SSA). The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics. In addition, the performance of our proposed model was convergent on all numbers of thresholds level in the Structured Similarity Index Metric (SSIM) and Universal Quality Index (UQI) metrics.
The proton exchange membrane fuel cell (PEMFC) is a favorable renewable energy source to overcome environmental pollution and save electricity. However, the mathematical model of the PEMFC contains ...some unknown parameters which have to be accurately estimated to build an accurate PEMFC model; this problem is known as the parameter estimation of PEMFC and belongs to the optimization problem. Although this problem belongs to the optimization problem, not all optimization algorithms are suitable to solve it because it is a nonlinear and complex problem. Therefore, in this paper, a new optimization algorithm known as the artificial gorilla troops optimizer (GTO), which simulates the collective intelligence of gorilla troops in nature, is adapted for estimating this problem. However, the GTO is suffering from local optima and low convergence speed problems, so a modification based on replacing its exploitation operator with a new one, relating the exploration and exploitation according to the population diversity in the current iteration, has been performed to improve the exploitation operator in addition to the exploration one. This modified variant, named the modified GTO (MGTO), has been applied for estimating the unknown parameters of three PEMFC stacks, 250 W stack, BCS-500W stack, and SR-12 stack, used widely in the literature, based on minimizing the error between the measured and estimated data points as the objective function. The outcomes obtained by applying the GTO and MGTO on those PEMFC stacks have been extensively compared with those of eight well-known optimization algorithms using various performance analyses, best, average, worst, standard deviation (SD), CPU time, mean absolute percentage error (MAPE), and mean absolute error (MAE), in addition to the Wilcoxon rank-sum test, to show which one is the best for solving this problem. The experimental findings show that MGTO is the best for all performance metrics, but CPU time is competitive among all algorithms.
Reports of the occurrence of lumbar vertebrae variants in horses in Trinidad are rare in the literatures. Parts of the skeletons of two horses of unknown age and sex that died in a horse farm in ...Trinidad and Tobago were brought to the Anatomy laboratory. It was reported that specimens of fused left transverse processes of the 5th and 6th lumbar vertebrae and a blunted left transverse process of the 6th lumbar vertebra in thoroughbred racehorses in Trinidad.
Separation of photogenerated charge carriers and lowering the band gap are essential in designing an efficient photocatalyst in visible light setup. Separation of charge carriers can be achieved via ...incorporation of a metal-oxide catalyst on the surface of a semiconductor. In this paper we report synthesis of WO3/g-C3N4 nanocomposite material starting with a mesoporous MCM-41 as a template to produce high surface area g-C3N4 with subsequent WO3 decoration. Various compositions were prepared (3–12% WO3 decoration) and tested for their photocatalytic efficiency utilizing water splitting reaction. Comparisons to the efficiencies of pristine g-C3N4 and WO3 reveal superior activity of the nanocomposites. 9% content of WO3 was determined to be the optimum concentration of the additive and showed 27.5 and 55 times more efficiency than that of WO3 and g-C3N4. Addition of glycerol as a positive hole scavenger helps to increase the efficiency of the catalytic system. The nanocomposite photocatalyst also showed stability upon reuse and remained efficient even after five runs. Major parameters affecting the photocatalytic activity of the WO3/g-C3N4 nanocomposite include its ability of charge separation during the photocatalytic process, its mesoporous structure, a band gap width in the visible region and its high surface area.
Using solar energy to reduce extremely noxious Cr(VI) into environmentally friendly Cr(III) presents a promising approach to remedy the contamination of heavy metals. Herein, CdS nanoparticles were ...dispersed onto the surface of a mesoporous Gd2O3 matrix to construct heterojunction CdS/Gd2O3 nanocomposites for the first time. Cr(VI) photoreduction was employed to determine the photocatalytic abilities of the CdS/Gd2O3 nanocomposites under visible illumination. All CdS/Gd2O3 photocatalysts exhibited superior photocatalytic abilities compared to pristine Gd2O3, and 9 % CdS/Gd2O3 exhibited the highest Cr(VI) photoreduction (100 %) within 30 min compared to pristine Gd2O3 (4 %) and other CdS/Gd2O3 nanocomposites. The Cr(VI) reduction rate over the 9 %CdS/Gd2O3 nanocomposite was 0.1243 min−1 faster than over pristine Gd2O3 (0.0019 min−1). The rate constant of the 9 %CdS/Gd2O3 nanocomposite was 65 times greater than that of pristine Gd2O3. After incorporating CdS onto mesoporous Gd2O3 nanocrystals, the photocurrent of Gd2O3 was significantly enhanced, while its photoluminescence response was substantially reduced, which indicated the enhanced separation efficiency of the charge carrier. This is explained by the synergistic phenomena between CdS and Gd2O3, effective construction p-n heterojunction, and the integration of enhanced charge transfer and effective visible absorption. Furthermore, mesoporous CdS/Gd2O3 nanocomposites presented stable photocatalytic ability during five repeated experiments. The promoted Cr(VI) reduction mechanism over the heterojunction CdS/Gd2O3 photocatalyst was addressed based on an S-scheme carrier transfer process. This research work provides high separation efficiency in charge carriers and the application of CdS/Gd2O3 nanocomposites in the photoreduction of Cr(VI) and oxidation of toxic organic pollutants.
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•Mesoporous heterojunction CdS/Gd2O3 nanocomposites was constructed.•Cr(VI) reduction was utilized to estimate the photocatalytic ability of CdS/Gd2O3.•9 % CdS/Gd2O3 exhibited the highest Cr(VI) reduction (100 %) within 30 min.•The rate of 9 %CdS/Gd2O3 was 65 times greater than that of pristine Gd2O3•There is no significant loss in Cr(VI) reduction after five successive cycles of runs.
•We suggest an enhancement for exploration and exploitation factors of the Equilibrium Optimizer (EO) to balance between exploration and exploitation operators in the proposed multi-objective ...equilibrium optimizer.•We propose an improvement-based reference points method increasing the diversity between the population members in multi-objective optimization problems.•The CEC 2020, CEC 2009, DTLZ, and ZDT test problems are solved using a multi-objective equilibrium optimizer algorithm.•Our proposed algorithm outperforms the other algorithms in terms of the spread and inverted generational distance measures for different test problems that satisfy all characteristics for optimization problems.
In this work, we explore a novel multi-objective optimization algorithm to identify a set of solutions that could be optimal for more than one task. The proposed approach is used to generate a set of solutions that balance the tradeoff between convergence and diversity in multi-objective optimization problems. Equilibrium Optimizer (EO) algorithm is a novel developed meta-heuristic algorithm inspired by the physics laws. In this paper, we propose a Multi-objective Equilibrium Optimizer Algorithm (MEOA) for tackling multi-objective optimization problems. We suggest an enhancement for exploration and exploitation factors of the EO algorithm to randomize the values of these factors with decreasing the initial value of the exploration factor with the iteration and increasing the exploitation factor to accelerate the convergence toward the best solution. To achieve good convergence and well-distributed solutions, the proposed algorithm is integrated with the Improvement-Based Reference Points Method (IBRPM). The proposed approach is applied to the CEC 2020, CEC 2009, DTLZ, and ZDT test functions. Also, the inverted generational and spread spacing metrics are used to compare the proposed algorithm with the most recent evolutionary algorithms. It's obvious from the results that the proposed algorithm is better in both convergence and diversity.
The remarkable success of chimeric antigen receptor (CAR)-engineered T cells in pre-B cell acute lymphoblastic leukemia (ALL) and B cell lymphoma led to the approval of anti-CD19 CAR T cells as the ...first ever CAR T cell therapy in 2017. However, with the number of CAR T cell-treated patients increasing, observations of tumor escape and resistance to CAR T cell therapy with disease relapse are demonstrating the current limitations of this therapeutic modality. Mechanisms hampering CAR T cell efficiency include limited T cell persistence, caused for example by T cell exhaustion and activation-induced cell death (AICD), as well as therapy-related toxicity. Furthermore, the physical properties, antigen heterogeneity and immunosuppressive capacities of solid tumors have prevented the success of CAR T cells in these entities. Herein we review current obstacles of CAR T cell therapy and propose strategies in order to overcome these hurdles and expand CAR T cell therapy to a broader range of cancer patients.
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