Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications...Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.展开更多
As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage p...As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.展开更多
In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has ...In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has enforced the diagnosis,staging,management of liver diseases beside integration into transplantation.This review highlights the evolving discipline of“endo-hepatology”,where endoscopic ultrasound,endoscopic retrograde cholangiopancreatography,and novel interventional tools are employed to address the critical challenges in chronic liver disease.The review provides a comprehensive synthesis of current evidence and different clinical applications,while also exploring future directions including revolution of artificial intelligence-assisted endoscopies and enhanced imaging endoscopies.By bridging the anatomical and functional interface between the gastrointestinal lumen and the liver,endo-hepatology is not only improving diagnostic accuracy and therapeutic precision but also reshaping multidisciplinary paradigms in hepatology practice.展开更多
This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Select...This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.展开更多
To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the ...To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.展开更多
This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with ...This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.展开更多
Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling...Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.展开更多
Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid ap...Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid approach integrating Long Short-Term Memory(LSTM)networks with the Hybrid Greylag Goose and Particle Swarm Optimization(GGPSO)algorithm to optimize preterm birth classification using Electrohysterogram signals.The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings,capturing key physiological features such as contraction patterns,entropy,and statistical variations.Statistical analysis and feature selection methods are applied to identify the most relevant predictors and enhance model interpretability.LSTM networks effectively capture temporal patterns in uterine activity,while the GGPSO algorithm finetunes hyperparameters,mitigating overfitting and improving classification accuracy.The proposed GGPSO-optimized LSTM model achieved superior performance with 97.34%accuracy,96.91%sensitivity,97.74%specificity,and 97.23%F-score,significantly outperforming traditional machine learning approaches and demonstrating the effectiveness of hybrid metaheuristic optimization in enhancing deep learning models for clinical applications.By combining deep learning withmetaheuristic optimization,this study contributes to advancing intelligent auto-diagnosis systems,facilitating early detection of pretermbirth risks and timely medical interventions.展开更多
Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants,as it directly impacts operational costs and emissions in light of energy transition goals.This study sets it...Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants,as it directly impacts operational costs and emissions in light of energy transition goals.This study sets itself apart from existing research by applying a novel optimization technique to a basic ideal Rankine cycle,focusing on a specific power plant that has not been previously analyzed.Currently,this cycle operates at 41%efficiency and a steam quality of 76%,constrained by fixed operational parameters.The primary objectives are to increase thermal efficiency beyond 46%and raise steam quality above 85%,while adhering to operational limits:a boiler pressure not exceeding 15 MPa,condenser pressure not dropping below 10 kPa,and turbine temperature not surpassing 500℃.This study utilizes numerical simulations to model the effects of varying boiler pressure(Pb)and condenser pressure(Pc)within the ranges of 12MPa<Pb<15 MPa and 5 kPa<Pc<10 kPa.By systematically adjusting these parameters,the proposed aimto identify optimal conditions that maximize efficiency and performance within specified constraints.The findings will provide valuable insights for power plant operators seeking to optimize performance under real-world conditions,contributing to more efficient and sustainable power generation.展开更多
With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heurist...With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.展开更多
Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered ...Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered alloys are thermodynamically stable and have semiconductor behavior with indirect band gaps of 0.62,0.75,and 0.8 eV for LuVCo_(2)Bi_(2),LuNbCo_(2)Bi_(2),and LuTaCu_(2)Bi_(2),respectively.The investigated compounds exhibit semiconducting behavior with energy gaps below 0.8 eV.The impact of heat and pressure on thermodynamic coefficients was evaluated,and the influence of charge carriers on the temperature-dependent properties was studied using the semi-classical Boltzmann model.The studied compounds were characterized by their low lattice thermal conductivity at room temperature and low thermal expansion coefficient.These alloys exhibit substantial absorption coefficients in the ultraviolet(UV)light region,high optical conductivity,and high reflectivity in the visible light region,making them highly appealing materials for applications in the energy and electronics sectors.展开更多
The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure t...The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.展开更多
The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications direct...The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications directly addressing the research problem at hand.Similarly,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission systems.Furthermore,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied.RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure.Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing.Additionally,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy systems.This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.展开更多
Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Op...Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.展开更多
The thermal nanofluids have garnered widespread attention for their use in multiple thermal systems,including heating processes,sustainable energy,and nuclear reactions.Research on nanofluids has revealed that the the...The thermal nanofluids have garnered widespread attention for their use in multiple thermal systems,including heating processes,sustainable energy,and nuclear reactions.Research on nanofluids has revealed that the thermal efficiencies of such materials are adversely affected by various thermal features.The purpose of the current work is to demonstrate the thermal analysis of Jeffrey nanofluids with the suspension of microorganisms in the presence of variable thermal sources.The variable effects of thermal conductivity,Brownian diffusivity,and motile density are utilized.The investigated model also reveals the contributions of radiation phenomena and chemical reactions.A porous,saturated,moving surface with a suction phenomenon promotes flow.The modeling of the problem is based on the implementation of the Cattaneo-Christov approach.The convective thermal constraints are used to promote the heat transfer features.A simplified form of the governing model is treated with the assistance of a shooting technique.The physical effects of different parameters for the problem are presented.The current problem justifies its applications in heat transfer,coating processes,heat exchangers,cooling systems in microelectronics,solar systems,chemical processes,etc.展开更多
The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are c...The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.展开更多
In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees wi...In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m (R^(2) = 0.99) and 0.15 m (R^(2) = 0.93) for the two orthogonal crown widths, and 3.87 m2 for CPA (R^(2) = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m (R^(2) = 0.91), 0.51 m (R^(2) = 0.74), and 4.96 m2 (R^(2) = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.展开更多
Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
Nutrition is an important factor for the growth and development of plants. Among the main nutritional elements, there are nitrogen （N） and calcium （Ca）. The N comes from two forms of inorganic ions, ammonium （NH...Nutrition is an important factor for the growth and development of plants. Among the main nutritional elements, there are nitrogen （N） and calcium （Ca）. The N comes from two forms of inorganic ions, ammonium （NH4＋） and nitrate （NO3） whose functions in the plant are the increase in leaf area and improving the succulence of many crops, among other essential physiological processes. Both the deficit and excess NO3- have a negative impact on plants increasing susceptibility to insect pests and pathogens, while Ca as divalent ion absorbed Ca2＋ plays an important role in control of physiological disorders and the resistance of plants to diseases to increase the mechanical strength. The N and Ca have a close relationship in the nutritional role of the plant, because the Ca absorption acting nitrates, so that a proper balance affects plants better growth and higher strength.展开更多
This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-S...This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.展开更多
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22 MCIN/AEI/10.13039/501100011033 and by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22,MCIN/AEI/10.13039/501100011033by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.
文摘In recent years,hepatology has undergone a transformative evolution driven by significant advancements in diagnostic and therapeutic technologies.The expanding integration of endoscopic modalities into hepatology has enforced the diagnosis,staging,management of liver diseases beside integration into transplantation.This review highlights the evolving discipline of“endo-hepatology”,where endoscopic ultrasound,endoscopic retrograde cholangiopancreatography,and novel interventional tools are employed to address the critical challenges in chronic liver disease.The review provides a comprehensive synthesis of current evidence and different clinical applications,while also exploring future directions including revolution of artificial intelligence-assisted endoscopies and enhanced imaging endoscopies.By bridging the anatomical and functional interface between the gastrointestinal lumen and the liver,endo-hepatology is not only improving diagnostic accuracy and therapeutic precision but also reshaping multidisciplinary paradigms in hepatology practice.
基金the financial support from the National Key R&D Program of China(No.2023YFE0125900)National Natural Science Foundation of China(Nos.12372008 and 12102234)+1 种基金Natural Science Foundation of Heilongjiang Province,China(No.YQ2022A008)Taif University,Saudi Arabia,for supporting this work through Project number(TU-DSPP-2024-73).
文摘This paper analyzes the nonlinear dynamic characteristics and stability of Aero-Engine Dual-Rotor(AEDR)systems under high-frequency excitation,based on the Adaptive Harmonic Balance with the Asymptotic Harmonic Selection(AHB-AHS)method.A finite element dynamic equation for the AEDR system is introduced,considering complex nonlinearities of the intershaft bearing,unbalanced excitations,and high-frequency excitation.A solving strategy combining the AHB-AHS method and improved arclength continuation method is proposed to solve highdimensional dynamic equations containing complex nonlinearities and to track periodic solutions with parameter variations.The Floquet theory is used to analyze the types of bifurcation points in the system and the stability of periodic motions.The results indicate that high-frequency excitation can couple high-order and low-order modes,especially when the system undergoes superharmonic resonance.High-frequency excitation leads to more combination frequency harmonics,among which N_(f)ω_(1)-2ω_(2)dominates.Furthermore,changing the parameters(amplitude and frequency)of high-frequency excitation widens or shifts the unstable regions of the system.These findings contribute to understanding the mechanism of high-frequency excitation on aero-engines and demonstrate that the proposed AHB-AHS method is a powerful tool for analyzing highdimensional complex nonlinear dynamic systems under multi-frequency excitation.
文摘To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.
基金Project supported by the National Key R&D Program of China(No.2023YFE0125900)。
文摘This study explores the nonlinear dynamics of a quasi-zero stiffness(QZS)vibration isolator coupled with a piezoelectric energy harvester connected to an RL-resonant circuit.The model of the system is formulated with the Lagrangian mechanics,representing a two-degree-of-freedom nonlinear electromechanical system subject to harmonic base excitation under a 1:1 internal resonance condition.The model is normalized,and the conditions dictating monostable and bistable oscillation modes are identified.The bifurcation characteristics of the coupled system are analyzed in both oscillation modes by means of harmonic balance and continuation methods.The vibration isolation performance,with and without the coupled harvester,is evaluated in terms of displacement transmissibility to assess its dual functionalities for vibration isolation and energy harvesting.Analytical results demonstrate that integrating a piezoelectric harvester into a monostable QZS isolator under 1:1 internal resonance does not compromise its vibration isolation capability while enabling efficient energy harvesting at extremely low-frequency base excitation.Furthermore,the system's response under strong base excitation is investigated exclusively for energy harvesting in both monostable and bistable modes,leading to optimal structural parameter design.The conditions for intra-well and inter-well periodic oscillation modes,as well as chaotic responses,are analyzed analytically and validated numerically through stability charts,basins of attraction,bifurcation diagrams,time histories,and Poincarémaps.This work provides a comprehensive understanding of the oscillation dynamics of QZS isolators and offers valuable insights for optimizing their geometric parameters to function as high-performance vibration isolators and/or energy harvesters.
基金the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567).
文摘Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.
基金funded by the National Plan for Science,Technology and Innovation(MAARIFAH)-King Abdulaziz City for Science and Technology-The Kingdom of Saudi Arabia-award number(13-MAT377-08).
文摘Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes.This study introduces a hybrid approach integrating Long Short-Term Memory(LSTM)networks with the Hybrid Greylag Goose and Particle Swarm Optimization(GGPSO)algorithm to optimize preterm birth classification using Electrohysterogram signals.The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings,capturing key physiological features such as contraction patterns,entropy,and statistical variations.Statistical analysis and feature selection methods are applied to identify the most relevant predictors and enhance model interpretability.LSTM networks effectively capture temporal patterns in uterine activity,while the GGPSO algorithm finetunes hyperparameters,mitigating overfitting and improving classification accuracy.The proposed GGPSO-optimized LSTM model achieved superior performance with 97.34%accuracy,96.91%sensitivity,97.74%specificity,and 97.23%F-score,significantly outperforming traditional machine learning approaches and demonstrating the effectiveness of hybrid metaheuristic optimization in enhancing deep learning models for clinical applications.By combining deep learning withmetaheuristic optimization,this study contributes to advancing intelligent auto-diagnosis systems,facilitating early detection of pretermbirth risks and timely medical interventions.
文摘Enhancing the efficiency of Rankine cycles is crucial for improving the performance of thermal power plants,as it directly impacts operational costs and emissions in light of energy transition goals.This study sets itself apart from existing research by applying a novel optimization technique to a basic ideal Rankine cycle,focusing on a specific power plant that has not been previously analyzed.Currently,this cycle operates at 41%efficiency and a steam quality of 76%,constrained by fixed operational parameters.The primary objectives are to increase thermal efficiency beyond 46%and raise steam quality above 85%,while adhering to operational limits:a boiler pressure not exceeding 15 MPa,condenser pressure not dropping below 10 kPa,and turbine temperature not surpassing 500℃.This study utilizes numerical simulations to model the effects of varying boiler pressure(Pb)and condenser pressure(Pc)within the ranges of 12MPa<Pb<15 MPa and 5 kPa<Pc<10 kPa.By systematically adjusting these parameters,the proposed aimto identify optimal conditions that maximize efficiency and performance within specified constraints.The findings will provide valuable insights for power plant operators seeking to optimize performance under real-world conditions,contributing to more efficient and sustainable power generation.
基金supported by Prince Sattam bin Abdulaziz University for funding this research work through the project number(2024/RV/06).
文摘With the rapid advancements in technology and science,optimization theory and algorithms have become increasingly important.A wide range of real-world problems is classified as optimization challenges,and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains,such as machine learning,process control,and engineering design,showcasing their capability to address complex optimization problems.The Stochastic Fractal Search(SFS)algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials.Since its introduction by Hamid Salimi in 2015,SFS has garnered significant attention from researchers and has been applied to diverse optimization problems acrossmultiple disciplines.Its popularity can be attributed to several factors,including its simplicity,practical computational efficiency,ease of implementation,rapid convergence,high effectiveness,and ability to address singleandmulti-objective optimization problems,often outperforming other established algorithms.This review paper offers a comprehensive and detailed analysis of the SFS algorithm,covering its standard version,modifications,hybridization,and multi-objective implementations.The paper also examines several SFS applications across diverse domains,including power and energy systems,image processing,machine learning,wireless sensor networks,environmental modeling,economics and finance,and numerous engineering challenges.Furthermore,the paper critically evaluates the SFS algorithm’s performance,benchmarking its effectiveness against recently published meta-heuristic algorithms.In conclusion,the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.
文摘Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered alloys are thermodynamically stable and have semiconductor behavior with indirect band gaps of 0.62,0.75,and 0.8 eV for LuVCo_(2)Bi_(2),LuNbCo_(2)Bi_(2),and LuTaCu_(2)Bi_(2),respectively.The investigated compounds exhibit semiconducting behavior with energy gaps below 0.8 eV.The impact of heat and pressure on thermodynamic coefficients was evaluated,and the influence of charge carriers on the temperature-dependent properties was studied using the semi-classical Boltzmann model.The studied compounds were characterized by their low lattice thermal conductivity at room temperature and low thermal expansion coefficient.These alloys exhibit substantial absorption coefficients in the ultraviolet(UV)light region,high optical conductivity,and high reflectivity in the visible light region,making them highly appealing materials for applications in the energy and electronics sectors.
文摘The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.
文摘The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications directly addressing the research problem at hand.Similarly,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission systems.Furthermore,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied.RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure.Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing.Additionally,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy systems.This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.
基金funded by Researchers Supporting Programnumber(RSPD2024R809),King Saud University,Riyadh,Saudi Arabia.
文摘Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.
基金appreciation to King Saud University for funding this work through researchers supporting project(No.RSPD2025R1056).
文摘The thermal nanofluids have garnered widespread attention for their use in multiple thermal systems,including heating processes,sustainable energy,and nuclear reactions.Research on nanofluids has revealed that the thermal efficiencies of such materials are adversely affected by various thermal features.The purpose of the current work is to demonstrate the thermal analysis of Jeffrey nanofluids with the suspension of microorganisms in the presence of variable thermal sources.The variable effects of thermal conductivity,Brownian diffusivity,and motile density are utilized.The investigated model also reveals the contributions of radiation phenomena and chemical reactions.A porous,saturated,moving surface with a suction phenomenon promotes flow.The modeling of the problem is based on the implementation of the Cattaneo-Christov approach.The convective thermal constraints are used to promote the heat transfer features.A simplified form of the governing model is treated with the assistance of a shooting technique.The physical effects of different parameters for the problem are presented.The current problem justifies its applications in heat transfer,coating processes,heat exchangers,cooling systems in microelectronics,solar systems,chemical processes,etc.
文摘The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.
基金This study was partially funded by the“Collaboration Research Program of IDEAS”,Chubu University(IDEAS 201603 and IDEAS201702)the CREST Program“Knowledge Discovery by Constructing AgriBigData”(JPMJCR1512)the SICORP Program“Data Science-based Farming Support System for Sustainable Crop Production under Climatic Change”of the Japan Science and Technology Agency.
文摘In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m (R^(2) = 0.99) and 0.15 m (R^(2) = 0.93) for the two orthogonal crown widths, and 3.87 m2 for CPA (R^(2) = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m (R^(2) = 0.91), 0.51 m (R^(2) = 0.74), and 4.96 m2 (R^(2) = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
文摘Nutrition is an important factor for the growth and development of plants. Among the main nutritional elements, there are nitrogen （N） and calcium （Ca）. The N comes from two forms of inorganic ions, ammonium （NH4＋） and nitrate （NO3） whose functions in the plant are the increase in leaf area and improving the succulence of many crops, among other essential physiological processes. Both the deficit and excess NO3- have a negative impact on plants increasing susceptibility to insect pests and pathogens, while Ca as divalent ion absorbed Ca2＋ plays an important role in control of physiological disorders and the resistance of plants to diseases to increase the mechanical strength. The N and Ca have a close relationship in the nutritional role of the plant, because the Ca absorption acting nitrates, so that a proper balance affects plants better growth and higher strength.
文摘This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.
