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
Cancer stem cells(CSCs)are a major challenge in cancer therapy.Stem cell-like cells form a unique subpopulation within many tumors,which govern the degree of malignancy by promoting metastasis,recurrence,heterogeneity...Cancer stem cells(CSCs)are a major challenge in cancer therapy.Stem cell-like cells form a unique subpopulation within many tumors,which govern the degree of malignancy by promoting metastasis,recurrence,heterogeneity,and resistance to drug and radiation.Furthermore,these cells can persist in patients even after undergoing multiple cycles of conventional cancer therapy via dormancy,where they no longer dividing but remain active.These may cause cancer recurrence at any time,even years after a supposed cure,and remain invisible to the immune system.Targeting specific surface markers,signaling pathways and tumor microenvironment,which all have a significant effect on CSC function and maintenance,could help to eradicate CSCs and improve patient survival.Combinations of traditional therapies with nano-based drug delivery systems can efficiently target CSCs.Considering the biology and properties of CSCs,we classify recent approaches involving nanoparticle engineering,extracellular matrix modulation,cocktail strategies,multi-stage therapy,CSC defanging,Trojan horse systems,targeted therapy and organelle targeting.We highlight the most recent advances in nanocarrier design and drug delivery technologies to target CSCs,combined with conventional treatment in preclinical and clinical trials.The prospects of these approaches for CSCs elimination and recurrent cancer treatment are discussed.展开更多
Carbon capture and storage(CCS)is an advanced environmental technology for mitigating CO_(2) emissions and addressing climate change.Among the various approaches,adsorption has emerged as a promising method for CO_(2)...Carbon capture and storage(CCS)is an advanced environmental technology for mitigating CO_(2) emissions and addressing climate change.Among the various approaches,adsorption has emerged as a promising method for CO_(2) capture due to its effiectiveness and practicality.This review explores the potential of clay minerals as adsorbents for CO_(2) capture,providing an in-depth analysis of their inherent properties and the mechanisms involved in adsorption process.The review begins with an introduction to CCS and the concept of adsorption,followed by a detailed examination of various clay minerals,including sepiolite,montmorillonite,bentonite,kaolinite,saponite,halloysite,and illite.Each mineral’s suitability for CO_(2) adsorption is assessed,highlighting the specific properties that contribute to their performance.The mechanisms of CO_(2) adsorption including physisorption,chemisorption,ion exchange,pore diffusion,intraparticle diffusion,surface complexation,and competitive adsorption are thoroughly discussed.The review also covers the modification of clay minerals through physical and chemical treatments,amine functionalization,and composite formation to enhance their CO_(2) adsorption capacity.Additionally,regeneration methods such as temperature-swing adsorption(TSA),pressure-swing adsorption(PSA),and purging are discussed,along with CO_(2) recovery and storage techniques for improving energy efficiency.The review concludes with an overview of characterization methods for clay-based adsorbents and potential applications,while addressing the challenges and future trends in thefield.This work emphasizes the promising role of clay-based adsorbents in advancing CCS technology.展开更多
Greenhouse gas(GHG)emssions from fossil fuel consumption are driving global climate change.This study applied the fully modified ordinary least squares(FMOLS)model and pairwise panel Granger causality test to explore ...Greenhouse gas(GHG)emssions from fossil fuel consumption are driving global climate change.This study applied the fully modified ordinary least squares(FMOLS)model and pairwise panel Granger causality test to explore the relationships of GHG emissions with gross domestic product(GDP),population,urbanization,natural resource rents,foreign direct investment(FDI),and renewable energy consumption in 12 Middle East and North Africa(MENA)countries(Algeria,Bahrain,Comoros,Djibouti,Egypt,Qatar,Somalia,Saudi Arabia,Syria,the United Arab Emirates,Tunisia,and Yemen)from 1990 to 2023.Due to the limited data on renewable energy after 2020,the coverage of renewable energy consumption is from 1990 to 2021.Findings showed that Saudi Arabia,Egypt,Algeria,the United Arab Emirates,and Qatar are the top 5 GHG emitters in the MENA region,with the GHG emissions of the energy sector rising fastest among all sectors.Results also indicated that a 1.00%increase in GDP,population,urbanization,natural resource rents,and FDI raises GHG emissions by 0.48%,0.61%,0.86%,0.29%,and 0.11%,respectively.Conversely,a 1.00%increase in renewable energy consumption reduces GHG emissions by 0.13%.Effective policies promoting renewable energy investment and the adoption of renewable energy could significantly reduce electricity costs and GHG emissions,contributing to achieving climate goals,such as net-zero emissions and environmental sustainability.Additionally,the increase of renewable energy consumption and technology development would improve energy efficiency,create jobs,and stimulate economic growth in the MENA region.This study recommends tailored policy instruments to support the transition to low-emission technologies and strategies.展开更多
In this paper,we use the Riemann-Hilbert(RH)method to investigate the Cauchy problem of the reverse space-time nonlocal Hirota equation with step-like initial data:q(z,0)=o(1)as z→-∞and q(z,0)=δ+o(1)as z→∞,where...In this paper,we use the Riemann-Hilbert(RH)method to investigate the Cauchy problem of the reverse space-time nonlocal Hirota equation with step-like initial data:q(z,0)=o(1)as z→-∞and q(z,0)=δ+o(1)as z→∞,whereδis an arbitrary positive constant.We show that the solution of the Cauchy problem can be determined by the solution of the corresponding matrix RH problem established on the plane of complex spectral parameterλ.As an example,we construct an exact solution of the reverse space-time nonlocal Hirota equation in a special case via this RH problem.展开更多
During cold rolling in magnesium(Mg)alloys,there will always be serious ductility deterioration,which greatly restricts the application of this processing method.In this work,ductility-strength synergy occurred after ...During cold rolling in magnesium(Mg)alloys,there will always be serious ductility deterioration,which greatly restricts the application of this processing method.In this work,ductility-strength synergy occurred after cold rolling was carried out on a<0001>fiber textured Mg-17.5Gd-Zr alloy(wt.%).The ductility-strength synergy can be mainly attributed to texture randomization,microstructure refinement,and easier basal slip transfer condition in the twinned grains.Firstly,the activation of multiple twinning behaviors,i.e.,{10-12},{10-11},and{11-21}twinning,and related twin-twin interaction restrict the formation of strong basal texture and facilitate the texture randomization.Secondly,profuse high-angle grain boundaries form through dislocation-twin interaction,twin-twin interaction,and dislocation interaction,which promote microstructure refinement.Thirdly,the basal slip transfer becomes easier in the twinned grains owing to the activation of multiple twinning behaviors and their impingements.Activation of{11-21}twinning and<c+a>dislocations and the<0001>fiber texture which is favorable for multiple twinning activating during cold rolling are considered to be the key roles for microstructure and texture optimization.展开更多
Two new free-living marine nematode species belonging to the genus Tricoma Cobb,1894(Desmoscolecidae)are identi-fied from the continental shelf region of the Bay of Bengal.Tricoma ajmalia sp.nov.is characterized by th...Two new free-living marine nematode species belonging to the genus Tricoma Cobb,1894(Desmoscolecidae)are identi-fied from the continental shelf region of the Bay of Bengal.Tricoma ajmalia sp.nov.is characterized by the number of main rings(84 male and 85 female),the arrangement of somatic setae,the head shape with broadly truncated end,the relatively long cephalic setae(69%-73%of corresponding head diameter),the amphids extending up to second main ring,slender spicules slightly curved and gu-berbaculum with dorsocaudally directed apophyses.The second new species,Tricoma sampadansis sp.nov.is characterized by the number of rings(64),the arrangement of somatic setae(14-13 subdorsal,16 subvenral),the head shape with broadly truncated end,the relatively long cephalic setae(75%corresponding head diameter),the amphidial fovea extending over first main ring,anterioirly bent spicules and gubernaculum with hooked proximal end.An updated valid species list of Tricoma is also presented.展开更多
Twins play an important role in the texture transition during annealing.In a cold rolled high rare earth content magnesium(Mg)alloys with{10–12}extension twins,{11–21}extension twins,{10–11}compression twins and{10...Twins play an important role in the texture transition during annealing.In a cold rolled high rare earth content magnesium(Mg)alloys with{10–12}extension twins,{11–21}extension twins,{10–11}compression twins and{10–11}-{10–12}double twins and frequent twin-twin interactions,quasi-in-situ electron backscatter diffraction method was used to observe the twin induced static recrystallization(SRX)and related effect on texture during annealing.The results show that basal component was consumed owing to the SRX occurred in basal oriented{10–12}twins and SRXed grains with several specific orientations show preferential grain growth.SRX widely operated in the{10–12}extension and{11–21}extension twins,but absent in most{10–11}compression and{10–11}-{10–12}double twins,which is different to traditional twin induced SRX.Most compression/double twins detwinned while only partial tension twins detwinned.Operation of{11–21}twins and resultant twin-twin interaction facilitate the formation of serrated twin boundaries,which can serve as nucleation sites.Activation of<c+a>dislocation and related dislocation interaction in high dislocation density areas promote the formation of new grain boundaries and related SRX.Profuse<c+a>dislocations in basal oriented twins release the strain accumulation in compression/double twins and thus result in the absence of SRX.The twin size difference,storage energy and dislocation-twin interaction commonly functioned to the detwinning during annealing.The near-coincide site lattice boundaries that show high mobility were considered to be the important contributor to the preferential grain growth of SRXed grains.展开更多
We propose a method of full-color,scan-free,and natural-light motion-picture holography for full-color 4D(3D+time)imaging and develop a portable natural-light motion-picture holographic camera that can be set on a mov...We propose a method of full-color,scan-free,and natural-light motion-picture holography for full-color 4D(3D+time)imaging and develop a portable natural-light motion-picture holographic camera that can be set on a movable table without any antivibration structure.Full-color motion-picture holograms of objects illuminated by natural light are obtained at the frame rate of an image sensor.We perform the single-shot natural-light full-color 3D imaging of objects illuminated by sunlight and the full-color 4D imaging of a moving object.This holographic camera is capable of full-color 4D imaging of objects ranging in size from the centimeter order to the 10-m order.This opens up a new stage in holographic imaging,overcoming the limitations of conventional holographic imaging despite the portability of this camera.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 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
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
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.
基金supported by Tabriz University of Medical Sciences,grant number 65364.
文摘Cancer stem cells(CSCs)are a major challenge in cancer therapy.Stem cell-like cells form a unique subpopulation within many tumors,which govern the degree of malignancy by promoting metastasis,recurrence,heterogeneity,and resistance to drug and radiation.Furthermore,these cells can persist in patients even after undergoing multiple cycles of conventional cancer therapy via dormancy,where they no longer dividing but remain active.These may cause cancer recurrence at any time,even years after a supposed cure,and remain invisible to the immune system.Targeting specific surface markers,signaling pathways and tumor microenvironment,which all have a significant effect on CSC function and maintenance,could help to eradicate CSCs and improve patient survival.Combinations of traditional therapies with nano-based drug delivery systems can efficiently target CSCs.Considering the biology and properties of CSCs,we classify recent approaches involving nanoparticle engineering,extracellular matrix modulation,cocktail strategies,multi-stage therapy,CSC defanging,Trojan horse systems,targeted therapy and organelle targeting.We highlight the most recent advances in nanocarrier design and drug delivery technologies to target CSCs,combined with conventional treatment in preclinical and clinical trials.The prospects of these approaches for CSCs elimination and recurrent cancer treatment are discussed.
文摘Carbon capture and storage(CCS)is an advanced environmental technology for mitigating CO_(2) emissions and addressing climate change.Among the various approaches,adsorption has emerged as a promising method for CO_(2) capture due to its effiectiveness and practicality.This review explores the potential of clay minerals as adsorbents for CO_(2) capture,providing an in-depth analysis of their inherent properties and the mechanisms involved in adsorption process.The review begins with an introduction to CCS and the concept of adsorption,followed by a detailed examination of various clay minerals,including sepiolite,montmorillonite,bentonite,kaolinite,saponite,halloysite,and illite.Each mineral’s suitability for CO_(2) adsorption is assessed,highlighting the specific properties that contribute to their performance.The mechanisms of CO_(2) adsorption including physisorption,chemisorption,ion exchange,pore diffusion,intraparticle diffusion,surface complexation,and competitive adsorption are thoroughly discussed.The review also covers the modification of clay minerals through physical and chemical treatments,amine functionalization,and composite formation to enhance their CO_(2) adsorption capacity.Additionally,regeneration methods such as temperature-swing adsorption(TSA),pressure-swing adsorption(PSA),and purging are discussed,along with CO_(2) recovery and storage techniques for improving energy efficiency.The review concludes with an overview of characterization methods for clay-based adsorbents and potential applications,while addressing the challenges and future trends in thefield.This work emphasizes the promising role of clay-based adsorbents in advancing CCS technology.
基金the support provided by the King Fahd University of Petroleum & Minerals (KFUPM) for facilitating this research
文摘Greenhouse gas(GHG)emssions from fossil fuel consumption are driving global climate change.This study applied the fully modified ordinary least squares(FMOLS)model and pairwise panel Granger causality test to explore the relationships of GHG emissions with gross domestic product(GDP),population,urbanization,natural resource rents,foreign direct investment(FDI),and renewable energy consumption in 12 Middle East and North Africa(MENA)countries(Algeria,Bahrain,Comoros,Djibouti,Egypt,Qatar,Somalia,Saudi Arabia,Syria,the United Arab Emirates,Tunisia,and Yemen)from 1990 to 2023.Due to the limited data on renewable energy after 2020,the coverage of renewable energy consumption is from 1990 to 2021.Findings showed that Saudi Arabia,Egypt,Algeria,the United Arab Emirates,and Qatar are the top 5 GHG emitters in the MENA region,with the GHG emissions of the energy sector rising fastest among all sectors.Results also indicated that a 1.00%increase in GDP,population,urbanization,natural resource rents,and FDI raises GHG emissions by 0.48%,0.61%,0.86%,0.29%,and 0.11%,respectively.Conversely,a 1.00%increase in renewable energy consumption reduces GHG emissions by 0.13%.Effective policies promoting renewable energy investment and the adoption of renewable energy could significantly reduce electricity costs and GHG emissions,contributing to achieving climate goals,such as net-zero emissions and environmental sustainability.Additionally,the increase of renewable energy consumption and technology development would improve energy efficiency,create jobs,and stimulate economic growth in the MENA region.This study recommends tailored policy instruments to support the transition to low-emission technologies and strategies.
基金supported by the National Natural Science Foundation of China under Grant No.12147115the Discipline(Subject)Leader Cultivation Project of Universities in Anhui Province under Grant Nos.DTR2023052 and DTR2024046+2 种基金the Natural Science Research Project of Universities in Anhui Province under Grant No.2024AH040202the Young Top Notch Talents and Young Scholars of High End Talent Introduction and Cultivation Action Project in Anhui Provincethe Scientific Research Foundation Funded Project of Chuzhou University under Grant Nos.2022qd022 and 2022qd038。
文摘In this paper,we use the Riemann-Hilbert(RH)method to investigate the Cauchy problem of the reverse space-time nonlocal Hirota equation with step-like initial data:q(z,0)=o(1)as z→-∞and q(z,0)=δ+o(1)as z→∞,whereδis an arbitrary positive constant.We show that the solution of the Cauchy problem can be determined by the solution of the corresponding matrix RH problem established on the plane of complex spectral parameterλ.As an example,we construct an exact solution of the reverse space-time nonlocal Hirota equation in a special case via this RH problem.
基金supported by the National Natural Science Foundation of China(Nos.52301164,52371121 and 52271107)the Natural Science Foundation of Shandong Province(No.ZR2021ME241)the Basic Research Project of Education Department of Liaoning Province(No.JYTQN2023066).
文摘During cold rolling in magnesium(Mg)alloys,there will always be serious ductility deterioration,which greatly restricts the application of this processing method.In this work,ductility-strength synergy occurred after cold rolling was carried out on a<0001>fiber textured Mg-17.5Gd-Zr alloy(wt.%).The ductility-strength synergy can be mainly attributed to texture randomization,microstructure refinement,and easier basal slip transfer condition in the twinned grains.Firstly,the activation of multiple twinning behaviors,i.e.,{10-12},{10-11},and{11-21}twinning,and related twin-twin interaction restrict the formation of strong basal texture and facilitate the texture randomization.Secondly,profuse high-angle grain boundaries form through dislocation-twin interaction,twin-twin interaction,and dislocation interaction,which promote microstructure refinement.Thirdly,the basal slip transfer becomes easier in the twinned grains owing to the activation of multiple twinning behaviors and their impingements.Activation of{11-21}twinning and<c+a>dislocations and the<0001>fiber texture which is favorable for multiple twinning activating during cold rolling are considered to be the key roles for microstructure and texture optimization.
基金the Centre for Marine Living Resources and Ecology (CMLRE),Ministry of Earth Sciences (MoES), Kochi, Government of India, for the support of crew ‘FORV Sagar Sampada’Funding was provided by the Centre for Marine Living Resources and Ecology (CMLRE), Ministry of Earth Sciences (MoES), Kochi, Government of India
文摘Two new free-living marine nematode species belonging to the genus Tricoma Cobb,1894(Desmoscolecidae)are identi-fied from the continental shelf region of the Bay of Bengal.Tricoma ajmalia sp.nov.is characterized by the number of main rings(84 male and 85 female),the arrangement of somatic setae,the head shape with broadly truncated end,the relatively long cephalic setae(69%-73%of corresponding head diameter),the amphids extending up to second main ring,slender spicules slightly curved and gu-berbaculum with dorsocaudally directed apophyses.The second new species,Tricoma sampadansis sp.nov.is characterized by the number of rings(64),the arrangement of somatic setae(14-13 subdorsal,16 subvenral),the head shape with broadly truncated end,the relatively long cephalic setae(75%corresponding head diameter),the amphidial fovea extending over first main ring,anterioirly bent spicules and gubernaculum with hooked proximal end.An updated valid species list of Tricoma is also presented.
基金supported by the National Natural Science Foundation of China(Nos.52301164,52371121 and 52271107).
文摘Twins play an important role in the texture transition during annealing.In a cold rolled high rare earth content magnesium(Mg)alloys with{10–12}extension twins,{11–21}extension twins,{10–11}compression twins and{10–11}-{10–12}double twins and frequent twin-twin interactions,quasi-in-situ electron backscatter diffraction method was used to observe the twin induced static recrystallization(SRX)and related effect on texture during annealing.The results show that basal component was consumed owing to the SRX occurred in basal oriented{10–12}twins and SRXed grains with several specific orientations show preferential grain growth.SRX widely operated in the{10–12}extension and{11–21}extension twins,but absent in most{10–11}compression and{10–11}-{10–12}double twins,which is different to traditional twin induced SRX.Most compression/double twins detwinned while only partial tension twins detwinned.Operation of{11–21}twins and resultant twin-twin interaction facilitate the formation of serrated twin boundaries,which can serve as nucleation sites.Activation of<c+a>dislocation and related dislocation interaction in high dislocation density areas promote the formation of new grain boundaries and related SRX.Profuse<c+a>dislocations in basal oriented twins release the strain accumulation in compression/double twins and thus result in the absence of SRX.The twin size difference,storage energy and dislocation-twin interaction commonly functioned to the detwinning during annealing.The near-coincide site lattice boundaries that show high mobility were considered to be the important contributor to the preferential grain growth of SRXed grains.
基金supported by the Japan Society for the Promotion of Science(JSPS)(Grant Nos.JP23K26579 and JP23K17364)the Mitsubishi Foundation(Grant No.202111007)+3 种基金Precursory Research for Embryonic Science and Technology(PRESTO)(Grant No.JPMJPR16P8)the Cooperative Research Program of“Network Joint Research Centre for Materials and Devices”(Grant Nos.20234030 and 20240411)the JST CREST(Grant No.JPMJCR23A1)the JST FOREST(Grant No.JPMJFR206K).
文摘We propose a method of full-color,scan-free,and natural-light motion-picture holography for full-color 4D(3D+time)imaging and develop a portable natural-light motion-picture holographic camera that can be set on a movable table without any antivibration structure.Full-color motion-picture holograms of objects illuminated by natural light are obtained at the frame rate of an image sensor.We perform the single-shot natural-light full-color 3D imaging of objects illuminated by sunlight and the full-color 4D imaging of a moving object.This holographic camera is capable of full-color 4D imaging of objects ranging in size from the centimeter order to the 10-m order.This opens up a new stage in holographic imaging,overcoming the limitations of conventional holographic imaging despite the portability of this camera.
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 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.
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.
