查看更多>>摘要：3D-printed Porous Titanium Alloy Implants(pTi),owing to their biologically inertness and relatively smooth surface morphology,adversely affect the biological functions of surrounding cells.To address the challenges,constructing a bio-inspired interface that mimics the hierarchical structure of bone tissue can enhance the cellular functions of cells.In this context,Hollow Mesoporous Silica Nanoparticles(HMSNs),renowned for their unique physicochemical properties and superior biocompatibility,offer a promising direction for this research.In this research,the initially synthesized HMSNs were used to construct a"hollow-mesoporous-macroporous"hierarchical bioinspired coating on the pTi surface through the Layer-by-Layer technique.Simultaneously,diverse morphologies of coatings were established by adjusting the depo-sition strategy of PDDA/HMSNs on the pTi surface(pTi-HMSN-2,pTi-HMSN-4,pTi-HMSN-6).A range of techniques were employed to investigate the physicochemical properties and regulation of cellular biological functions of the diverse HMSN coating strategies.Notably,the pTi-HMSN-4 and pTi-HMSN-6 groups exhibited the uniform coatings,leading to a substantial enhancement in surface roughness and hydrophilicity.Meantime,the coating constructed strategy of pTi-HMSN-4 possessed commendable stability.Based on the aforementioned findings,both pTi-HMSN-4 and pTi-HMSN-6 facilitated the adhesion,spreading,and pseudopodia extension of BMSCs,which led to a notable upsurge in the expres-sion levels of vinculin protein in BMSCs.Comprehensive analysis indicates that the coating,when PDDA/HMSNs are deposited four times,possesses favorable overall performance.The research will provide a solid theoretical basis for the translation of HMSN bioinspired coatings for orthopedic implants.
查看更多>>摘要：Excellent fluid sealing performance is crucial to ensuring the safety of important equipment,especially in aerospace field,such as space capsule and fuel chamber.The frequently opening and closing of the sealing devices is particularly important.Driven by this background,clams(Mactra chinensis)which can open and close their double shells with superior sealing performance,are studied in this work.Here,we show that the clam's sealing ability is the result of its unique multilevel intermeshing microstructures,including hinge teeth and micro-blocks.These microstructures,which resemble gear teeth,engage with each other when the shell closes,forming a tight structure that prevents the infiltration of water from the outside.Furthermore,the presence of micron blocks prevents the penetration of finer liquids.The simulation results of the bionic end seal components show that the multilevel microstructure has a superior sealing effect.This research is expected to be applied to undersea vehicles that require frequent door opening and closing.
查看更多>>摘要：It is generally considered that heat treatments have a negative impact on the mechanical properties of nacre due to ther-mal decomposition of the organic matrix.However,the present work investigated the microindentation behavior on fresh and heat-treated nacres from two orthogonal directions,and the results demonstrate that both hardness value and damage tolerance can remain almost unchanged on the cross-section with the organic matrix degeneration,despite a significant deterioration on the platelet surface.Theoretical analyses suggest that the anisotropic response of indentation behavior to heat treatment in nacre is primarily caused by its structural orientation.Specifically,compared with a single layer of irregu-lar interplatelet interfaces in cross-sectional specimens,the multiple layers of parallel interlamellar interfaces in in-plane specimens exhibit a much greater ability to impede indenter-triggered destruction,and heat treatments would reduce the in-plane hardness but nearly have no effect on the cross-sectional hardness.Moreover,the deeper embedding of platelets in cross-sectional specimens enhances their resistance to interface cracking caused by organic matrix degradation at high temperatures,leading to a reduced sensitivity to damage.Therefore,the indentation behavior of nacre shows different tendencies in response to variations in the organic matrix state along normal and parallel directions.
Mohammad H.Nadimi-ShahrakiHajar FarhanginasabShokooh TaghianAli Safaa Sadiq...
1465-1495页
查看更多>>摘要：The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited number of param-eters,WOA's search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex problems.To address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Opti-mization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of difficulty.MTV-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and exploitation.For the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test functions.Moreover,MTV-WOA is compared with well-stablished,recent,and WOA variant algorithms.The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence rate.Additionally,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative algorithms.Finally,we solved five engi-neering design problems to demonstrate the practicality of MTV-WOA.The results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.
查看更多>>摘要：This study,grounded in Waxman fusion method,introduces an algorithm for the fusion of visible and infrared images,tailored to a two-level lighting environment,inspired by the mathematical model of the visual receptive field of rattlesnakes and the two-mode cells'mechanism.The research presented here is segmented into three components.In the first segment,we design a preprocessing module to judge the ambient light intensity and divide the lighting environment into two levels:day and night.The second segment proposes two distinct network structures designed specifically for these daytime and nighttime images.For the daytime images,where visible light information is predominant,we feed the ON-VIS signal and the IR-enhanced visual signal into the central excitation and surrounding suppression regions of the ON-center receptive field in the B chan-nel,respectively.Conversely,for nighttime images where infrared information takes precedence,the ON-IR signal and the Visual-enhanced IR signal are separately input into the central excitation and surrounding suppression regions of the ON-center receptive field in the B channel.The outcome is a pseudo-color fused image.The third segment employs five different no-reference image quality assessment methods to evaluate the quality of thirteen sets of pseudo-color images produced by fusing infrared and visible information.These images are then compared with those obtained by six other methods cited in the relevant reference.The empirical results indicate that this study's outcomes surpass the comparative results in terms of average gradient and spatial frequency.Only one or two sets of fused images underperformed in terms of standard deviation and entropy when compared to the control results.Four sets of fused images did not perform as well as the comparison in the QAB/F index.In conclusion,the fused images generated through the proposed method show superior performance in terms of scene detail,visual perception,and image sharpness when compared with their counterparts from other methods.
查看更多>>摘要：Medical image segmentation has witnessed rapid advancements with the emergence of encoder-decoder based methods.In the encoder-decoder structure,the primary goal of the decoding phase is not only to restore feature map resolution,but also to mitigate the loss of feature information incurred during the encoding phase.However,this approach gives rise to a challenge:multiple up-sampling operations in the decoder segment result in the loss of feature information.To address this challenge,we propose a novel network that removes the decoding structure to reduce feature information loss(CBL-Net).In particular,we introduce a Parallel Pooling Module(PPM)to counteract the feature information loss stemming from conven-tional and pooling operations during the encoding stage.Furthermore,we incorporate a Multiplexed Dilation Convolution(MDC)module to expand the network's receptive field.Also,although we have removed the decoding stage,we still need to recover the feature map resolution.Therefore,we introduced the Global Feature Recovery(GFR)module.It uses atten-tion mechanism for the image feature map resolution recovery,which can effectively reduce the loss of feature information.We conduct extensive experimental evaluations on three publicly available medical image segmentation datasets:DRIVE,CHASEDB and MoNuSeg datasets.Experimental results show that our proposed network outperforms state-of-the-art methods in medical image segmentation.In addition,it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component.
查看更多>>摘要：Energy issues have always been one of the most significant concerns for scientists worldwide.With the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of depletion.Wind energy is a readily available and sustainable energy source.Wind farm layout optimization problem,through scientifically arranging wind turbines,significantly enhances the efficiency of harnessing wind energy.Meta-heuristic algorithms have been widely employed in wind farm layout optimization.This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm,referred to as AIGA,for optimizing wind farm layout problems.The adaptive strategy dynamically adjusts the placement of wind turbines,leading to a substantial improvement in energy utilization efficiency within the wind farm.In this study,AIGA is tested in four different wind conditions,alongside four other classical algorithms,to assess their energy conversion efficiency within the wind farm.Experimental results demonstrate a notable advantage of AIGA.
查看更多>>摘要：Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.
查看更多>>摘要：Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.
查看更多>>摘要：Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food source.This algorithm has the characteristics of a simple structure and high performance.However,PFA faces challenges such as insufficient popula-tion diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation capabilities.This paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimiza-tion problems.Firstly,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the algorithm.Secondly,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst solutions.Thirdly,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence speed.These three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the algorithm.In performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic algo-rithms.The numerical experimental results demonstrated the superiority of APFA.
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