A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the pote...A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the potential fairness issue that may arise from the maximum sum-rate based objective function and the optical power constraints are set considering the non-negativity of the transmit signal, the requirement of the human eyes safety and all users' quality of service(Qo S). Then, the SSA is utilized to solve this optimization problem. Moreover, to demonstrate the superiority of the proposed strategy, it is compared with the fixed power allocation(FPA) and the gain ratio power allocation(GRPA) schemes. Results show that regardless of the number of users considered, the sum-rate achieved by SSA consistently outperforms that of FPA and GRPA schemes. Specifically, compared to FPA and GRPA schemes, the sum-rate obtained by SSA is increased by 40.45% and 53.44% when the number of users is 7, respectively. The proposed SSA also has better performance in terms of user fairness. This work will benefit the design and development of the NOMA-visible light communication(VLC) systems.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead...Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios.展开更多
With the continuous improvement of the accuracy of geodetic deformation data,the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms.In this research,an improved Spar...With the continuous improvement of the accuracy of geodetic deformation data,the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms.In this research,an improved Sparrow Search Algorithm(SSA)is proposed for the seismic source parameter inversion problem.By replacing the original population generation in the improved algorithm with Latin hypercubic sampling,the Sparrow Search Algorithm reduces the repetition of samples in the population initialization.Subsequently,the algorithm introduces adaptive weights in the discoverer generation phase of the sparrow algorithm and combines the Levy flight strategy to make the algorithm more comprehensive and improve the search accuracy during the whole iteration process.Therefore,the improved Latin hypercube-based sparrow search algorithm(ILHSSA)has better advantages in terms of iterative convergence speed and stability.In order to verify the performance of ILHSSA,the basic genetic algorithm(GA)and sparrow search algorithm(SSA)are examined and compared with ILHSSA by simulated earthquakes of two different earthquake types.The simulation experiments show that the improved algorithm ILHSSA outperforms SSA in accuracy and stability.Compared with the GA algorithm,ILHSSA can achieve the same inversion accuracy as GA,and it even surpasses GA in inversion speed and the inversion results of some parameters,demonstrating better stability.Finally,the improved algorithm is used for the 2017 Bodrum-Cos earthquake and the 2016 Amatrice earthquake in Italy.The inversion results all reflect the practicality and reliability of the improved algorithm.展开更多
It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional ...It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional Sparrow Search Algorithm(SSA)suffers from limited global search capability,insufficient population diversity,and slow convergence,which often leads to premature stagnation in local optima.Despite the proposal of various enhanced versions,the effective balancing of exploration and exploitation remains an unsolved challenge.To address the previously mentioned problems,this study proposes a multi-strategy collaborative improved SSA,which systematically integrates four complementary strategies:(1)the Northern Goshawk Optimization(NGO)mechanism enhances global exploration through guided prey-attacking dynamics;(2)an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom;(3)a dual chaotic initialization method(Bernoulli and Sinusoidal maps)increases population diversity and distribution uniformity;and(4)an elite retention strategy maintains solution quality and prevents degradation during iterations.These strategies cooperate synergistically,forming a tightly coupled optimization framework that significantly improves search efficiency and robustness.Therefore,this paper names it NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization.Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude.Compared with SSA,GWO,ISSA,and CSSOA,NTSSA improves solution accuracy by up to 14.3%(F8)and 99.8%(F12),while accelerating convergence by approximately 1.5–2×.The Wilcoxon rank-sum test(p<0.05)indicates that NTSSA demonstrates a statistically substantial performance advantage.Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation,chaos-based diversification,and elite preservation ensures both high convergence accuracy and global stability.This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation,offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design.展开更多
With rapid urbanization occurring throughout China,the existence of Tree Sparrows (Passer montanus) in big cities is likely to be affected by a decrease in habitat and food availability.Can the urban Tree Sparrow adap...With rapid urbanization occurring throughout China,the existence of Tree Sparrows (Passer montanus) in big cities is likely to be affected by a decrease in habitat and food availability.Can the urban Tree Sparrow adapt to these changes? To elucidate this question,we studied the effect of urbanization on the abundance and distribution of Tree Sparrows in Beijing.We found the abundance of the Tree Sparrow negatively correlated with an urbanization score.Sparrow abundance was very low in residential areas with high-rise buildings,commercial centers and main roads,while their numbers were significantly higher in parks,university campuses,low building residential and suburban areas.Environmental factors within the 50 m and 200 m scales were most suitable in predicting the distribution of Tree Sparrows during winter,while factors within 50 m and 400 m scales are suitable during the breeding season.During winter,the number of conifer trees and pedestrians were the major factors at the 50 m scale,while the area of high-rise buildings and vegetation become the predominant factors on a 200 m scale.Alternatively,during the breeding season the area of low buildings and the number of conifers and pedestrians were the main factors on the 50 m scale while the area of high-rise buildings and vegetation remained the most important factors on the 400 m scale.These results indicate that highly urbanized areas are not suitable habitats for the Tree Sparrow,although this species can adapt to human environments.Food and nest sites for urban birds should be considered in urban planning of big cities in developing countries.展开更多
The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence spe...The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.展开更多
BMR （basal metabolic rate）, body mass and organ masses of tree sparrows （Passer montanus） were measured to analyze the correlation between organ masses and BMR in tree sparrows, and to evaluate the underlying phys...BMR （basal metabolic rate）, body mass and organ masses of tree sparrows （Passer montanus） were measured to analyze the correlation between organ masses and BMR in tree sparrows, and to evaluate the underlying physiological causes of difference in BMR. Adult tree sparrows were live-trapped by mist net in Qiqihar City, Heilongjiang Province （47°29′N, 124°02′E）. The closed circuit respirometer was used to measure the metabolic rate （MR）, and controlled the ambient temperature by using a water bath （±0.5℃）. Body masses were measured to the nearest 0.01 g before and after BMR measurements with a Sartorius balance （model BT25S）. The mean value was recorded as body mass. Wet and dry masses of several organs were measured, too. BMR was （4.276± 0.385） mL O2/（g·h） and mean body mass was （18.522±0.110） g. Since not all the variables were normal distributed, a log10- transformation of those variables was employed to linearize them, prior to analyses. Simple regression analyses indicated that most organ masses showed a significant high correlation with body mass. Both the small intestine and rectum masses were notable exception to that trend. The body-mass-adjusted residual analysis showed that only the kidney wet mass, brain mass, stomach mass, small mass and rectum wet mass correlated with BMR. In addition, correlations between several organ masses and BMR were observed. Because of the inter-correlations of organ masses, a principal component analysis （PCA） was performed to redefine the morphological variability. The first four components whose eigenvalues were greater than 1 could explain 75.2% variance of BMR. The first component, whose proportion reached 30.19%, was affected mainly by stomach mass, small intestine mass and rectum mass. Therefore, the results supported the hypothesis that BMR was controlled by some ＂expensive metabolic＂ organs展开更多
Fear and anxiety may be adaptive responses to life-threatening situations, and animals may communicate fear to others vocally. A fundamental understanding of fear inducing sounds is important for both wildlife conserv...Fear and anxiety may be adaptive responses to life-threatening situations, and animals may communicate fear to others vocally. A fundamental understanding of fear inducing sounds is important for both wildlife conservation and management because it helps us understand how to design repellents and also how （and why） animals may be negatively impacted by anthropogenic sounds. Nonlinear phenomena--sounds produced by the desynchronization of vibrations in a sound production system-are commonly found in stress-induced animal vocalizations, such as in alarm calls, mobbing calls, and fear screams. There are several functional hypotheses for these nonlinear phenomena. One specific hypothesis is the unpredictability hypothesis, which suggests that because nonlinear phenomena are more variable and somewhat unpredictable, animals are less likely to habituate to them. Animals should, therefore, have a prolonged response to sounds with nonlinear phenomena than sounds without them. Most of the studies involving nonlinear phenomena have used mammalian subjects and conspecific stimuli. Our study fo- cused on white-crowned sparrows （Zonotrichia leucophrys ssp. oriantha） and used synthesized acoustic stimuli to investigate behavioral responses to stimuli with and without nonlinear phenomena. We predicted that birds would be less relaxed after hearing a stimulus with a nonlinear component. We calculated the difference from baseline of proportion of time spent in relaxed behaviors and performed pair-wise comparisons between a pure tone control stimulus and each of three experimental stimuli, including a frequency jump up, a frequency jump down, and white noise. These comparisons showed that in the 30q50 s after the playback experiment, birds were significantly less relaxed after hearing noise or an abrupt frequency jump down an octave but not an abrupt frequency jump up an octave or a pure tone. Nonlinear phenomena, therefore, may be generally arousing to animals and may explain why these acoustic properties are commonly found in animal signals associated with fear [Current Zoology 60 （4）： 534-541, 2014].展开更多
Following an introduction, non-native species are exposed to environments that differ from those found in their native range; further, as these non-native species expand beyond the site of introduction, they must cons...Following an introduction, non-native species are exposed to environments that differ from those found in their native range; further, as these non-native species expand beyond the site of introduction, they must constantly adapt to novel environ- ments. Although introduced species are present across most ecosystems, few species have successfully established themselves on a truly global scale. One such species, the house sparrow Passer domesticus, is now one of the world＇s most broadly distributed vertebrate species and has been introduced to a great part of its current range. To date, work on four continents suggests both ge- netic and phenotypic variation exists between native and introduced ranges. As such, house sparrows represent an excellent op- portunity to study adaptations to novel environments and how these adaptations are derived. The global distribution of this spe- cies and the multiple independent introductions to geographically isolated sites allow researchers to ask questions regarding ge- netic variation and adaptation on a global scale. Here, we summarize the molecular studies of invasive house sparrows from the earliest work using allozymes through more recent work on epigenetics; using these studies, we discuss patterns of dispersal of this species. We then discuss future directions in techniques （e.g. next generation sequencing） and how they will provide new in- sight into questions that are fundamental to invasion biology. Finally, we discuss how continued research on the house sparrow in light of these genetic changes and adaptations will elucidate answers of adaptation, invasion biology, range expansion, and resi- lience in vertebrate systems generally展开更多
The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration o...The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.展开更多
Background:Efficient and selective utilization of metabolic substrates is one of the key strategies in high-altitude animals to cope with hypoxia and hypothermia.Previous findings have shown that the energy substrate ...Background:Efficient and selective utilization of metabolic substrates is one of the key strategies in high-altitude animals to cope with hypoxia and hypothermia.Previous findings have shown that the energy substrate utilization of highland animals varies with evolutionary history and phylogeny.The heart is a proxy for the cardiopulmonary system,and the metabolic substrate utilization in the myocardium is also under the strong selective pressure of chronically hypoxic and hypothermic environments.However,little information is available on the physiological adjustments in relation to metabolic substrate utilization in the myocardium for coping with high-altitude environments.Methods:We compared the metabolic enzyme activities,including hexokinase(HK),phosphofructokinase(PFK),pyruvate kinase(PK),citrate synthase(CS),carnitine palmitoyl transferase 1(CPT-1),lactic dehydrogenase(LDH),and creatine kinase(CK),and metabolic substrate contents including glucose(Glu),triglyceride(TG),and free fatty acid(FFA)in the myocardium of a typical human commensal species,Eurasian Tree Sparrows(Passer montanus)between the Qinghai-Tibet Plateau(the QTP,3230 m)and low altitude population(Shijiazhuang,80 m),and between sexes.Results:Among the seven metabolic enzymes and three substrates investigated,we identified no significant differences in PK,CPT-1,HK,CS,LDH,and CK activities and TG content of the myocardium between high and low altitude populations.However,the QTP sparrows had significantly lower Glu content and PFK activities but higher FFA content relative to their lowland counterparts.In addition,male sparrows had higher myocardial HK and CS activities relative to females,independent of altitude.Conclusions:Our results showed that the QTP sparrows elevated fatty acid utilization rather than glucose preference in the myocardium relative to lowland counterpart,which contributes to uncovering both the physiological adjustments for adapting to the extreme conditions of the QTP,intraspecifically.展开更多
基金supported by the Cooperative Research Project between China Coal Energy Research Institute Co.,Ltd. and Xidian University (No.N-KY-HX-1101-202302-00725)the Key Research and Development Program of Shaanxi Province (No.2017ZDCXL-GY-06-02)。
文摘A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the potential fairness issue that may arise from the maximum sum-rate based objective function and the optical power constraints are set considering the non-negativity of the transmit signal, the requirement of the human eyes safety and all users' quality of service(Qo S). Then, the SSA is utilized to solve this optimization problem. Moreover, to demonstrate the superiority of the proposed strategy, it is compared with the fixed power allocation(FPA) and the gain ratio power allocation(GRPA) schemes. Results show that regardless of the number of users considered, the sum-rate achieved by SSA consistently outperforms that of FPA and GRPA schemes. Specifically, compared to FPA and GRPA schemes, the sum-rate obtained by SSA is increased by 40.45% and 53.44% when the number of users is 7, respectively. The proposed SSA also has better performance in terms of user fairness. This work will benefit the design and development of the NOMA-visible light communication(VLC) systems.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
文摘Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios.
基金funded by the National Natural Science Foundation of China(42174011).
文摘With the continuous improvement of the accuracy of geodetic deformation data,the inversion of seismic source parameters puts forward a higher demand for nonlinear inversion algorithms.In this research,an improved Sparrow Search Algorithm(SSA)is proposed for the seismic source parameter inversion problem.By replacing the original population generation in the improved algorithm with Latin hypercubic sampling,the Sparrow Search Algorithm reduces the repetition of samples in the population initialization.Subsequently,the algorithm introduces adaptive weights in the discoverer generation phase of the sparrow algorithm and combines the Levy flight strategy to make the algorithm more comprehensive and improve the search accuracy during the whole iteration process.Therefore,the improved Latin hypercube-based sparrow search algorithm(ILHSSA)has better advantages in terms of iterative convergence speed and stability.In order to verify the performance of ILHSSA,the basic genetic algorithm(GA)and sparrow search algorithm(SSA)are examined and compared with ILHSSA by simulated earthquakes of two different earthquake types.The simulation experiments show that the improved algorithm ILHSSA outperforms SSA in accuracy and stability.Compared with the GA algorithm,ILHSSA can achieve the same inversion accuracy as GA,and it even surpasses GA in inversion speed and the inversion results of some parameters,demonstrating better stability.Finally,the improved algorithm is used for the 2017 Bodrum-Cos earthquake and the 2016 Amatrice earthquake in Italy.The inversion results all reflect the practicality and reliability of the improved algorithm.
文摘It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional Sparrow Search Algorithm(SSA)suffers from limited global search capability,insufficient population diversity,and slow convergence,which often leads to premature stagnation in local optima.Despite the proposal of various enhanced versions,the effective balancing of exploration and exploitation remains an unsolved challenge.To address the previously mentioned problems,this study proposes a multi-strategy collaborative improved SSA,which systematically integrates four complementary strategies:(1)the Northern Goshawk Optimization(NGO)mechanism enhances global exploration through guided prey-attacking dynamics;(2)an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom;(3)a dual chaotic initialization method(Bernoulli and Sinusoidal maps)increases population diversity and distribution uniformity;and(4)an elite retention strategy maintains solution quality and prevents degradation during iterations.These strategies cooperate synergistically,forming a tightly coupled optimization framework that significantly improves search efficiency and robustness.Therefore,this paper names it NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization.Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude.Compared with SSA,GWO,ISSA,and CSSOA,NTSSA improves solution accuracy by up to 14.3%(F8)and 99.8%(F12),while accelerating convergence by approximately 1.5–2×.The Wilcoxon rank-sum test(p<0.05)indicates that NTSSA demonstrates a statistically substantial performance advantage.Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation,chaos-based diversification,and elite preservation ensures both high convergence accuracy and global stability.This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation,offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design.
基金supported by the National Natural Science Foundation of China (Grant No. 30900181)“111 Project” (2008-B08044)
文摘With rapid urbanization occurring throughout China,the existence of Tree Sparrows (Passer montanus) in big cities is likely to be affected by a decrease in habitat and food availability.Can the urban Tree Sparrow adapt to these changes? To elucidate this question,we studied the effect of urbanization on the abundance and distribution of Tree Sparrows in Beijing.We found the abundance of the Tree Sparrow negatively correlated with an urbanization score.Sparrow abundance was very low in residential areas with high-rise buildings,commercial centers and main roads,while their numbers were significantly higher in parks,university campuses,low building residential and suburban areas.Environmental factors within the 50 m and 200 m scales were most suitable in predicting the distribution of Tree Sparrows during winter,while factors within 50 m and 400 m scales are suitable during the breeding season.During winter,the number of conifer trees and pedestrians were the major factors at the 50 m scale,while the area of high-rise buildings and vegetation become the predominant factors on a 200 m scale.Alternatively,during the breeding season the area of low buildings and the number of conifers and pedestrians were the main factors on the 50 m scale while the area of high-rise buildings and vegetation remained the most important factors on the 400 m scale.These results indicate that highly urbanized areas are not suitable habitats for the Tree Sparrow,although this species can adapt to human environments.Food and nest sites for urban birds should be considered in urban planning of big cities in developing countries.
基金The Science Foundation of Shanxi Province,China(2020JQ-481,2021JM-224)Aero Science Foundation of China(201951096002).
文摘The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.
基金Supported by Natural Foundation for Youth of Daqing Normal College (YZQ004)
文摘BMR （basal metabolic rate）, body mass and organ masses of tree sparrows （Passer montanus） were measured to analyze the correlation between organ masses and BMR in tree sparrows, and to evaluate the underlying physiological causes of difference in BMR. Adult tree sparrows were live-trapped by mist net in Qiqihar City, Heilongjiang Province （47°29′N, 124°02′E）. The closed circuit respirometer was used to measure the metabolic rate （MR）, and controlled the ambient temperature by using a water bath （±0.5℃）. Body masses were measured to the nearest 0.01 g before and after BMR measurements with a Sartorius balance （model BT25S）. The mean value was recorded as body mass. Wet and dry masses of several organs were measured, too. BMR was （4.276± 0.385） mL O2/（g·h） and mean body mass was （18.522±0.110） g. Since not all the variables were normal distributed, a log10- transformation of those variables was employed to linearize them, prior to analyses. Simple regression analyses indicated that most organ masses showed a significant high correlation with body mass. Both the small intestine and rectum masses were notable exception to that trend. The body-mass-adjusted residual analysis showed that only the kidney wet mass, brain mass, stomach mass, small mass and rectum wet mass correlated with BMR. In addition, correlations between several organ masses and BMR were observed. Because of the inter-correlations of organ masses, a principal component analysis （PCA） was performed to redefine the morphological variability. The first four components whose eigenvalues were greater than 1 could explain 75.2% variance of BMR. The first component, whose proportion reached 30.19%, was affected mainly by stomach mass, small intestine mass and rectum mass. Therefore, the results supported the hypothesis that BMR was controlled by some ＂expensive metabolic＂ organs
文摘Fear and anxiety may be adaptive responses to life-threatening situations, and animals may communicate fear to others vocally. A fundamental understanding of fear inducing sounds is important for both wildlife conservation and management because it helps us understand how to design repellents and also how （and why） animals may be negatively impacted by anthropogenic sounds. Nonlinear phenomena--sounds produced by the desynchronization of vibrations in a sound production system-are commonly found in stress-induced animal vocalizations, such as in alarm calls, mobbing calls, and fear screams. There are several functional hypotheses for these nonlinear phenomena. One specific hypothesis is the unpredictability hypothesis, which suggests that because nonlinear phenomena are more variable and somewhat unpredictable, animals are less likely to habituate to them. Animals should, therefore, have a prolonged response to sounds with nonlinear phenomena than sounds without them. Most of the studies involving nonlinear phenomena have used mammalian subjects and conspecific stimuli. Our study fo- cused on white-crowned sparrows （Zonotrichia leucophrys ssp. oriantha） and used synthesized acoustic stimuli to investigate behavioral responses to stimuli with and without nonlinear phenomena. We predicted that birds would be less relaxed after hearing a stimulus with a nonlinear component. We calculated the difference from baseline of proportion of time spent in relaxed behaviors and performed pair-wise comparisons between a pure tone control stimulus and each of three experimental stimuli, including a frequency jump up, a frequency jump down, and white noise. These comparisons showed that in the 30q50 s after the playback experiment, birds were significantly less relaxed after hearing noise or an abrupt frequency jump down an octave but not an abrupt frequency jump up an octave or a pure tone. Nonlinear phenomena, therefore, may be generally arousing to animals and may explain why these acoustic properties are commonly found in animal signals associated with fear [Current Zoology 60 （4）： 534-541, 2014].
文摘Following an introduction, non-native species are exposed to environments that differ from those found in their native range; further, as these non-native species expand beyond the site of introduction, they must constantly adapt to novel environ- ments. Although introduced species are present across most ecosystems, few species have successfully established themselves on a truly global scale. One such species, the house sparrow Passer domesticus, is now one of the world＇s most broadly distributed vertebrate species and has been introduced to a great part of its current range. To date, work on four continents suggests both ge- netic and phenotypic variation exists between native and introduced ranges. As such, house sparrows represent an excellent op- portunity to study adaptations to novel environments and how these adaptations are derived. The global distribution of this spe- cies and the multiple independent introductions to geographically isolated sites allow researchers to ask questions regarding ge- netic variation and adaptation on a global scale. Here, we summarize the molecular studies of invasive house sparrows from the earliest work using allozymes through more recent work on epigenetics; using these studies, we discuss patterns of dispersal of this species. We then discuss future directions in techniques （e.g. next generation sequencing） and how they will provide new in- sight into questions that are fundamental to invasion biology. Finally, we discuss how continued research on the house sparrow in light of these genetic changes and adaptations will elucidate answers of adaptation, invasion biology, range expansion, and resi- lience in vertebrate systems generally
基金supported by the Basic Research Special Plan of Yunnan Provincial Department of Science and Technology-General Project(Grant No.202101AT070094)。
文摘The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.
基金supported by the National Natural Science Foundation of China(NSFC,No.31971413)to DL and NSFC(No.31770445)to YWthe Second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK0501)+2 种基金the Natural Science Foundation of Hebei Province(NSFHB,C2020205038)to DLthe Foundation of Hebei Normal University(L2019B26)to CJthe Post-doctoral Research Programm to PD。
文摘Background:Efficient and selective utilization of metabolic substrates is one of the key strategies in high-altitude animals to cope with hypoxia and hypothermia.Previous findings have shown that the energy substrate utilization of highland animals varies with evolutionary history and phylogeny.The heart is a proxy for the cardiopulmonary system,and the metabolic substrate utilization in the myocardium is also under the strong selective pressure of chronically hypoxic and hypothermic environments.However,little information is available on the physiological adjustments in relation to metabolic substrate utilization in the myocardium for coping with high-altitude environments.Methods:We compared the metabolic enzyme activities,including hexokinase(HK),phosphofructokinase(PFK),pyruvate kinase(PK),citrate synthase(CS),carnitine palmitoyl transferase 1(CPT-1),lactic dehydrogenase(LDH),and creatine kinase(CK),and metabolic substrate contents including glucose(Glu),triglyceride(TG),and free fatty acid(FFA)in the myocardium of a typical human commensal species,Eurasian Tree Sparrows(Passer montanus)between the Qinghai-Tibet Plateau(the QTP,3230 m)and low altitude population(Shijiazhuang,80 m),and between sexes.Results:Among the seven metabolic enzymes and three substrates investigated,we identified no significant differences in PK,CPT-1,HK,CS,LDH,and CK activities and TG content of the myocardium between high and low altitude populations.However,the QTP sparrows had significantly lower Glu content and PFK activities but higher FFA content relative to their lowland counterparts.In addition,male sparrows had higher myocardial HK and CS activities relative to females,independent of altitude.Conclusions:Our results showed that the QTP sparrows elevated fatty acid utilization rather than glucose preference in the myocardium relative to lowland counterpart,which contributes to uncovering both the physiological adjustments for adapting to the extreme conditions of the QTP,intraspecifically.
