Alottery was broadcast live both on TV and over the Internet in Beijing on January 26. The "talk-of-the-town" lottery attracted at least 187,000 pairs of attentive eyes of people who were not yearning for
In 2015,the discovery of mammalian fossils,paleolithic artifacts,and burned bones in the Maoershan Cave of the Guilin Basin,northeastern Guangxi,indicated that it is a late Middle Pleistocene Paleolithic site.In 2021,...In 2015,the discovery of mammalian fossils,paleolithic artifacts,and burned bones in the Maoershan Cave of the Guilin Basin,northeastern Guangxi,indicated that it is a late Middle Pleistocene Paleolithic site.In 2021,stratigraphic sectioning and the systematic screening of small mammal sand samples were conducted.This paper presents a comprehensive account of the new material of the fruit bat Rousettus leschenaultii,accompanied by a concise overview of the non-volant micromammals that coexisted with it in the Quaternary sediments of the Maoershan Cave.This finding marks the second occurrence of fruit bat fossils in China,contributing to our understanding of the dental morphology and past geographical distribution of Rousettus.The micromammalian assemblage of the Maoershan Cave is composed of 3 orders,9 families,26 genera,and 30 species,and exhibits notable similarities with those of the late Middle Pleistocene Yumi Cave,Xinglong Cave,Yanhui Cave,Mawokou Cave,Zhongliangshan,and Chuan Cave faunas.The biochronology of the micromammalian assemblage from the Maoershan Cave has been determined to be consistent with the late Middle Pleistocene,providing a new assemblage in the Pleistocene mammalian faunal sequence in southern China.The presence of abundant oriental forested elements indicates that the Guilin Basin used to be a humid and warm subtropical forest paleoenvironment similar to the contemporary environment during the late Middle Pleistocene.展开更多
Temperate forest ecosystems are important habitats for many bat species. However, these habitats are increasingly affected by anthropogenic disturbances, particularly urban development, leading to landscapes with vary...Temperate forest ecosystems are important habitats for many bat species. However, these habitats are increasingly affected by anthropogenic disturbances, particularly urban development, leading to landscapes with varying land cover composition and configuration. Limited research has examined how forest and urban landscape composition and configuration influence bat activity and diversity. Using a multi-year statewide bat acoustic monitoring dataset from North Carolina, USA, we investigated the effects of forest and urban composition and configuration at multiple spatial scales on bat activity and diversity. First, we constructed single-variable landscape index regression models and found that both the composition and configuration of forests and urban developments influenced bat activity and diversity in a species-specific manner. Next, we applied a hierarchical partitioning approach to compare the relative contributions of composition and configuration indices in explaining variance in bat activity. For big brown bats and hoary bats, evergreen forest and urban development composition indices contributed the most to explaining activity variance. In contrast, for eastern red bats, evening bats, and tricolored bats, deciduous forest fragmentation indices describing landscape configuration were the most influential factors. Silver-haired bat activity variance was primarily explained by an evergreen forest fragmentation index. Lastly, urban development configuration indices were the strongest predictors of Mexican free-tailed bat activity and total bat activity. These results suggest that forest and urban landscape configuration should be considered in conservation and management planning for North American temperate forest ecosystems, particularly in regions that have not experienced drastic deforestation in recent decades.展开更多
The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and stron...The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.展开更多
Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine lea...Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.展开更多
Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,wh...Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,which combines an enhanced Bat Algorithm(BA)and Support Vector Machine(SVM).To enhance the optimization capability of BA,water follow search and random follow search are introduced to optimize the efficiency and accuracy of the feature subset search.Experimental validation conducted on the IEEE CEC 2017 benchmark functions and the talented innovative capacity dataset demonstrates the efficacy of the proposed method relative to peer and prominent machine learning models.The experimental results reveal that the predictive accuracy of the bWRBA-SVM-FS model is 97.503%,with a sensitivity of 98.391%.Our findings indicate significant predictors of innovation capacity,including project application goals,educational background,and interdisciplinary thinking abilities.The bWRBA-SVM-FS model offers effective strategies for talent selection in higher education,fostering the development of future research leaders.展开更多
In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployme...In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm(VFBA)using the classical four-node regular distribution as an entry point.The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity,reduce the premature maturity problem caused by population convergence,avoid the over aggregation of individuals in the local optimal region,and enhance the superiority in global search;the virtual force algorithm simulates the attraction and repulsion between individuals,which enables individual bats to precisely locate the optimal solution within the search space.At the same time,the fusion effect of virtual force prompts the bat individuals to move faster to the potential optimal solution.To validate the effectiveness of the fusion algorithm,the benchmark test function is selected for simulation testing.Finally,the simulation result verifies that the VFBA achieves superior coverage and effectively reduces node redundancy compared to the other three regular layout methods.The VFBA also shows better coverage results when compared to other optimization algorithms.展开更多
文摘Alottery was broadcast live both on TV and over the Internet in Beijing on January 26. The "talk-of-the-town" lottery attracted at least 187,000 pairs of attentive eyes of people who were not yearning for
文摘In 2015,the discovery of mammalian fossils,paleolithic artifacts,and burned bones in the Maoershan Cave of the Guilin Basin,northeastern Guangxi,indicated that it is a late Middle Pleistocene Paleolithic site.In 2021,stratigraphic sectioning and the systematic screening of small mammal sand samples were conducted.This paper presents a comprehensive account of the new material of the fruit bat Rousettus leschenaultii,accompanied by a concise overview of the non-volant micromammals that coexisted with it in the Quaternary sediments of the Maoershan Cave.This finding marks the second occurrence of fruit bat fossils in China,contributing to our understanding of the dental morphology and past geographical distribution of Rousettus.The micromammalian assemblage of the Maoershan Cave is composed of 3 orders,9 families,26 genera,and 30 species,and exhibits notable similarities with those of the late Middle Pleistocene Yumi Cave,Xinglong Cave,Yanhui Cave,Mawokou Cave,Zhongliangshan,and Chuan Cave faunas.The biochronology of the micromammalian assemblage from the Maoershan Cave has been determined to be consistent with the late Middle Pleistocene,providing a new assemblage in the Pleistocene mammalian faunal sequence in southern China.The presence of abundant oriental forested elements indicates that the Guilin Basin used to be a humid and warm subtropical forest paleoenvironment similar to the contemporary environment during the late Middle Pleistocene.
基金funding support from the United States Fish and Wildlife Service,the North Carolina Wildlife Resources Commission,and the University of North Carolina at Greensboro,as part of a collective effort for the North American Bat Monitoring Program(NABat).
文摘Temperate forest ecosystems are important habitats for many bat species. However, these habitats are increasingly affected by anthropogenic disturbances, particularly urban development, leading to landscapes with varying land cover composition and configuration. Limited research has examined how forest and urban landscape composition and configuration influence bat activity and diversity. Using a multi-year statewide bat acoustic monitoring dataset from North Carolina, USA, we investigated the effects of forest and urban composition and configuration at multiple spatial scales on bat activity and diversity. First, we constructed single-variable landscape index regression models and found that both the composition and configuration of forests and urban developments influenced bat activity and diversity in a species-specific manner. Next, we applied a hierarchical partitioning approach to compare the relative contributions of composition and configuration indices in explaining variance in bat activity. For big brown bats and hoary bats, evergreen forest and urban development composition indices contributed the most to explaining activity variance. In contrast, for eastern red bats, evening bats, and tricolored bats, deciduous forest fragmentation indices describing landscape configuration were the most influential factors. Silver-haired bat activity variance was primarily explained by an evergreen forest fragmentation index. Lastly, urban development configuration indices were the strongest predictors of Mexican free-tailed bat activity and total bat activity. These results suggest that forest and urban landscape configuration should be considered in conservation and management planning for North American temperate forest ecosystems, particularly in regions that have not experienced drastic deforestation in recent decades.
基金partially supported by MRC(MC_PC_17171)Royal Society(RP202G0230)+8 种基金BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(20P2PF11)Sino-UK Industrial Fund(RP202G0289)LIAS(20P2ED10,20P2RE969)Data Science Enhancement Fund(20P2RE237)Fight for Sight(24NN201)Sino-UK Education Fund(OP202006)BBSRC(RM32G0178B8).
文摘The Bat algorithm,a metaheuristic optimization technique inspired by the foraging behaviour of bats,has been employed to tackle optimization problems.Known for its ease of implementation,parameter tunability,and strong global search capabilities,this algorithm finds application across diverse optimization problem domains.However,in the face of increasingly complex optimization challenges,the Bat algorithm encounters certain limitations,such as slow convergence and sensitivity to initial solutions.In order to tackle these challenges,the present study incorporates a range of optimization compo-nents into the Bat algorithm,thereby proposing a variant called PKEBA.A projection screening strategy is implemented to mitigate its sensitivity to initial solutions,thereby enhancing the quality of the initial solution set.A kinetic adaptation strategy reforms exploration patterns,while an elite communication strategy enhances group interaction,to avoid algorithm from local optima.Subsequently,the effectiveness of the proposed PKEBA is rigorously evaluated.Testing encompasses 30 benchmark functions from IEEE CEC2014,featuring ablation experiments and comparative assessments against classical algorithms and their variants.Moreover,real-world engineering problems are employed as further validation.The results conclusively demonstrate that PKEBA ex-hibits superior convergence and precision compared to existing algorithms.
文摘Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.
基金supported by the Zhejiang Province 14th Five Year Plan Teaching Reform Project(jg20220514).
文摘Developing innovative capabilities in university students is essential for individual career success and broader societal advancement.This study introduces a predictive Feature Selection(FS)model named bWRBA-SVM-FS,which combines an enhanced Bat Algorithm(BA)and Support Vector Machine(SVM).To enhance the optimization capability of BA,water follow search and random follow search are introduced to optimize the efficiency and accuracy of the feature subset search.Experimental validation conducted on the IEEE CEC 2017 benchmark functions and the talented innovative capacity dataset demonstrates the efficacy of the proposed method relative to peer and prominent machine learning models.The experimental results reveal that the predictive accuracy of the bWRBA-SVM-FS model is 97.503%,with a sensitivity of 98.391%.Our findings indicate significant predictors of innovation capacity,including project application goals,educational background,and interdisciplinary thinking abilities.The bWRBA-SVM-FS model offers effective strategies for talent selection in higher education,fostering the development of future research leaders.
基金supported in part by the National Natural Science Foundation of China under Grant No.62271453in part by the National Natural Science Foundation of China No.62101512+2 种基金in part by the Central Support for Local Projects under Grant No.YDZJSX2024D031in part by Project supported by the Shanxi Provincial Foundation for Leaders of Disciplines in Science,China under Grant No.2024Q022in part by Shanxi Province Patent Conversion Special Plan Funding Projects under Grant No.202405004。
文摘In the deployment of wireless networks in two-dimensional outdoor campus spaces,aiming at the problem of efficient coverage of the monitoring area by limited number of access points(APs),this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm(VFBA)using the classical four-node regular distribution as an entry point.The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity,reduce the premature maturity problem caused by population convergence,avoid the over aggregation of individuals in the local optimal region,and enhance the superiority in global search;the virtual force algorithm simulates the attraction and repulsion between individuals,which enables individual bats to precisely locate the optimal solution within the search space.At the same time,the fusion effect of virtual force prompts the bat individuals to move faster to the potential optimal solution.To validate the effectiveness of the fusion algorithm,the benchmark test function is selected for simulation testing.Finally,the simulation result verifies that the VFBA achieves superior coverage and effectively reduces node redundancy compared to the other three regular layout methods.The VFBA also shows better coverage results when compared to other optimization algorithms.
