Background: The Bar-headed Goose(Anser indicus) is a species that relies heavily on the plateau wetlands of Asia and whose population was thought to be declining. Over the past decade, south-central Tibet, one of the ...Background: The Bar-headed Goose(Anser indicus) is a species that relies heavily on the plateau wetlands of Asia and whose population was thought to be declining. Over the past decade, south-central Tibet, one of the most important wintering grounds, supported large numbers of Bar-headed Geese, but the population had not been regularly monitored in this area.Methods: We surveyed wintering Bar-headed Geese along the Yarlung Zangbo, Lhasa and Nyang Qu rivers, the three major river valleys and their tributaries in south-central Tibet in January 2014 and recorded their location, flock size and habitat utilization. Based on these data and the latest wintering counts elsewhere, we revised the population estimate for this species.Results: We recorded more than 67,000 Bar-headed Geese in south-central Tibet during January 2014. By geographic area, the geese were most abundant in the Lhasa River valley(38.5%) and the Nyang Qu River valley(31.0%), and by administrative division in Lhunzhub(27.2%) and Shigatse(26.7%). Bar-headed Geese were most often observed feeding in winter wheat fields and ploughed fields, resting on pastureland and marshes. The approximate number of 67,000 geese recorded in Tibet is more than four times the estimate of 1993 for the same region and exceeds the most recent world population estimate of 52,000–60,000. Based on our work in Tibet and the latest wintering counts available from other areas, we revised the estimated population size of this species to 97,000–118,000.Conclusions: Our result reveals a remarkable increase in the number of Bar-headed Geese wintering in south-central Tibet. This population increase most likely stems from a proliferation of cropland and especially winter wheat fields in south-central Tibet. This habitat improvement may also cause short-stopping of the Bar-headed Goose and thus reduce mortality of the geese that would otherwise undertake a somewhat daunting trans-Himalayan migration.展开更多
To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation ...To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.展开更多
In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps)...In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic processes.Prospects of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital solutions.An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines.Integrating Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine learningmodels.Thus,the model performs and yields more accurate results.This work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees algorithms.The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to.99348.The proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank test.Some of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.展开更多
We conduct a theoretical analysis of the massive and tunable Goos–Hänchen(GH) shift on a polar crystal covered with periodical black phosphorus(BP)-patches in the THz range. The surface plasmon phonon polaritons...We conduct a theoretical analysis of the massive and tunable Goos–Hänchen(GH) shift on a polar crystal covered with periodical black phosphorus(BP)-patches in the THz range. The surface plasmon phonon polaritons(SPPPs), which are coupled by the surface phonon polaritons(SPh Ps) and surface plasmon polaritons(SPPs), can greatly increase GH shifts.Based on the in-plane anisotropy of BP, two typical metasurface models are designed and investigated. An enormous GH shift of about-7565.58 λ_(0) is achieved by adjusting the physical parameters of the BP-patches. In the designed metasurface structure, the maximum sensitivity accompanying large GH shifts can reach about 6.43 × 10^(8) λ_(0)/RIU, which is extremely sensitive to the size, carrier density, and layer number of BP. Compared with a traditional surface plasmon resonance sensor, the sensitivity is increased by at least two orders of magnitude. We believe that investigating metasurface-based SPPPs sensors could lead to high-sensitivity biochemical detection applications.展开更多
基金funded by the Wildlife Rescue Project from the Department of Wildlife Protection and Nature Reserve Management, State Forestry Administration (SFA) of Chinathe Project of Surveillance of H7N9 in Wild Birds (No. 201404404) from the Department of Science and Technology, SFAco-funded by the International Crane Foundation
文摘Background: The Bar-headed Goose(Anser indicus) is a species that relies heavily on the plateau wetlands of Asia and whose population was thought to be declining. Over the past decade, south-central Tibet, one of the most important wintering grounds, supported large numbers of Bar-headed Geese, but the population had not been regularly monitored in this area.Methods: We surveyed wintering Bar-headed Geese along the Yarlung Zangbo, Lhasa and Nyang Qu rivers, the three major river valleys and their tributaries in south-central Tibet in January 2014 and recorded their location, flock size and habitat utilization. Based on these data and the latest wintering counts elsewhere, we revised the population estimate for this species.Results: We recorded more than 67,000 Bar-headed Geese in south-central Tibet during January 2014. By geographic area, the geese were most abundant in the Lhasa River valley(38.5%) and the Nyang Qu River valley(31.0%), and by administrative division in Lhunzhub(27.2%) and Shigatse(26.7%). Bar-headed Geese were most often observed feeding in winter wheat fields and ploughed fields, resting on pastureland and marshes. The approximate number of 67,000 geese recorded in Tibet is more than four times the estimate of 1993 for the same region and exceeds the most recent world population estimate of 52,000–60,000. Based on our work in Tibet and the latest wintering counts available from other areas, we revised the estimated population size of this species to 97,000–118,000.Conclusions: Our result reveals a remarkable increase in the number of Bar-headed Geese wintering in south-central Tibet. This population increase most likely stems from a proliferation of cropland and especially winter wheat fields in south-central Tibet. This habitat improvement may also cause short-stopping of the Bar-headed Goose and thus reduce mortality of the geese that would otherwise undertake a somewhat daunting trans-Himalayan migration.
基金funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,Grant No.(44-PRFA-P-48).
文摘To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R 308)。
文摘In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic processes.Prospects of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital solutions.An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines.Integrating Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine learningmodels.Thus,the model performs and yields more accurate results.This work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees algorithms.The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to.99348.The proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank test.Some of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.
基金Project supported by the Natural Science Foundation of Heilongjiang Province of China (Grant No.LH2020A014)the Graduate Students' Research Innovation Project of Harbin Normal University (Grant No.HSDSSCX2022-47)。
文摘We conduct a theoretical analysis of the massive and tunable Goos–Hänchen(GH) shift on a polar crystal covered with periodical black phosphorus(BP)-patches in the THz range. The surface plasmon phonon polaritons(SPPPs), which are coupled by the surface phonon polaritons(SPh Ps) and surface plasmon polaritons(SPPs), can greatly increase GH shifts.Based on the in-plane anisotropy of BP, two typical metasurface models are designed and investigated. An enormous GH shift of about-7565.58 λ_(0) is achieved by adjusting the physical parameters of the BP-patches. In the designed metasurface structure, the maximum sensitivity accompanying large GH shifts can reach about 6.43 × 10^(8) λ_(0)/RIU, which is extremely sensitive to the size, carrier density, and layer number of BP. Compared with a traditional surface plasmon resonance sensor, the sensitivity is increased by at least two orders of magnitude. We believe that investigating metasurface-based SPPPs sensors could lead to high-sensitivity biochemical detection applications.
