For a long time,rural economic development has put economic benefits in the first place,ignoring the impact of unreasonable land use on local land resources and ecological environment,which is not conducive to the lon...For a long time,rural economic development has put economic benefits in the first place,ignoring the impact of unreasonable land use on local land resources and ecological environment,which is not conducive to the long-term high-quality development of local economy and sustainable land use.There is an urgent need to study the relationship between sustainable land use and rural economic development in order to achieve the coordinated development of the two.By using the methods of literature research and field investigation,this paper studies Meining Village,Tiandong County,Guangxi Zhuang Autonomous Region.The study found that farmers tend to plant pure eucalyptus forest,single land use structure,short-term rotation planting model and traditional afforestation and land preparation technology are not conducive to sustainable land use in forest areas,and affected by economic and educational factors,farmers livelihood is relatively simple,so the development of rural economy will be restricted.In view of the above problems,this paper puts forward the following solutions:changing the land use model of Meining Village to promote sustainable land use;introducing advanced science and technology and diversified livelihood ways to promote the sustainable development of rural economy;building a virtuous circle of sustainable land use and rural economic development.展开更多
Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence netw...Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence networks.However,the resource constraints of devices,the delay-sensitive nature of tasks,and the dynamic environmental conditions present significant challenges.While Multi-Armed Bandit(MAB)algorithms have been explored for task offloading,their performance is often constrained in highly dynamic scenarios with complex,nonlinear utility dependencies.To address these challenges,we propose a Group Neural MAB(GN-MAB)approach that jointly optimizes idle device selection(i.e.,arm groups)and DNN partitioning decisions(i.e.,arms)for efficient collaborative inference.Building upon the neural upper confidence bound algorithm,GN-MAB dynamically balances the exploration and exploitation,enabling continuous adaptation of offloading strategies across sequential inference tasks.Extensive experimental results show that GN-MAB outperforms baseline approaches,achieving superior inference performance while exhibiting robust adaptability to the fluctuating conditions of maritime environments.展开更多
文摘For a long time,rural economic development has put economic benefits in the first place,ignoring the impact of unreasonable land use on local land resources and ecological environment,which is not conducive to the long-term high-quality development of local economy and sustainable land use.There is an urgent need to study the relationship between sustainable land use and rural economic development in order to achieve the coordinated development of the two.By using the methods of literature research and field investigation,this paper studies Meining Village,Tiandong County,Guangxi Zhuang Autonomous Region.The study found that farmers tend to plant pure eucalyptus forest,single land use structure,short-term rotation planting model and traditional afforestation and land preparation technology are not conducive to sustainable land use in forest areas,and affected by economic and educational factors,farmers livelihood is relatively simple,so the development of rural economy will be restricted.In view of the above problems,this paper puts forward the following solutions:changing the land use model of Meining Village to promote sustainable land use;introducing advanced science and technology and diversified livelihood ways to promote the sustainable development of rural economy;building a virtuous circle of sustainable land use and rural economic development.
基金supported by the Fundamental Research Funds for the Central Universities,South-Central MinZu University(No.CZQ25005)the Young Innovative Talents Project of Department of Education of Guangdong Province(Nos.2025KQNCX261,2025KQNCX262)+1 种基金the National Natural Science Foundation of China(No.62201621)the Young Scientific and Technological Talents Training Program of Hubei Province(No.2025DJA072).
文摘Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence networks.However,the resource constraints of devices,the delay-sensitive nature of tasks,and the dynamic environmental conditions present significant challenges.While Multi-Armed Bandit(MAB)algorithms have been explored for task offloading,their performance is often constrained in highly dynamic scenarios with complex,nonlinear utility dependencies.To address these challenges,we propose a Group Neural MAB(GN-MAB)approach that jointly optimizes idle device selection(i.e.,arm groups)and DNN partitioning decisions(i.e.,arms)for efficient collaborative inference.Building upon the neural upper confidence bound algorithm,GN-MAB dynamically balances the exploration and exploitation,enabling continuous adaptation of offloading strategies across sequential inference tasks.Extensive experimental results show that GN-MAB outperforms baseline approaches,achieving superior inference performance while exhibiting robust adaptability to the fluctuating conditions of maritime environments.
