By analyzing the concept of conservation-minded garden,connotations of such gardens and their significance for the garden construction are specified.From the perspectives of spatial layout,compound planting pattern,tr...By analyzing the concept of conservation-minded garden,connotations of such gardens and their significance for the garden construction are specified.From the perspectives of spatial layout,compound planting pattern,tridimensional greening,terrain processing,utilization of underground spaces,the significance of resource-efficient elements in the construction of conservation-minded garden is respectively discussed.This study proposes that spatial layout of the conservation-minded garden should build vertical transportation,save green spaces,take terrain features into consideration,expand three-dimensional spaces,reasonably plan road network,and improve utilization rate of green spaces;advantages of the compound planting pattern are analyzed;tridimensional greening pattern is analyzed from the perspectives of roof greening and vertical greening;concepts of "core" and "surface" are highlighted in terrain processing;land-saving efficiency of underground garage and market are introduced in terms of the utilization of underground spaces.Design cases are taken as examples to summarize the application of resource-efficient elements to better serve the landscaping industry and provide references for the construction of conservation-minded garden.展开更多
The world today is .faced with the critical challenge of developing industries .for both high technological efficiency and possibly less impact on resources and the environment. This paper employs the Slacks-based mea...The world today is .faced with the critical challenge of developing industries .for both high technological efficiency and possibly less impact on resources and the environment. This paper employs the Slacks-based measure （SBM） to conduct an empirical analysis of China＇s industrial environmental efficiency between 1998 and 2008. We.found a slight decline in SBM nationwide due to the combined effects of resource^environmental constraint as well as technological progress. From a static perspective, there is a clear disparity in coordination among regions, particularly between western regions and eastern coastal regions. From a dynamic perspective, there is limited change in the coordination of industries with natural resources and the environment in coastal and central regions, but a marked decline in the northeast and western regions. This contrast can be traced to the .following reasons： the capital-intensive structure of the economy and the high proportion of large industrial enterprises that restrict the improvement of SBM environmental efficiency. Higher living standards, indigenous innovation, acquisition of new technologies, and foreign direct investment will be conducive to industries＇synchronous development with natural resources and the environment.展开更多
Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,...Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,a feedback-driven,explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier.AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones,followed by post-hoc SHAP-guided retraining to refine feature importance,forming a feedback loopwhere only the most impactful attributes are reused to retrain themodel.This iterative refinement reduces computational overhead,accelerates detection latency,and improves transparency.Evaluated on the CIC IoT 2023 dataset,AutoSHARC achieves 98.98%accuracy,98.9%F1-score,and strong robustness with a Matthews Correlation Coefficient of 0.98 and Cohen’s Kappa of 0.98.The final model contains only 531,272 trainable parameters with a compact 2 MB size,enabling real-time deployment on resource-constrained IoT nodes.By combining explainable AI with iterative feature refinement,AutoSHARC provides scalable and trustworthy intrusion detection while preserving key QoS indicators such as latency,throughput,and reliability.展开更多
文摘By analyzing the concept of conservation-minded garden,connotations of such gardens and their significance for the garden construction are specified.From the perspectives of spatial layout,compound planting pattern,tridimensional greening,terrain processing,utilization of underground spaces,the significance of resource-efficient elements in the construction of conservation-minded garden is respectively discussed.This study proposes that spatial layout of the conservation-minded garden should build vertical transportation,save green spaces,take terrain features into consideration,expand three-dimensional spaces,reasonably plan road network,and improve utilization rate of green spaces;advantages of the compound planting pattern are analyzed;tridimensional greening pattern is analyzed from the perspectives of roof greening and vertical greening;concepts of "core" and "surface" are highlighted in terrain processing;land-saving efficiency of underground garage and market are introduced in terms of the utilization of underground spaces.Design cases are taken as examples to summarize the application of resource-efficient elements to better serve the landscaping industry and provide references for the construction of conservation-minded garden.
文摘The world today is .faced with the critical challenge of developing industries .for both high technological efficiency and possibly less impact on resources and the environment. This paper employs the Slacks-based measure （SBM） to conduct an empirical analysis of China＇s industrial environmental efficiency between 1998 and 2008. We.found a slight decline in SBM nationwide due to the combined effects of resource^environmental constraint as well as technological progress. From a static perspective, there is a clear disparity in coordination among regions, particularly between western regions and eastern coastal regions. From a dynamic perspective, there is limited change in the coordination of industries with natural resources and the environment in coastal and central regions, but a marked decline in the northeast and western regions. This contrast can be traced to the .following reasons： the capital-intensive structure of the economy and the high proportion of large industrial enterprises that restrict the improvement of SBM environmental efficiency. Higher living standards, indigenous innovation, acquisition of new technologies, and foreign direct investment will be conducive to industries＇synchronous development with natural resources and the environment.
文摘Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,a feedback-driven,explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier.AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones,followed by post-hoc SHAP-guided retraining to refine feature importance,forming a feedback loopwhere only the most impactful attributes are reused to retrain themodel.This iterative refinement reduces computational overhead,accelerates detection latency,and improves transparency.Evaluated on the CIC IoT 2023 dataset,AutoSHARC achieves 98.98%accuracy,98.9%F1-score,and strong robustness with a Matthews Correlation Coefficient of 0.98 and Cohen’s Kappa of 0.98.The final model contains only 531,272 trainable parameters with a compact 2 MB size,enabling real-time deployment on resource-constrained IoT nodes.By combining explainable AI with iterative feature refinement,AutoSHARC provides scalable and trustworthy intrusion detection while preserving key QoS indicators such as latency,throughput,and reliability.
