The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterpri...The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].展开更多
A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in trans...A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in transform-domain. Then, an iterative algorithm is presented to solve the deconvolution. Because the window-function is available to describe arbitrary shape, our algorithm is suitable for restoring irregular segment of data loss, including square-block. Finally, several simulation tests are done and results prove that the algorithm is valid.展开更多
Mangrove restoration is recognized as an effective strategy for enhancing the carbon storage capacity of natural ecosystems,advancing toward the“carbon neutrality”goal.The carbon storage effects of ecological restor...Mangrove restoration is recognized as an effective strategy for enhancing the carbon storage capacity of natural ecosystems,advancing toward the“carbon neutrality”goal.The carbon storage effects of ecological restoration efforts remain insufficiently understood as previous studies have focused on carbon storage dynamics in ecosystems,yet the specific impacts of targeted mangrove restoration are less explored.This study utilizes multi-temporal remote sensing data and actual restoration data from Dongzhai Harbor Hainan Island to identify the mangrove wetland coverage and quantify the spatiotemporal evolution of carbon storage under various restoration efforts using the InVEST model.Additionally,we employed the PLUS model to simulate and compare carbon storage potential under multiple development goals.The findings reveal the following:(a)Mangrove restoration significantly increased the area of land with high carbon sink capability,resulting in a regional carbon storage increase of 210,001.68 tons from 2015 to 2021,with 97%of this increase attributable to ecological restoration.(b)Mangrove coverage is crucial for regional carbon storage,with an average of 443 tons of carbon stored per hectare.Decreases in carbon storage occurred mainly during the conversion of mangroves to aquaculture,and forests/agriculture to residential areas.Increases in carbon storage were seen in the reverse transitions.(c)Comparing the scenarios focused solely on mangrove protection with cultivated land protection,the carbon storage in Dongzhai Harbor is projected to reach its maximum by 2045 under the carbon storage priority scenario.Our findings build a scientific foundation for formulating effective mangrove conservation and restoration strategies.展开更多
文摘The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].
基金Supported by the National Natural Science Foundation of China (No.60072012).
文摘A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in transform-domain. Then, an iterative algorithm is presented to solve the deconvolution. Because the window-function is available to describe arbitrary shape, our algorithm is suitable for restoring irregular segment of data loss, including square-block. Finally, several simulation tests are done and results prove that the algorithm is valid.
基金financially supported by the National Natural Science Foundation of China(no.42371272)the Hainan Provincial Natural Science Foundation of China(721RC1048)
文摘Mangrove restoration is recognized as an effective strategy for enhancing the carbon storage capacity of natural ecosystems,advancing toward the“carbon neutrality”goal.The carbon storage effects of ecological restoration efforts remain insufficiently understood as previous studies have focused on carbon storage dynamics in ecosystems,yet the specific impacts of targeted mangrove restoration are less explored.This study utilizes multi-temporal remote sensing data and actual restoration data from Dongzhai Harbor Hainan Island to identify the mangrove wetland coverage and quantify the spatiotemporal evolution of carbon storage under various restoration efforts using the InVEST model.Additionally,we employed the PLUS model to simulate and compare carbon storage potential under multiple development goals.The findings reveal the following:(a)Mangrove restoration significantly increased the area of land with high carbon sink capability,resulting in a regional carbon storage increase of 210,001.68 tons from 2015 to 2021,with 97%of this increase attributable to ecological restoration.(b)Mangrove coverage is crucial for regional carbon storage,with an average of 443 tons of carbon stored per hectare.Decreases in carbon storage occurred mainly during the conversion of mangroves to aquaculture,and forests/agriculture to residential areas.Increases in carbon storage were seen in the reverse transitions.(c)Comparing the scenarios focused solely on mangrove protection with cultivated land protection,the carbon storage in Dongzhai Harbor is projected to reach its maximum by 2045 under the carbon storage priority scenario.Our findings build a scientific foundation for formulating effective mangrove conservation and restoration strategies.
