Transparent Computing (TC) is becoming a promising paradigm in network computing era. Although many researchers believe that TC model has a high requirement for the communication bandwidth, there is ...no research on the communication bandwidth boundary or resource allocation, which impedes the development of TC. This paper focuses on studying an efficient transparent computing resource allocation model in an economic view. First, under the quality of experiments (QoE) ensured, the utility function of clients and transparent computing providers (TCPs) is constructed. After that, the demand boundary of communication bandwidth is analyzed under the ideal transparent computing model. Based on the above analyses, a resource allocation scheme based on double-sided combinational auctions (DCA) is proposed so that the resource can be shared by both the service side and the client side with the welfare of the whole society being maximized. Afterward, the results scheduled in different experimental scenarios are given, which verifies the effectiveness of the proposed strategy. Overall, this work provides an effective resource allocation model for optimizing the performance of TC.
O
wing to their high porosity, high water sorption capacity, and thermal stability, aluminophosphate(AlPO) zeolites have shown promising applications in adsorption heat pump(AHP) systems to utilize ...low-temperature waste heat from heat sources. To accelerate the development of new high-efficiency AHP adsorbents, we report a high-throughput grand canonical Monte Carlo(GCMC) approach to predict the heat storage capabilities of 78 known and 84292 hypothetical AlPO zeolites. We employ three evaluation metrics, including water working capacity, energy density, and regenerability, to comprehensively evaluate the performance of these AlPO structures. Finally, we identify 29 promising candidates with water adsorption properties superior to the commercial adsorbent AQSOA-Z02. This is the first study in large-scale screening of AlPO zeolites for water adsorption. The obtained results will provide important guidance toward the experimental discovery of high-performance AlPO zeolites for AHP applications.
Okinawa Trough receives a large amount of fluvial sediment transported by complex oceanographic circulations, and is an ideal location for the study of paleoceanography and paleoenvironment changes. ...However, our knowledge of the sediment provenance and paleoenvironment evolution in the Trough during the past 17ka is still limited. Based on high-resolution grain size, clay minerals and AMS 14C data of the Core OKI04, we present new evidences of the provenances and paleoenvironment evolution in the middle Okinawa Trough during the last 17ka. Our results indicate that clay-sized terrigenous sediment deposited in the middle Okinawa Trough is mainly from those rivers flowing into East China Sea and Yellow Sea (e.g., Yangtze and Yellow Rivers) and Taiwanese Rivers. Their contributions varied greatly in space and time during the past 17ka. From 16.5 to 13.8ka, sediment was mainly derived from the Yangtze and Yellow Rivers, with little contribution from Taiwan. From 13.8 to 5.4ka, the contributions from the Yangtze and Yellow Rivers oscillated multiple times: low in 13.8–11.6ka, high in 11.6–7.6ka, and back to low in 7.6–5.4ka, whereas the pattern of Taiwanese contribution was opposite. After 5.4ka when the sea level was high and shoreline was far away from the Okinawa Trough, the Yangtze River and eastern Taiwanese Rivers became the main provenances. The contribution from the Yellow River decreased, which is probably because Yellow River mouth was far away from the middle Trough. Sediment grain size data show that two environmentally sensitive populations are 71–7μm and 7–0.5μm. The variation of the populations' mean grain size and content shows that the strength of Kuroshio Current was weak during 16.5–11.6ka, and became strong in 11.6–5.4ka, in which Tsushima Warm Current and Yellow Sea Warm Current formed around 8.5 and 6.5ka, respectively. The strength of Kuroshio Current decreased in 5.4ka and increased since 3.2ka. Based on clay mineralogical analysis, we find that the kaolinite/chlorite could be used as a new effective indicator for the East Asian Winter Monsoon's evolution in the Trough since 17ka.
•Yangtze and Yellow Rivers' contribution to the southern middle Okinawa Trough since 17ka•Grain size sensitive populations are used to investigate Kuroshio Current evolution.•Kaolinite/chlorite ratio is used as a proxy on the East Asian winter Monsoon evolution.•East Asian Winter Monsoon's influence on the transport of sediment to the Trough
Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy ...vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.
In this study, we aimed to perform a comprehensive analysis on the metagenomic next-generation sequencing for the etiological diagnosis of septic patients, and further to establish optimal read ...values for detecting common pathogens.
In this single-center retrospective study, septic patients who underwent pathogen detection by both microbial culture and metagenomic next-generation sequencing in the intensive care unit of the Second People's Hospital of Shenzhen from June 24, 2015, to October 20, 2019, were included.
A total of 193 patients with 305 detected specimens were included in the final analysis. The results of metagenomic next-generation sequencing showed significantly higher positive rates in samples from disparate loci, including blood, bronchoalveolar lavage fluid, and cerebrospinal fluid, as well as in the determination of various pathogens. The optimal diagnostic reads were 2893, 1825.5, and 892.5 for Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae, respectively.
The metagenomic next-generation sequencing is capable of identifying multiple pathogens in specimens from septic patients, and shows significantly higher positive rates than culture-based diagnostics. The optimal diagnostic reads for frequently detected microbes might be useful for the clinical application of metagenomic next-generation sequencing in terms of timely and accurately determining etiological pathogens for suspected and confirmed cases of sepsis due to well-performed data interpretation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Redox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not ...included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.
Plain Language Summary
Coastal wetlands can store carbon rapidly but are difficult to represent in current models for accurate accounting of how much carbon can be trapped. This difficulty is due to the complex interactions between tides, chemical reactions, and water salinity, which strongly affect the decay of organic matter and the production of greenhouse gases. We enhanced an existing model by linking it to a powerful chemical reaction simulator such that organic matter decomposition was tightly connected to chemical reactions involving key components such as sulfur, iron, oxygen, and methane. We used this model to compute the effect of salinity on organic matter decomposition and greenhouse gas production in saline and freshwater wetlands. The model predicted much lower methane emissions from saltwater‐affected wetlands, which compared well to field measurements from coastal wetland sites in Massachusetts, USA. This model improves the accounting of carbon in wetland ecosystems and opens a broad range of possibilities for representing complex chemistry in land models.
Key Points
Coastal wetlands store large amounts of carbon and are sensitive to chemical interactions driven by salinity and tidal fluctuations
We coupled a land surface model to a reactive transport model to simulate biogeochemical cycling in saline and fresh tidal wetlands
Sulfate availability in saline wetlands lowered simulated methane emissions, which compared well with site measurements
As interfaces connecting terrestrial and ocean ecosystems, coastal wetlands develop temporally and spatially complex redox conditions, which drive uncertainties in greenhouse gas emission as well as ...the total carbon budget of the coastal ecosystem. To evaluate the role of complex redox reactions in methane emission from coastal wetlands, a coupled reactive‐transport model was configured to represent subsurface biogeochemical cycles of carbon, nitrogen, and sulfur, along with production and transport of multiple gas species through diffusion and ebullition. This model study was conducted at multiple sites along a salinity gradient in the Barataria Basin at the Mississippi River Deltaic Plain. Over a freshwater to saline gradient, simulated total flux of methane was primarily controlled by its subsurface production and consumption, which were determined by redox reactions directly (e.g., methanogenesis, methanotrophy) and indirectly (e.g., competition with sulfate reduction) under aerobic and/or anaerobic conditions. At fine spatiotemporal scales, surface methane fluxes were also strongly dependent on transport processes, with episodic ebullitive fluxes leading to higher spatial and temporal variability compared to the gradient‐driven diffusion flux. Ebullitive methane fluxes were determined by methane fraction in total ebullitive gas and the frequency of ebullitive events, both of which varied with subsurface methane concentrations and other gas species. Although ebullition thresholds are constrained by local physical factors, this study indicates that redox interactions not only determine gas composition in ebullitive fluxes but can also regulate ebullition frequency through gas production.
Plain Language Summary
Coastal wetlands store a large amount of carbon from the atmosphere and oceans, which is usually referred as blue carbon. Buried carbon in coastal wetlands can be decomposed into methane (CH4) whose global warming potential over a century is about 30 times higher than carbon dioxide (CO2), and this decomposition process may increase under a warming climate. Surface methane fluxes are controlled by multiple factors that influence subsurface methane production, consumption, and transport from subsurface to surface. We built a model to simulate how important chemical reactions in the subsurface (e.g., nitrification, denitrification, sulfate reduction) influence the relationships between surface methane fluxes and abiotic drivers including temperature and water level in a river deltaic wetland system. Our model results show that methane fluxes decrease from freshwater marsh to salt marsh, which is due to a combination of lower methane production and higher methane consumption in the subsurface of wetlands that are more strongly influenced by saltwater. Rates of methane emission over short time scales depend on episodic gas escaping events, which are related to subsurface redox interactions and drive large variations in surface flux rates.
Key Points
Subsurface methane productivity declines with increasing salinity gradient
Ebullitive methane flux is episodic and highly variable in coastal wetland system
Subsurface redox interactions regulate the frequency and gas composition in ebullitive flux
Terrestrial Light Detection And Ranging (LiDAR), also referred to as terrestrial laser scanning (TLS), has gained increasing popularity in terms of providing highly detailed micro-topography with ...millimetric measurement precision and accuracy. However, accurately depicting terrain under dense vegetation remains a challenge due to the blocking of signal and the lack of nearby ground. Without dependence on historical data, this research proposes a novel and rapid solution to map densely vegetated coastal environments by integrating terrestrial LiDAR with GPS surveys. To verify and improve the application of terrestrial LiDAR in coastal dense-vegetation areas, we set up eleven scans of terrestrial LiDAR in October 2015 along a sand berm with vegetation planted in Plaquemines Parish of Louisiana. At the same time, 2634 GPS points were collected for the accuracy assessment of terrain mapping and terrain correction. Object-oriented classification was applied to classify the whole berm into tall vegetation, low vegetation and bare ground, with an overall accuracy of 92.7% and a kappa value of 0.89. Based on the classification results, terrain correction was conducted for the tall-vegetation and low-vegetation areas, respectively. An adaptive correction factor was applied to the tall-vegetation area, and the 95th percentile error was calculated as the correction factor from the surface model instead of the terrain model for the low-vegetation area. The terrain correction method successfully reduced the mean error from 0.407 m to −0.068 m (RMSE errors from 0.425 m to 0.146 m) in low vegetation and from 0.993 m to −0.098 m (RMSE from 1.070 m to 0.144 m) in tall vegetation.
Sandy sediments preserved as paleo-channel fill on the inner shelf, some of which are overlain by modern muds, have been mined for barrier island restoration along the northern Gulf of Mexico. These ...mined areas have been termed “mud-capped” dredge pits. The processes governing the morphological evolution of the pits are poorly constrained due to limited observational data. Physical oceanographic (e.g., currents and waves) and sedimentary data were collected at Sandy Point dredge pit offshore Plaquemines Parish, Louisiana in summer 2015. Currents outside the pit flowed southward and/or southeastward at speeds of 8–20 cm/s, while currents inside the pit had speeds less than 2 cm/s with no clear dominant direction. Wave heights detected inside the pit were less than 0.4 m. A high turbidity layer with suspended sediment concentration around 4 g/L was observed above the pit floor, and its thickness was ~0.5 m. With observational data as input, three 2–D numerical models were employed to predict pit morphological responses, including pit infilling, margin erosion and slope change. The model results suggest that resuspension events were rare on the seafloor adjacent to the pit under summer fair weather conditions. Modeled pit margin erosion was very limited. With little resuspension of seafloor sediment locally, weak margin erosion and stable pit walls, the dominant process governing pit evolution was infilling sourced by the deposition of suspended sediments from the Mississippi River plume.
Category-level 6D pose estimation, aiming to predict the location and orientation of unseen object instances, is fundamental to many scenarios such as robotic manipulation and augmented reality, yet ...still remains unsolved. Precisely recovering instance 3D model in the canonical space and accurately matching it with the observation is an essential point when estimating 6D pose for unseen objects. In this paper, we achieve accurate category-level 6D pose estimation via cascaded relation and recurrent reconstruction networks. Specifically, a novel cascaded relation network is dedicated for advanced representation learning to explore the complex and informative relations among instance RGB image, instance point cloud and category shape prior. Furthermore, we design a recurrent reconstruction network for iterative residual refinement to progressively improve the reconstruction and correspondence estimations from coarse to fine. Finally, the instance 6D pose is obtained leveraging the estimated dense correspondences between the instance point cloud and the reconstructed 3D model in the canonical space. We have conducted extensive experiments on two well-acknowledged benchmarks of category-level 6D pose estimation, with significant performance improvement over existing approaches. On the representatively strict evaluation metrics of 3D 75 and 5°2cm, our method exceeds the latest state-of-the-art SPD 1 by 4.9% and 17.7% on the CAMERA25 dataset, and by 2.7% and 8.5% on the REAL275 dataset. Codes are avaliable at https://wangjiaze.cn/projects/6DPoseEstimation.html.
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