Evaluation of Parameters Importance in Cloud Service Selection Using Rough Sets

With the rapid development of the cloud computing technology, it has matured enough for a lot of individuals and organizations to move their work into the cloud. Correspondingly, a variety of cloud services are emerging. It is a key issue to assess the cloud services in order to help the cloud users select the most suitable cloud service and the cloud providers offer this service with the highest quality. The criteria parameters defining the cloud services are complex which lead to cloud service deviation. In this paper, we propose an assessment method of parameters importance in cloud services using rough set theory. The method can effectively compute the importance of cloud services parameters and sort them. On the one hand, the calculation can be used as the credible reference when users choose their appropriate cloud services. On the other hand, it can help cloud service providers to meet user requirements and enhance the user experience. The simulation results show the effectiveness of the method and its relevance in the cloud context.

Linking leaf elemental traits to biomass across forest biomes in the Himalayas

Plants require a number of essential elements in different proportions for ensuring their growth and development.The elemental concentrations in leaves reflect the functions and adaptations of plants under specific environmental conditions.However,less is known about how the spectrum of leaf elements associated with resource acquisition,photosynthesis and growth regulates forest biomass along broad elevational gradients.We examined the influence of leaf element distribution and diversity on forest biomass by analyzing ten elements(C,N,P,K,Ca,Mg,Zn,Fe,Cu,and Mn)in tree communities situated every 100 meters along an extensive elevation gradient,ranging from the tropical forest(80 meters above sea level)to the alpine treeline(4200 meters above sea level)in the Kangchenjunga Landscape in eastern Nepal Himalayas.We calculated communityweighted averages(reflecting dominant traits governing biomass,i.e.,mass-ratio effect)and functional divergence(reflecting increased trait variety,i.e.,complementarity effect)for leaf elements in a total of 1,859 trees representing 116 species.An increasing mass-ratio effect and decreasing complementarity in leaf elements enhance forest biomass accumulation.A combination of elements together with elevation explains biomass(52.2%of the variance)better than individual elemental trait diversity(0.05%to 21%of the variance).Elevation modulates trait diversity among plant species in biomass accumulation.Complementarity promotes biomass at lower elevations,but reduces biomass at higher elevations,demonstrating an interaction between elevation and complementarity.The interaction between elevation and mass-ratio effect produces heterogeneous effects on biomass along the elevation gradient.Our research indicates that biomass accumulation can be disproportionately affected by elevation due to interactions among trait diversities across vegetation zones.While higher trait variation enhances the adaptation of species to environmental changes,it reduces biomass accumulation,especially at higher elevations.

Spatial variations and mechanisms for the stability of terrestrial carbon sink in China

Understanding the stability of terrestrial carbon sinks(S-TCS)contributes to more accurate prediction of the terrestrial carbon sink(TCS)in the context of future global change and helps inform climate change mitigation policies.Here,focusing on China,we analyzed the spatial distribution and driving mechanisms for the S-TCS,quantified by the interannual variability of the TCS,using three independent approaches(atmospheric inversions,ecosystem carbon cycle models,and machine learning models based on flux tower observations).We found that the interannual variability of the TCS in China is relatively small compared with the conterminous United States and geographic Europe,indicating a generally stable TCS in China.Spatially,the S-TCS is lower in the North China Plain,Northeast China Plain,and western Yunnan-Guizhou Plateau than in other regions,with varying underlying mechanisms.Large interannual variations in precipitation and high TCS sensitivities to precipitation fluctuations explain the low S-TCS in the North China Plain and Northeast China Plain,while high TCS sensitivities to temperature variations drive the low S-TCS in the western Yunnan-Guizhou Plateau.Our findings highlight the importance of considering local contexts for stabilizing and enhancing China’s TCS in a changing environment.

Deciphering impacts of climate extremes on Tibetan grasslands in the last fifteen years

Climate extremes have emerged as a crucial driver of changes in terrestrial ecosystems. The Tibetan Plateau, facing a rapid climate change, tends to favor climate extremes. But we lack a clear understanding of the impacts of such extremes on alpine grasslands. Here we show that extreme events(drought,extreme wet, extreme cold and extreme hot) occurred at a frequency of 0.67–4 months decade^(-1) during2001–2015, with extreme precipitation predominantly occurring in June-to-August and extreme temperatures in May. Drought and extreme wet cause opposite and asymmetric effects on grassland growth,with drought-induced reductions greater than increases due to extreme wet. Grassland responses to extreme temperatures, which predominantly occur in May, show a dipole-like spatial pattern, with extreme hot(cold) events enhanced(reduced) growth in the eastern plateau but slightly reduced(enhanced) growth in the western plateau. These opposite responses to extreme temperatures over the eastern plateau are explained by the possibility that the occurrence of extreme cold slows the preseason temperature accumulation, delaying the triggering of spring phenology, while extreme hot hastens the accumulation. In the western plateau, in contrast, positive responses to extreme cold are induced by accompanying high precipitation. Furthermore, high extremeness of climate events generally led to a much lower extremeness in growth response, implying that the Tibetan grasslands have a relatively high resistance to climate extremes. The ecosystem models tested could not accurately simulate grassland responses to drought and extreme temperatures, and require re-parameterization before trust can be placed in their output for this region.