Soil organic carbon (SOC) concentration is closely related to soil quality and climate change. The objectives of this study were to estimate the effects of contemporary land use on SOC concentrations at 0–20 cm depths, and to investigate the dynamics of SOC in paddy-field soil and dry-land soil after their conversion from natural wetlands (20 and 30 years ago). We investigated the dissolved organic carbon (DOC), light fraction organic carbon (LFOC), heavy fraction organic carbon (HFOC), and other soil properties (i.e., moisture content, bulk density, pH, clay, sand, silt, available phosphorous, light fraction nitrogen, and heavy fraction nitrogen) in natural wetlands, constructed wetlands, fishponds, paddy fields, and soybean fields. The results indicated that the content of DOC increased 17% in constructed wetland and decreased 39% in fishponds, and the content of HFOC in constructed wetland and fishponds increased 50% and 8%, respectively, compared with that in natural wetlands at 0–20 cm. After the conversion of a wetland, the content of HFOC increased 72% in the paddy fields and decreased 62% in the dry land, while the content of DOC and LFOC decreased in both types. In the paddy fields, LFOC and HFOC content in the topmost 0.2 m of the soil layer was significantly higher compared to the layer below (from 0.2 to 0.6 m), and there were no significant differences observed in the dry land. The findings suggest that the paddy fields can sequester organic carbon through the accumulation of HFOC. However, the HFOC content decreased 22% after 10 years of cultivation with the decrease of clay content, indicating that paddy fields need to favor clay accumulation for the purpose of enhancing carbon sequestration in the paddy fields.

Accurately assessing the carbon sink intensity of China’s ecosystem is crucial for achieving carbon neutrality. However, existing ecosystem process models have significant uncertainties in net ecosystem productivity (NEP) estimates due to the lack of or insufficient description of phenological regulation. Although plant developmental factors have been proven to significantly influence autumn phenology, they have not been systematically incorporated into autumn phenology models. In this study, we modified the autumn phenology model (cold-degree-day, CDD) by incorporating the growing-season gross primary productivity (GPP) and the start of growing season (SOS) and used it as a constraint to improve the CASA model for quantifying NEP across China from 2003 to 2021. Validation results showed that the CDD model incorporating developmental factors significantly improved the simulation accuracy at the end of the growing season (EOS). More importantly, compared with flux tower observations, the NEP derived from the improved CASA model based on the above phenology model showed a 15.34% reduction in root mean square error and a 74% increase in the coefficient of determination relative to the original model. During the study period, China’s multiyear average total NEP was 489.67 ± 38.27 Tg C/yr, with the highest found in evergreen broadleaf forests and the lowest detected in shrublands. Temporally, China’s NEP demonstrated an overall increasing trend with an average rate of 1.75 g C/m²/yr². However, the growth rate of NEP remained far below concurrent carbon emissions from fossil fuel combustion totally, especially for eastern China, while the northeastern regions performed relatively better. The improved regional carbon flux estimation framework proposed in this study will provide important support for developing future climate change mitigation strategies.

Microservice architectures backed by container technology have been widely used in many real-world cloud-native applications. The number of containers in cloud data centers continues to increase, expected to reach several million soon. At this scale, existing service and configuration management solutions have limited efficiency, scalability and robustness. To overcome these limitations, we propose Ripple, which uniquely combines several existing and some novel features including consistent hashing-based workload distribution, dynamic destination list-based and client-assisted message delivery, incremental update, and adaptive load balancing. The experiments show that our solution greatly outperforms its counterparts.

Microservice architectures backed by container technology have been widely used in many real-world cloud-native applications. By enabling customers to manage their services and configurations in the cloud in a centralized, externalized, and dynamic manner, efficient service and configuration management plays a fundamental role in building cloud-native service-centric applications. The number of containers in cloud data centers continues to increase. For example, in the Alibaba Cloud, the number of containers reached hundreds of thousands by 2023 and is expected to reach several million soon. At this scale, existing service and configuration management solutions have limited efficiency, scalability and robustness. Other related approaches, such as message bus systems and publish/subscribe (pub/sub for short) systems, also do not work well for large-scale service and configuration management in the cloud, as their designs are more general purpose directed. To overcome these limitations, we design a system, called Ripple, that uniquely combines several existing and some novel features such as consistent hashing-based workload distribution, dynamic destination list-based and client-assisted message delivery, incremental update, and adaptive load balancing. Approaches exhibiting these features have not been well investigated in the domain of service and configuration management. We compare our proposed solution with existing academic and industrial approaches. The experiments show that our solution greatly outperforms its counterparts. For example, for the same workload, when Ripple is used, the average message delivery latency and network bandwidth consumption can be reduced by up to 77% and 93%, respectively.