Net community production (NCP) is defined as the difference between gross primary production (GPP) and total community respiration (R). NCP indicates the balance between the production and consumption of community organic carbon, therefore making it a key parameter for evaluating the efficiency of carbon sequestration using the biological pump (BP). It is difficult to quantify NCP directly via satellite, because there are complex processes in community production and respiration. We reviewed previous research on satellite-based NCP and classified the methods into two primary categories: empirical methods and semi-analytical methods. The former category was established based on numerical relationships between NCP and satellite-based proxies, while the latter was developed by utilizing mechanistic analysis to establish quantitative expressions linking NCP to such proxies. Although satellite-based calculations of NCP have been attempted, they still suffer from significant uncertainties. Future research should focus on the precise calculation of satellite-based NCP by investigating the underlying processes and mechanisms that regulate NCP, developing regional models, and increasing the resolution of satellite sensors, as well as applying satellite lidar and coordinated multi-sensor observation technology.

In order to study the dissolution-diffusion process and mechanism of CO2 in multi-component crude oil, a model of multi-component crude oil system with octane as the main component and 16 other alkanes as a compound was constructed by using molecular dynamics simulation method. We estimated the CO2 density distribution in crude oil model and the shift in crude oil model volume change. We then investigated the microscopic influence mechanism of CO2 dissolution-diffusion on the volume expansion of crude oil by simulating the action of CO2 dissolution-diffusion in the multi-component crude oil model. Based on the variation law of mean square displacement between crude oil molecules, the dissolution and diffusion coefficients of CO2 were predicted, and the influence of CO2 dissolution-diffusion on crude oil mobility was analyzed. It is found that temperature intensifies the molecular thermal motion and increases the voids between alkane molecules, which promotes the dissolution of CO2 and encourages CO2 molecules to transmit, making the crude oil expand and viscosity decrease, and improving the flow ability of crude oil; with the enhancement of given pressure, the potential energy difference between the inside and outside of the crude oil model becomes larger, and the voids between alkane molecules become larger, which is favorable to the dissolution of CO2. Nevertheless, the action of CO2 molecules’ diffusing in the crude oil sample is significantly limited or even tends to zero, besides, the mobility of crude oil is affected due to the advance of external pressure. The mechanism of CO2 dissolution and diffusion in multi-component crude oil is revealed at the microscopic level, and provides theoretical guidance for the development of CO2 flooding.

The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

IntroductionAs an ephemeral and oligotrophic environment, the phyllosphere harbors many highly diverse microorganisms. Importantly, it is known that their colonization of plant leaf surfaces is considerably influenced by a few abiotic factors related to climatic conditions. Yet how the dynamics of phyllosphere bacterial community assembly are shaped by detailed climatological elements, such as various bioclimatic variables, remains poorly understood.MethodsUsing high-throughput 16S rRNA gene amplicon sequencing technology, we analyzed the bacterial communities inhabiting the leaf surfaces of an oilseed tree, yellowhorn (Xanthoceras sorbifolium), grown at four sites (Yinchuan, Otogqianqi, Tongliao, and Zhangwu) whose climatic status differs in northern China.Results and DiscussionWe found that the yellowhorn phyllosphere’s bacterial community was generally dominated by four phyla: Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Nevertheless, bacterial community composition differed significantly among the four sampled site regions, indicating the possible impact of climatological factors upon the phyllosphere microbiome. Interestingly, we also noted that the α-diversities of phyllosphere microbiota showed strong positive or negative correlation with 13 bioclimatic factors (including 7 precipitation factors and 6 temperature factors). Furthermore, the relative abundances of 55 amplicon sequence variants (ASVs), including three ASVs representing two keystone taxa (the genera Curtobacterium and Streptomyces), exhibited significant yet contrary responses to the precipitation and temperature climatic variables. That pattern was consistent with all ASVs’ trends of possessing opposite correlations to those two parameter classes. In addition, the total number of links and nodes, which conveys community network complexity, increased with rising values of most temperature variables. Besides that, remarkably positive relevance was found between average clustering coefficient and most precipitation variables. Altogether, these results suggest the yellowhorn phyllosphere bacterial community is capable of responding to variation in rainfall and temperature regimes in distinctive ways.