Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Geological reservoir and resource potential(10^(13)m^(3))of gas hydrates in the South China Sea

A detailed understanding of the distribution and potential of natural gas hydrate(NGHs)resources is crucial to fostering the industrialization of those resources in the South China Sea,where NGHs are abundant.In this study,this study analyzed the applicability of resource evaluation methods,including the volumetric,genesis,and analogy methods,and estimated NGHs resource potential in the South China Sea by using scientific resource evaluation methods based on the factors controlling the geological accumulation and the reservoir characteristics of NGHs.Furthermore,this study compared the evaluation results of NGHs resource evaluations in representative worldwise sea areas via rational analysis.The results of this study are as follows:(1)The gas hydrate accumulation in the South China Sea is characterized by multiple sources of gas supply,multi-channel migration,and extensive accumulation,which are significantly different from those of oil and gas and other unconventional resources.(2)The evaluation of gas hydrate resources in the South China Sea is a highly targeted,stratified,and multidisciplinary evaluation of geological resources under the framework of a multi-type gas hydrate resource evaluation system and focuses on the comprehensive utilization of multi-source heterogeneous data.(3)Global NGHs resources is n×10^(15)m^(3),while the NGHs resources in the South China Sea are estimated to be 10^(13)m^(3),which is comparable to the abundance of typical marine NGHs deposits in other parts of the world.In the South China Sea,the NGHs resources have a broad prospect and provide a substantial resource base for production tests and industrialization of NGHs.

Planning and construction of Xiong’an New Area(city of over 5 million people):Contributions of China’s geologists and urban geology

China established Xiong’an New Area in Hebei Province in 2017,which is planned to accommodate about 5 million people,aiming to relieve Beijing City of the functions non-essential to its role as China’s capital and to expedite the coordinated development of the Beijing-Tianjin-Hebei region.From 2017 to 2021,the China Geological Survey(CGS)took the lead in multi-factor urban geological surveys involving space,resources,environments,and disasters according to the general requirements of“global vision,international standards,distinctive Chinese features,and future-oriented goals”in Xiong’an New Area,identifying the engineering geologic conditions and geologic environmental challenges of this area.The achievements also include a 3D engineering geological structure model for the whole area,along with“one city proper and five clusters”,insights into the ecology and the background endowment of natural resources like land,geothermal resources,groundwater,and wetland of the area before engineering construction,a comprehensive monitoring network of resources and environments in the area,and the“Transparent Xiong’an”geological information platform that is open,shared,dynamically updated,and three-dimensionally visualized.China’s geologists and urban geology have played a significant role in the urban planning and construction of Xiong’an New Area,providing whole-process geological solutions for urban planning,construction,operation and management.The future urban construction of Xiong’an New Area will necessitate the theoretical and technical support of earth system science(ESS)from various aspects,and the purpose is to enhance the resilience of the new type of city and to provide support for the green,low-carbon,and sustainable development of this area.

深海环境海洋生态系统监测与修复新技术

The United Nations(UN)’s call for a decade of“ecosystem restoration”was prompted by the need to address the extensive impact of anthropogenic activities on natural ecosystems.Marine ecosystem restoration is increasingly necessary due to increasing habitat degredation in deep waters(>200 m depth).At these depths,which are far beyond those accessible by divers,only established and emerging robotic platforms such as remotely operated vehicles(ROVs),autonomous underwater vehicles(AUVs),landers,and crawlers can operate through manipulators and multiparametric sensor arrays(e.g.,optoacoustic imaging,omics,and environmental probes).The use of advanced technologies for deep-sea ecosystem restoration can provide:①high-resolution three-dimensional(3D)imaging and acoustic mapping of substrates and key taxa,②physical manipulation of substrates and key taxa,③real-time supervision of remote operations and long-term ecological monitoring,and④the potential to work autonomously.Here,we describe how robotic platforms with in situ manipulation capabilities and payloads of innovative sensors could autonomously conduct active restoration and monitoring across large spatial scales.We expect that these devices will be particularly useful in deep-sea habitats,such as①reef-building cold-water corals,②soft-bottom bamboo corals,and③soft-bottom fishery resources that have already been damaged by offshore industries(i.e.,fishing and oil/gas).

Candidate genes conferring ethylene-response in cultivated peanuts determined by BSA-seq and fine-mapping

Ethylene plays essential roles in plant growth,development and stress responses.The ethylene signaling pathway and molecular mechanism have been studied extensively in Arabidopsis and rice but limited in peanuts.Here,we established a sand-culture method to screen pingyangmycin mutagenized peanut lines based on their specific response to ethylene(“triple response”).An ethylene-insensitive mutant,inhibition of peanut hypocotyl elongation 1(iph1),was identified that showed reduced sensitivity to ethylene in both hypocotyl elongation and root growth.Through bulked segregant analysis sequencing,a major gene related to iph1,named AhIPH1,was preliminarily mapped at the chromosome Arahy.01,and further narrowed to a 450-kb genomic region through substitution mapping strategy.A total of 7014 genes were differentially expressed among the ACC treatment through RNA-seq analysis,of which only the Arahy.5BLU0Q gene in the candidate mapping interval was differentially expressed between WT and mutant iph1.Integrating sequence variations,functional annotation and transcriptome analysis revealed that a predicated gene,Arahy.5BLU0Q,encoding SNF1 protein kinase,may be the candidate gene for AhIPH1.This gene contained two single-nucleotide polymorphisms at promoter region and was more highly expressed in iph1 than WT.Our findings reveal a novel ethylene-responsive gene,which provides a theoretical foundation and new genetic resources for the mechanism of ethylene signaling in peanuts.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter(CSIT).A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CsI feedback stage.In fact,the downlink communication generally includes three stages,i.e.,pilot training,CsI feedback,and data transmission.These three stages are mutually related and jointly determine the overall system performance.Unfortunately,there exist few studies on the reduction of csIT acquisition overhead from the global point of view.In this paper,we integrate the Minimum Mean Square Error(MMSE)channel estimation,Random Vector Quantization(RVQ)based limited feedback and Maximal Ratio Combining(MRC)precoding into a unified framework for investigating the resource allocation problem.In particular,we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio(SNR)based on the deterministic equivalence theory.Then the three performance metrics(the spectral efficiency,energy efficiency,and total energy consumption)oriented problems are formulated analytically.With practical system requirements,these three metrics can be collaboratively optimized.Finally,we propose an optimization solver to derive the optimal partition of channel coherence time.Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

一种基于血液检测的诊断和预测HBV相关疾病的弹性方法

Chronic hepatitis B virus(HBV)infection,which threatens global public health,is a major contributor to liver-related morbidity and mortality.Examinations for liver diseases related to chronic HBV infection-including laboratory tests,ultrasounds,computed tomography(CT),and liver biopsies-may take up medical resources,particularly since they overlap in most instances.Thus,there is an urgent need to establish an economical and effective diagnosis method in order to streamline the medical process for HBV-related disea ses.Using complex network models constructed based on clinical blood tests,we provide such a method by defining the novel measure of functional resilience to assess patients’liver conditions.By combining network models and dynamics,we discovered the pivotal items and their corresponding thresholds,which can guide further research on preventing disease deterioration in critical states of these diseases.The macro-averaged precision of our method,functional resilience,is84.74%,whereas the macro-averaged precision of physicians’experience without assistance from imaging or biopsy is 55.63%.From an economic perspective,our approach could save the equivalent of at least30 USD per visit for most Chinese patients and at least 400 USD per visit for most US patients,compared with general diagnostic methods.Globally,this will add to savings of at least 10.5 billion USD annually.Our method can comprehensively evaluate the condition of patients’livers and help avert the waste of medical resources during the diagnosis of liver disease by reducing excessive imaging exams.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Mega-Constellations Based TT&C Resource Sharing: Keep Reliable Aeronautical Communication in an Emergency

With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control Center(OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking,and Command(TT&C) architecture named Collaborative TT&C(CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium(NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.

An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.

Preparation and performance evaluation of the slickwater using novel polymeric drag reducing agent with high temperature and shear resistance ability

Slickwater fracturing fluids are widely used in the development of unconventional oil and gas resources due to the advantages of low cost,low formation damage and high drag reduction performance.However,their performance is severely affected at high temperatures.Drag reducing agent is the key to determine the drag reducing performance of slickwater.In this work,in order to further improve the temperature resistance of slickwater,a temperature-resistant polymeric drag reducing agent(PDRA)was synthesized and used as the basis for preparing the temperature-resistant slickwater.The slickwater system was prepared with the compositions of 0.2 wt%PDRA,0.05 wt%drainage aid nonylphenol polyoxyethylene ether phosphate(NPEP)and 0.5 wt%anti-expansion agent polyepichlorohydrindimethylamine(PDM).The drag reduction ability,rheology properties,temperature and shear resistance ability,and core damage property of slickwater were systematically studied and evaluated.In contrast to on-site drag reducing agent(DRA)and HPAM,the temperature-resistant slickwater demonstrates enhanced drag reduction efficacy at 90℃,exhibiting superior temperature and shear resistance ability.Notably,the drag reduction retention rate for the slickwater achieved an impressive 90.52%after a 30-min shearing period.Additionally,the core damage is only 5.53%.We expect that this study can broaden the application of slickwater in high-temperature reservoirs and provide a theoretical basis for field applications.

Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency.To tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell.To fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent cells.To tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV position.Then,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation schemes.We further propose an iterative power allocation algorithm based on the sequential convex approximation theory.The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Robot-Oriented 6G Satellite-UAV Networks: Requirements, Paradigm Shifts, and Case Studies

Networked robots can perceive their surroundings, interact with each other or humans,and make decisions to accomplish specified tasks in remote/hazardous/complex environments. Satelliteunmanned aerial vehicle(UAV) networks can support such robots by providing on-demand communication services. However, under traditional open-loop communication paradigm, the network resources are usually divided into user-wise mostly-independent links,via ignoring the task-level dependency of robot collaboration. Thus, it is imperative to develop a new communication paradigm, taking into account the highlevel content and values behind, to facilitate multirobot operation. Inspired by Wiener’s Cybernetics theory, this article explores a closed-loop communication paradigm for the robot-oriented satellite-UAV network. This paradigm turns to handle group-wise structured links, so as to allocate resources in a taskoriented manner. It could also exploit the mobility of robots to liberate the network from full coverage,enabling new orchestration between network serving and positive mobility control of robots. Moreover,the integration of sensing, communications, computing and control would enlarge the benefit of this new paradigm. We present a case study for joint mobile edge computing(MEC) offloading and mobility control of robots, and finally outline potential challenges and open issues.

Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

Parent-offspring relationship recognition based on SSR and mtDNA confirmed resource supplement effect of Fenneropenaeus chinensis release

The resource of Fenneropenaeus chinensis has declined sharply due to excessive fishing intensity,ecological changes and diseases.In order to supplement the fishing yield and restore resources of F.chinensis,the relevant authorities have carried out the activities of stock enhancement and releasing.It can increase biomass and recover resources.However,compared with increasing biomass,there were still few reports on its effect on the recovery of resources.Resource recovery is a process related to whether the released individuals can form a reproductive population.Up to now,there has been a lack of evidence whether the released F.chinensis can complete the entire life history,and form reproduction population.In this study,gravid female shrimp after spawning migration were captured from coastal waters of Haiyang,Qingdao,and Yellow Sea.After identifying parentage relationships using simple sequence repeat(SSR)and mtDNA haplotype,it was finally confirmed that there were eight released individuals in the recapture samples.It was confirmed for the first time that at least part of the released F.chinensis can complete overwintering and reproductive migration,and maintain the migration habits as their wild counterparts.Therefore,we infered that the released shrimp can reproduce under natural conditions,these F.chinensis can form reproductive populations theoretically if without human intervention.These results indicated that enhancenment and release activities have a positive effect on resource recovery.

A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

In the context of enterprise systems,intrusion detection(ID)emerges as a critical element driving the digital transformation of enterprises.With systems spanning various sectors of enterprises geographically dispersed,the necessity for seamless information exchange has surged significantly.The existing cross-domain solutions are challenged by such issues as insufficient security,high communication overhead,and a lack of effective update mechanisms,rendering them less feasible for prolonged application on resource-limited devices.This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload.Within this framework,individual nodes solely engage in training local data and subsequently amalgamate the final model employing a federated learning algorithm to uphold enterprise systems with efficiency and security.To curtail the resource utilization of blockchains and deter malicious nodes,a node administration module predicated on the workload paradigm is introduced,enabling the release of surplus resources in response to variations in a node’s contribution metric.Upon encountering an intrusion,the system triggers an alert and logs the characteristics of the breach,facilitating a comprehensive global update across all nodes for collective defense.Experimental results across multiple scenarios have verified the security and effectiveness of the proposed solution,with no loss of its recognition accuracy.

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.