Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
Based on the data of regional geology,seismic,drilling,logging and production performance obtained from 94 major petroliferous basins worldwide,the global coal resources were screened and statistically analyzed.Then,u...Based on the data of regional geology,seismic,drilling,logging and production performance obtained from 94 major petroliferous basins worldwide,the global coal resources were screened and statistically analyzed.Then,using established definition methods and evaluation criteria for coal-rock gas in China,and by analogy with the tectono-sedimentary and burial-thermal evolution conditions of coal rocks in sedimentary basins within China,the geological resource potential of global coal-rock gas was estimated mainly by the volume method,partly by the volumetric method in selected regions.According to the evaluation indicator system comprising 14 parameters under 5 categories and the associated scoring criteria,the target basins were ranked,and the future research targets for these basins were proposed.The results reveal that,globally,coal rocks are primarily formed in four types of swamp environments within four categories of prototype basins,and distributed across five major coal-forming periods and eight coal-accumulation belts.The total geological coal resources are estimated at approximately 42×10^(12)t,including 22×10^(12)t in the strata deeper than 1500 m.The global geological coal-rock gas resources in deep strata are roughly 232×10^(12)m^(3),of which over 90%are endowed in Russia,Canada,the United States,China and Australia,with China contributing 24%.The top 10 basins by coal-rock gas resource endowment,i.e.Alberta,Kuznetsk,Ordos,East Siberian,Bowen,West Siberian,Sichuan,South Turgay,Lena-Vilyuy and Tarim,collectively hold 75%of the global total.The Permian,Cretaceous,Carboniferous,Jurassic,and Paleogene-Neogene account for 32%,30%,18%,10%,and 7%of total coal-rock gas resources,respectively.The 10 most practical basins for future coal-rock gas exploration and development are identified as Alberta,Ordos,Kuznetsk,San Juan,Sichuan,East Siberian,Rocky Mountain,Bowen,Junggar and Qinshui.Propelled by successful development practices in China,coal-rock gas is now entering a phase of theoretical breakthrough,technological innovation,and rapid production growth,positioning it to spearhead the next wave of the global unconventional oil and gas revolution.展开更多
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ...Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.展开更多
The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource a...The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource allocation model to determine the type,number and location of flexible resources to increase the values of resilience,carbon reduction and renewable energy consumption.To evaluate the values of resilience,a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators,energy storage systems(ESSs)and renewable energy generators in building restoration paths.The collaborative power-carbon-tradable green certificate(TGC)market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption.Finally,the model is formulated as a mixed-integer linear programming(MILP)with a nonconvex feasible domain,and the normalized normal constraint(NNC)method is applied to obtain approximate Pareto frontiers for decision makers.Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction.展开更多
With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service...With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.展开更多
Water resources health diagnosis is increasingly recognized as a critical tool.It should not only guide the preservation of water volume,quality,and ecological integrity but also assess the viability of carbon-neutral...Water resources health diagnosis is increasingly recognized as a critical tool.It should not only guide the preservation of water volume,quality,and ecological integrity but also assess the viability of carbon-neutral development pathways.But current research tends to consider water health assessment and carbon-neutrality planning as separate endeavors,which results in imprecise boundaries,disjointed metrics,and a weak linkage between diagnostic outcomes and testable management actions.This review brings together indicator systems,diagnostic systems,and environmental management systems architectures that make it possible to have integrated water-carbon governance.We define fundamental concepts and delimiting decisions followed by the examination of indicator designs across the hydrological regime,water quality,ecological integrity,service performance,resilience,and carbon-related measures,including intensity of energy/emissions,emissions caused by the watershed process of wastewater treatment,as well as the potential sink of the watershed.We compare diagnostic methods,such as composite indices and multi-criteria decision analysis,data-driven early-warning models,process-based and integrated simulations,as well as uncertainty-aware robustness models.Here,based on this synthesis,we suggest an environmental management systems(EMS)-based pathway,which connects the setting of the baseline,the diagnosis,the design of the intervention portfolio,and the measurement-reporting-verification into the closed-loop adaptive cycle.Digital enablement,comprising Internet of Things(IoT)monitoring,remote sensing,data fusion,optimization,and digital twins,is considered a viable way of scaling implementation,subject to interoperability,validation,and model governance.Among the major gaps,there are causal attribution to outcomes,cross-scale coupling of facility emissions and basin health,propagation of uncertainty in a coupled model,and credible Measurement,Reporting,and Verification(MRV)of non-CO_(2) gases and nature-based removals.The review gives a roadmap to normalize core metrics and fast-deployable systems to protect the health of the water resources and give verifiable progress towards carbon neutrality.展开更多
Coal serves not only as a crucial energy resource but also as a significant reservoir of critical metal elements,including Lithium(Li),Gallium(Ga),Germanium(Ge),and rare earth elements(REE).This paper provides a syste...Coal serves not only as a crucial energy resource but also as a significant reservoir of critical metal elements,including Lithium(Li),Gallium(Ga),Germanium(Ge),and rare earth elements(REE).This paper provides a systematic review of the enrichment characteristics,occurrence modes,and comprehensive utilization potential of these critical metals in coal.Globally,the distribution of these metal resources exhibits significant regional heterogeneity.While the concentration in most coals falls below industrial cut-off grades,anomalous enrichment in specific coal basins results in Li,Ga,Ge,and REE concentrations far exceeding global averages,highlighting their considerable potential as unconventional metal deposits.The occurrence modes of these metals are diverse:Li is primarily hosted in mineral phases;Ga exists in inorganic,organic,and complex forms;Ge shows a strong association with organic matter;and REE are mainly present in adsorbed/isomorphic forms within clay minerals,while also displaying organic affinity.Direct extraction of metals from raw coal is often cost-prohibitive;effective recovery is therefore more feasible when integrated with coal processing.Metals are further enriched in solid wastes such as coal gangue,fly ash,and bottom ash,from which recovery is more economically and technically viable.Current comprehensive utilization primarily employs integrated mineral processing-hydrometallurgy approaches.Future research should focus on elucidating the precise occurrence forms of metals in coal and solid wastes,optimizing pre-treatment methods,and selecting effective activators and leachants.Advancing the synergistic extraction and green recovery of multiple associated resources from coal and its by-products is essential for achieving high-value,comprehensive utilization of coal-based resources.展开更多
Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the re...Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.展开更多
This article examines the intelligent forest sentry system in the Mount Tai Scenic Area, analyzing its applications, accomplishments, and significance in forest fire prevention. It highlights the system's critical...This article examines the intelligent forest sentry system in the Mount Tai Scenic Area, analyzing its applications, accomplishments, and significance in forest fire prevention. It highlights the system's critical role in enhancing comprehensive forest fire prevention and control capabilities, accelerating emergency response times, and safeguarding the heritage resources of Mount Tai. Furthermore, the system is evolving into a "Smart Forest and Grassland Resources Supervision Platform" that integrates forest fire early warning, ecological environment monitoring, and pest and disease management to promote the sustainable development of forest resources.展开更多
The concepts of the circular economy(CE)are actively popularized as ways of minimizing waste products and the need to rely on virgin resources.Nevertheless,their sustainability is doubtful at a general level where eco...The concepts of the circular economy(CE)are actively popularized as ways of minimizing waste products and the need to rely on virgin resources.Nevertheless,their sustainability is doubtful at a general level where ecosystem functioning and ecosystem services(ES)are not given explicit attention.This review will combine both conceptual and empirical evidence of the connection between CE interventions and ES outcomes to enable more sustainable management of resources.We describe the effects of the CE strategies on the key environmental pressure pathways,altering ecosystem conditions,and impacting the delivery of regulating,provisioning,and cultural ecosystem services using a pressure condition-service framework.Analysis reveals that demand-side reduction and product life-extension strategies tend to offer more consistent ecosystem service co-benefits than recycling and recovery strategies because they do not involve production,and will cause less disturbance to the upstream environment.Contrastingly,recycling and recovery sustainability performance is highly dependent on the sources of energy,intensity of processing,and the safety of materials.Bio-based circularity has the potential to increase soil functionality and nutrient cycling,and mass application will result in trade-offs in terms of land competition and nutrient leakage.The sectoral analysis identifies the unique opportunities and threats in the agri-food systems,the built environment,plastics and textiles,electronics and critical minerals,and water and wastewater systems in terms of the burden displacement,local environmental pressures,and equity concerns.Harmonized reporting,coupled with supply-chain and spatial ecological assessment,threshold-conscious strategies that promote safe and regenerative circular systems should be put into the line of future research.展开更多
The water resources surveying and hydrological is required to understand and manage the impacts of climate change on the water systems.This review discusses the ways in which such surveys can be used in improving clim...The water resources surveying and hydrological is required to understand and manage the impacts of climate change on the water systems.This review discusses the ways in which such surveys can be used in improving climate resilience,procedures,practices,and opportunities.The innovations of the traditional ground-based surveys into the modern hydrological survey are the current technologies,remote sensing,Geographic Information Systems(GIS),and real-time sensor networks,which allow scanning the water resources in an extensive,accurate,and timely way.These high-level methods would help manage water systems with substantial data in the prediction of floods,droughts and other water hazards caused by climate change.In addition,a hydrological survey plays a very crucial role during the climate adaptation and mitigation process since it illuminates the sustainable level of water use and the sustainability of the ecosystem.Despite the tremendous development in the use of survey techniques,there remain problems of data gaps,the high cost of using the technique,and data integration enhancement.The future of hydrological surveying is to take advantage of the emerging technologies,encourage more stakeholder cooperation,and sustainable practices to enhance access to and use of data.This review determines the significance of hydrological surveying in the construction of climate resilience and presents the contribution of how future improvements in technology and cooperation can empower the management of water resources in the climate change environment.展开更多
As the types of traffic requests increase,the elastic optical network(EON)is considered as a promising architecture to carry multiple types of traffic requests simultaneously,including immediate reservation(IR)and adv...As the types of traffic requests increase,the elastic optical network(EON)is considered as a promising architecture to carry multiple types of traffic requests simultaneously,including immediate reservation(IR)and advance reservation(AR).Various resource allocation schemes for IR/AR requests have been designed in EON to reduce bandwidth blocking probability(BBP).However,these schemes do not consider different transmission requirements of IR requests and cannot maintain a low BBP for high-priority requests.In this paper,multi-priority is considered in the hybrid IR/AR request scenario.We modify the asynchronous advantage actor critic(A3C)model and propose an A3C-assisted priority resource allocation(APRA)algorithm.The APRA integrates priority and transmission quality of IR requests to design the A3C reward function,then dynamically allocates dedicated resources for different IR requests according to the time-varying requirements.By maximizing the reward,the transmission quality of IR requests can be matched with the priority,and lower BBP for high-priority IR requests can be ensured.Simulation results show that the APRA reduces the BBP of high-priority IR requests from 0.0341 to0.0138,and the overall network operation gain is improved by 883 compared to the scheme without considering the priority.展开更多
To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication(JRC)systems under dynamic environments,an intelligent optimization framewor...To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication(JRC)systems under dynamic environments,an intelligent optimization framework integrating Deep Reinforcement Learning(DRL)and Graph Neural Network(GNN)is proposed.This framework models resource allocation as a Partially Observable Markov Game(POMG),designs a weighted reward function to balance radar and communication efficiencies,adopts the Multi-Agent Proximal Policy Optimization(MAPPO)framework,and integrates Graph Convolutional Networks(GCN)and Graph Sample and Aggregate(Graph-SAGE)to optimize information interaction.Simulations show that,compared with traditional methods and pure DRL methods,the proposed framework achieves improvements in performance metrics such as communication success rate,Average Age of Information(AoI),and policy convergence speed,effectively enabling resource management in complex environments.Moreover,the proposed GNN-DRL-based intelligent optimization framework obtains significantly better performance for resource management in multi-agent JRC systems than traditional methods and pure DRL methods.展开更多
To deal with a polluted by-product of coal production,central China’s Shanxi Province has explored a governance path that addresses both the symptoms and root causes.
As a major source of freshwater in Central Asia,Tajikistan is endowed with abundant glaciers and water resources.However,the country faces multiple challenges,including accelerated glacier retreat,complex inter-govern...As a major source of freshwater in Central Asia,Tajikistan is endowed with abundant glaciers and water resources.However,the country faces multiple challenges,including accelerated glacier retreat,complex inter-government water resource management,and inefficient water use.Existing research has predominantly focused on individual hydrological processes,such as glacier retreat,snow cover change,or transboundary water issues,but it has yet to fully capture the overall complexity of water system.Tajikistan’s water system functions as an integrated whole from mountain runoff to downstream supply,but a comprehensive study of its water resource has yet to be conducted.To address this research gap,this study systematically examined the status,challenges,and sustainable management strategies of Tajikistan’s water resources based on a literature review,remote sensing data analysis,and case studies.Despite Tajikistan’s relative abundance of water resources,global warming is accelerating glacier melting and altering the hydrological cycles,which have resulted in unstable runoff patterns and heightened risks of extreme events.In Tajikistan,outdated infrastructure and poor management are primary causes of low water-use efficiency in the agricultural sector,which accounts for 85.00%of the total water withdrawals.At the governance level,Tajikistan faces challenges in balancing the water-energy-food nexus and transboundary water resource issues.To address these issues,this study proposes core paths for Tajikistan to achieve sustainable water resource management,such as accelerating technological innovation,promoting water-saving agricultural technologies,improving water resource utilization efficiency,and establishing a community participation-based comprehensive management framework.Additionally,strengthening cross-border cooperation and improving real-time monitoring systems have been identified as critical steps to advance sustainable water resource utilization and evidence-based decision-making in Tajikistan and across Central Asia.展开更多
Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing riboso...Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing ribosome binding.Escherichia coli(E.coli)counters this bottleneck by inducing the cold-shock protein A(CspA),an RNA chaperone that remodels inhibitory structures.However,synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism,creating a fundamental resource-allocation trade-off.In this work,we develop a dynamical model capturing the interplay between metabolic precursors,ribosomes,and CspA,and use it to examine how growth and allocation patterns shift with temperature.Steady-state analysis shows that each temperature produces a distinct,locally stable equilibrium,illustrating how cold environments reshape cellular priorities.We then formulate growth maximization as an optimal control problem,solved using Pontryagin’s Maximum Principle,to identify allocation strategies that balance translation maintenance and biomass production.The resulting optimal strategies exhibit bang-bang and singular structures,highlighting periods of extreme and intermediate allocation that reflect how bacteria might dynamically prioritize competing cellular functions.These control patterns converge to their corresponding steady state allocations and provide quantitative insight into optimal resource management under cold stress.These results provide a quantitative optimal-control framework linking RNA-level cold-shock adaptation to proteome allocation and growth,yielding testable predictions for how bacteria balance translational maintenance and biomass production at suboptimal temperatures.展开更多
Expanding economic potential and protecting the environment,while facing mounting climate and biodiversity stress,is becoming the challenge of environmental resource management.This review explores developments and on...Expanding economic potential and protecting the environment,while facing mounting climate and biodiversity stress,is becoming the challenge of environmental resource management.This review explores developments and ongoing obstacles in six areas of resource of the Sustainable Development Goal(SDG)that include water;land,soils,and food systems;forests and terrestrial carbon biodiversity governance;oceans,coasts,and fisheries;biodiversity connectivity;and extractives and energy-transition supply chains.Most of the interventions continue to ignore ecological thresholds,accruing effects,and cross-system feedbacks,whereas monitoring systems focus on measures of activity rather than confirmed results.Conversely,sustained improvement is most frequently associated with integrated governance,which incorporates open measurement,implementation,rights-based participation,and fair distribution of benefits.Local conservation is often overwhelmed by market forces of demand and structural forces,including subsidies,supply chains,and investment in infrastructure,which fail to stop leakage or war,unless accountability mechanisms are in place.Climate change also aggravates set baselines and puts forward the importance of adaptive regulation,spatial planning,and diversified portfolios,which combine engineered reliability with ecosystem resilience.The review brings out current SDG priorities,which include outcome-based indicators,causal evaluation,governance structures that enhance legitimacy,and transition planning that harmonizes the mineral sourcing,renewable deployment,biodiversity,and water limits.Combined,these observations indicate that striking the balance between ecology and economy is possible when ecological boundaries are under consideration as binding constraints and equity is perceived as a source of sustainability.展开更多
Hyperspectral remote sensing has emerged as a transformative technology for sustainable natural resource management by providing unprecedented insight into the biochemical,biophysical,and compositional properties of E...Hyperspectral remote sensing has emerged as a transformative technology for sustainable natural resource management by providing unprecedented insight into the biochemical,biophysical,and compositional properties of Earth’s surface.The high spectral resolution of hyperspectral sensors allows a very specific discrimination of materials,monitoring of environmental stress at a very early stage,and provides quantitative retrieval of ecological and geochemical parameters in a wide range of landscapes.The booming technology in sensor design,machine learning,spectral unmixing,and multi-sensor data fusion has further improved the analysis potential and application of imaging spectroscopy to a large extent.This paper involves a discussion of the oversight of such technological advances and the manner in which they are utilized in the principal fields that include forestry,agriculture,water,mineral exploration,and coastal ecosystems.Case studies allow us to identify the potential practical consequences of both spaceborne and unmanned aerial vehicles(UAV)-based hyperspectral systems and AI-based workflows that can be used to aid in more efficient and accurate environmental review.Even though the issues associated with data volume,atmospheric impacts,lack of uniformity in the calibration process,and socioeconomic limits continue to exist,the new technology in sensor miniaturization,cloud computing,and artificial intelligence indicates a fast-changing environment.All these developments make hyperspectral remote sensing a key instrument in solving global sustainability problems and evidence-based management of natural resources in an evolving world.展开更多
Integrated sensing and communication(ISAC) systems can enhance security and reliability in full-duplex( FD) networks.This paper studies the resource allocation problem in FD networks based on ISAC.We formulate an opti...Integrated sensing and communication(ISAC) systems can enhance security and reliability in full-duplex( FD) networks.This paper studies the resource allocation problem in FD networks based on ISAC.We formulate an optimization problem that jointly considers beamforming,radar waveform,and reflection coefficient of a hybrid reconfigurable intelligent surface,aiming to maximize the beampattern gain of the concealed target while maintaining the quality of service of communication users.A two-stage algorithm is designed to solve this problem: the first stage optimizes the radar waveform and beamforming,and the second focuses on the reflection coefficient design.We acquire the feasible solution using semidefinite programming relaxation.The optimality of the feasible solution for radar waveform and beamforming subproblem is ensured through Cauchy-Schwartz inequality.For the reflection coefficient design,an approximate optimal solution is acquired through successive convex approximation.Numerical results demonstrate that the proposed algorithm can achieve a superior performance trade-off between communication and radar sensing compared to the benchmarks.展开更多
The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This rev...The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.展开更多
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金Supported by the China National Science and Technology Major Project on New-Type Oil and Gas Exploration and Development(2025ZD1404200,2025ZD1400800)PetroChina Science and Technology Project(2023ZZ07)。
文摘Based on the data of regional geology,seismic,drilling,logging and production performance obtained from 94 major petroliferous basins worldwide,the global coal resources were screened and statistically analyzed.Then,using established definition methods and evaluation criteria for coal-rock gas in China,and by analogy with the tectono-sedimentary and burial-thermal evolution conditions of coal rocks in sedimentary basins within China,the geological resource potential of global coal-rock gas was estimated mainly by the volume method,partly by the volumetric method in selected regions.According to the evaluation indicator system comprising 14 parameters under 5 categories and the associated scoring criteria,the target basins were ranked,and the future research targets for these basins were proposed.The results reveal that,globally,coal rocks are primarily formed in four types of swamp environments within four categories of prototype basins,and distributed across five major coal-forming periods and eight coal-accumulation belts.The total geological coal resources are estimated at approximately 42×10^(12)t,including 22×10^(12)t in the strata deeper than 1500 m.The global geological coal-rock gas resources in deep strata are roughly 232×10^(12)m^(3),of which over 90%are endowed in Russia,Canada,the United States,China and Australia,with China contributing 24%.The top 10 basins by coal-rock gas resource endowment,i.e.Alberta,Kuznetsk,Ordos,East Siberian,Bowen,West Siberian,Sichuan,South Turgay,Lena-Vilyuy and Tarim,collectively hold 75%of the global total.The Permian,Cretaceous,Carboniferous,Jurassic,and Paleogene-Neogene account for 32%,30%,18%,10%,and 7%of total coal-rock gas resources,respectively.The 10 most practical basins for future coal-rock gas exploration and development are identified as Alberta,Ordos,Kuznetsk,San Juan,Sichuan,East Siberian,Rocky Mountain,Bowen,Junggar and Qinshui.Propelled by successful development practices in China,coal-rock gas is now entering a phase of theoretical breakthrough,technological innovation,and rapid production growth,positioning it to spearhead the next wave of the global unconventional oil and gas revolution.
文摘Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.
基金supported by the Science and Technology Project of the State Grid Corporation of China“Research on Comprehensive Value Evaluation Method of Flexible Adjusting Resources under Carbon-electricity-certificate Market Coupling Environment”(No.5108-202455038A-1-1-ZN).
文摘The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource allocation model to determine the type,number and location of flexible resources to increase the values of resilience,carbon reduction and renewable energy consumption.To evaluate the values of resilience,a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators,energy storage systems(ESSs)and renewable energy generators in building restoration paths.The collaborative power-carbon-tradable green certificate(TGC)market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption.Finally,the model is formulated as a mixed-integer linear programming(MILP)with a nonconvex feasible domain,and the normalized normal constraint(NNC)method is applied to obtain approximate Pareto frontiers for decision makers.Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction.
文摘With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.
文摘Water resources health diagnosis is increasingly recognized as a critical tool.It should not only guide the preservation of water volume,quality,and ecological integrity but also assess the viability of carbon-neutral development pathways.But current research tends to consider water health assessment and carbon-neutrality planning as separate endeavors,which results in imprecise boundaries,disjointed metrics,and a weak linkage between diagnostic outcomes and testable management actions.This review brings together indicator systems,diagnostic systems,and environmental management systems architectures that make it possible to have integrated water-carbon governance.We define fundamental concepts and delimiting decisions followed by the examination of indicator designs across the hydrological regime,water quality,ecological integrity,service performance,resilience,and carbon-related measures,including intensity of energy/emissions,emissions caused by the watershed process of wastewater treatment,as well as the potential sink of the watershed.We compare diagnostic methods,such as composite indices and multi-criteria decision analysis,data-driven early-warning models,process-based and integrated simulations,as well as uncertainty-aware robustness models.Here,based on this synthesis,we suggest an environmental management systems(EMS)-based pathway,which connects the setting of the baseline,the diagnosis,the design of the intervention portfolio,and the measurement-reporting-verification into the closed-loop adaptive cycle.Digital enablement,comprising Internet of Things(IoT)monitoring,remote sensing,data fusion,optimization,and digital twins,is considered a viable way of scaling implementation,subject to interoperability,validation,and model governance.Among the major gaps,there are causal attribution to outcomes,cross-scale coupling of facility emissions and basin health,propagation of uncertainty in a coupled model,and credible Measurement,Reporting,and Verification(MRV)of non-CO_(2) gases and nature-based removals.The review gives a roadmap to normalize core metrics and fast-deployable systems to protect the health of the water resources and give verifiable progress towards carbon neutrality.
基金supported by the Key Support Project of Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China(No.U24A2095).
文摘Coal serves not only as a crucial energy resource but also as a significant reservoir of critical metal elements,including Lithium(Li),Gallium(Ga),Germanium(Ge),and rare earth elements(REE).This paper provides a systematic review of the enrichment characteristics,occurrence modes,and comprehensive utilization potential of these critical metals in coal.Globally,the distribution of these metal resources exhibits significant regional heterogeneity.While the concentration in most coals falls below industrial cut-off grades,anomalous enrichment in specific coal basins results in Li,Ga,Ge,and REE concentrations far exceeding global averages,highlighting their considerable potential as unconventional metal deposits.The occurrence modes of these metals are diverse:Li is primarily hosted in mineral phases;Ga exists in inorganic,organic,and complex forms;Ge shows a strong association with organic matter;and REE are mainly present in adsorbed/isomorphic forms within clay minerals,while also displaying organic affinity.Direct extraction of metals from raw coal is often cost-prohibitive;effective recovery is therefore more feasible when integrated with coal processing.Metals are further enriched in solid wastes such as coal gangue,fly ash,and bottom ash,from which recovery is more economically and technically viable.Current comprehensive utilization primarily employs integrated mineral processing-hydrometallurgy approaches.Future research should focus on elucidating the precise occurrence forms of metals in coal and solid wastes,optimizing pre-treatment methods,and selecting effective activators and leachants.Advancing the synergistic extraction and green recovery of multiple associated resources from coal and its by-products is essential for achieving high-value,comprehensive utilization of coal-based resources.
基金supported in part by the Chongqing Postgraduate Research and Innovation Project(CYB22250)National Natural Science Foundation of China(62271096,U20A20157)+2 种基金Natural Science Foundation of Chongqing-China(CSTB2023NSCQ-LZX0134,CSTB2024NSCQ-LZX0124)University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)。
文摘Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.
文摘This article examines the intelligent forest sentry system in the Mount Tai Scenic Area, analyzing its applications, accomplishments, and significance in forest fire prevention. It highlights the system's critical role in enhancing comprehensive forest fire prevention and control capabilities, accelerating emergency response times, and safeguarding the heritage resources of Mount Tai. Furthermore, the system is evolving into a "Smart Forest and Grassland Resources Supervision Platform" that integrates forest fire early warning, ecological environment monitoring, and pest and disease management to promote the sustainable development of forest resources.
文摘The concepts of the circular economy(CE)are actively popularized as ways of minimizing waste products and the need to rely on virgin resources.Nevertheless,their sustainability is doubtful at a general level where ecosystem functioning and ecosystem services(ES)are not given explicit attention.This review will combine both conceptual and empirical evidence of the connection between CE interventions and ES outcomes to enable more sustainable management of resources.We describe the effects of the CE strategies on the key environmental pressure pathways,altering ecosystem conditions,and impacting the delivery of regulating,provisioning,and cultural ecosystem services using a pressure condition-service framework.Analysis reveals that demand-side reduction and product life-extension strategies tend to offer more consistent ecosystem service co-benefits than recycling and recovery strategies because they do not involve production,and will cause less disturbance to the upstream environment.Contrastingly,recycling and recovery sustainability performance is highly dependent on the sources of energy,intensity of processing,and the safety of materials.Bio-based circularity has the potential to increase soil functionality and nutrient cycling,and mass application will result in trade-offs in terms of land competition and nutrient leakage.The sectoral analysis identifies the unique opportunities and threats in the agri-food systems,the built environment,plastics and textiles,electronics and critical minerals,and water and wastewater systems in terms of the burden displacement,local environmental pressures,and equity concerns.Harmonized reporting,coupled with supply-chain and spatial ecological assessment,threshold-conscious strategies that promote safe and regenerative circular systems should be put into the line of future research.
文摘The water resources surveying and hydrological is required to understand and manage the impacts of climate change on the water systems.This review discusses the ways in which such surveys can be used in improving climate resilience,procedures,practices,and opportunities.The innovations of the traditional ground-based surveys into the modern hydrological survey are the current technologies,remote sensing,Geographic Information Systems(GIS),and real-time sensor networks,which allow scanning the water resources in an extensive,accurate,and timely way.These high-level methods would help manage water systems with substantial data in the prediction of floods,droughts and other water hazards caused by climate change.In addition,a hydrological survey plays a very crucial role during the climate adaptation and mitigation process since it illuminates the sustainable level of water use and the sustainability of the ecosystem.Despite the tremendous development in the use of survey techniques,there remain problems of data gaps,the high cost of using the technique,and data integration enhancement.The future of hydrological surveying is to take advantage of the emerging technologies,encourage more stakeholder cooperation,and sustainable practices to enhance access to and use of data.This review determines the significance of hydrological surveying in the construction of climate resilience and presents the contribution of how future improvements in technology and cooperation can empower the management of water resources in the climate change environment.
文摘As the types of traffic requests increase,the elastic optical network(EON)is considered as a promising architecture to carry multiple types of traffic requests simultaneously,including immediate reservation(IR)and advance reservation(AR).Various resource allocation schemes for IR/AR requests have been designed in EON to reduce bandwidth blocking probability(BBP).However,these schemes do not consider different transmission requirements of IR requests and cannot maintain a low BBP for high-priority requests.In this paper,multi-priority is considered in the hybrid IR/AR request scenario.We modify the asynchronous advantage actor critic(A3C)model and propose an A3C-assisted priority resource allocation(APRA)algorithm.The APRA integrates priority and transmission quality of IR requests to design the A3C reward function,then dynamically allocates dedicated resources for different IR requests according to the time-varying requirements.By maximizing the reward,the transmission quality of IR requests can be matched with the priority,and lower BBP for high-priority IR requests can be ensured.Simulation results show that the APRA reduces the BBP of high-priority IR requests from 0.0341 to0.0138,and the overall network operation gain is improved by 883 compared to the scheme without considering the priority.
基金funded by Shandong Provincial Natural Science Foundation,grant number ZR2023MF111.
文摘To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication(JRC)systems under dynamic environments,an intelligent optimization framework integrating Deep Reinforcement Learning(DRL)and Graph Neural Network(GNN)is proposed.This framework models resource allocation as a Partially Observable Markov Game(POMG),designs a weighted reward function to balance radar and communication efficiencies,adopts the Multi-Agent Proximal Policy Optimization(MAPPO)framework,and integrates Graph Convolutional Networks(GCN)and Graph Sample and Aggregate(Graph-SAGE)to optimize information interaction.Simulations show that,compared with traditional methods and pure DRL methods,the proposed framework achieves improvements in performance metrics such as communication success rate,Average Age of Information(AoI),and policy convergence speed,effectively enabling resource management in complex environments.Moreover,the proposed GNN-DRL-based intelligent optimization framework obtains significantly better performance for resource management in multi-agent JRC systems than traditional methods and pure DRL methods.
文摘To deal with a polluted by-product of coal production,central China’s Shanxi Province has explored a governance path that addresses both the symptoms and root causes.
基金supported by the National Natural Science Foundation of China(W2412135)the Youth Innovation Promotion Association of the Chinese Academy of Sciences.
文摘As a major source of freshwater in Central Asia,Tajikistan is endowed with abundant glaciers and water resources.However,the country faces multiple challenges,including accelerated glacier retreat,complex inter-government water resource management,and inefficient water use.Existing research has predominantly focused on individual hydrological processes,such as glacier retreat,snow cover change,or transboundary water issues,but it has yet to fully capture the overall complexity of water system.Tajikistan’s water system functions as an integrated whole from mountain runoff to downstream supply,but a comprehensive study of its water resource has yet to be conducted.To address this research gap,this study systematically examined the status,challenges,and sustainable management strategies of Tajikistan’s water resources based on a literature review,remote sensing data analysis,and case studies.Despite Tajikistan’s relative abundance of water resources,global warming is accelerating glacier melting and altering the hydrological cycles,which have resulted in unstable runoff patterns and heightened risks of extreme events.In Tajikistan,outdated infrastructure and poor management are primary causes of low water-use efficiency in the agricultural sector,which accounts for 85.00%of the total water withdrawals.At the governance level,Tajikistan faces challenges in balancing the water-energy-food nexus and transboundary water resource issues.To address these issues,this study proposes core paths for Tajikistan to achieve sustainable water resource management,such as accelerating technological innovation,promoting water-saving agricultural technologies,improving water resource utilization efficiency,and establishing a community participation-based comprehensive management framework.Additionally,strengthening cross-border cooperation and improving real-time monitoring systems have been identified as critical steps to advance sustainable water resource utilization and evidence-based decision-making in Tajikistan and across Central Asia.
基金supported by NASA Oklahoma Established Program to Stimulate Competitive Research(EPSCoR)Infrastructure Development,“Machine Learning Ocean World Biosignature Detection from Mass Spec,”(PI:Brett McKinney),Grant No.80NSSC24M0109Tandy School of Computer Science,The University of Tulsa.
文摘Bacterial growth requires strategic allocation of limited intracellular resources,especially under cold stress,where stabilized messenger ribonucleic acid(mRNA)secondary structures slow translation by impairing ribosome binding.Escherichia coli(E.coli)counters this bottleneck by inducing the cold-shock protein A(CspA),an RNA chaperone that remodels inhibitory structures.However,synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism,creating a fundamental resource-allocation trade-off.In this work,we develop a dynamical model capturing the interplay between metabolic precursors,ribosomes,and CspA,and use it to examine how growth and allocation patterns shift with temperature.Steady-state analysis shows that each temperature produces a distinct,locally stable equilibrium,illustrating how cold environments reshape cellular priorities.We then formulate growth maximization as an optimal control problem,solved using Pontryagin’s Maximum Principle,to identify allocation strategies that balance translation maintenance and biomass production.The resulting optimal strategies exhibit bang-bang and singular structures,highlighting periods of extreme and intermediate allocation that reflect how bacteria might dynamically prioritize competing cellular functions.These control patterns converge to their corresponding steady state allocations and provide quantitative insight into optimal resource management under cold stress.These results provide a quantitative optimal-control framework linking RNA-level cold-shock adaptation to proteome allocation and growth,yielding testable predictions for how bacteria balance translational maintenance and biomass production at suboptimal temperatures.
基金supported by the Scientific Research Basic Ability Improvement Project for Young and Middleaged Teachers in Guangxi Universities,China,“Research on Digital Marketing of Characteristic Agricultural Products in Western Guangxi”(Grant No.2024KY0740)Scientific Research Basic Ability Improvement Project for Young and Middle-aged Teachers in Guangxi Universities,China:“Research on Industrial Revitalization Promoting High-Quality Development of Rural Economy in Guangxi under the Concept of Transportation-Tourism Integration”(Grant No.2024KY0743)+1 种基金Guangxi Higher Education Undergraduate Teaching Reform Project:“Reform and Practice of a‘Three-Line Five-Step’Virtual Simulation Teaching Mode for the Financial Sharing Course Based on Inquiry-Based Learning”(Grant No.2024JGB355)Guangxi Philosophy and Social Sciences Research Annual Project:“Research on the Mechanism and Path of Service Talent Team Building for Guangxi Community Elderly Care Service Complexes”(Grant No.24GLF012).
文摘Expanding economic potential and protecting the environment,while facing mounting climate and biodiversity stress,is becoming the challenge of environmental resource management.This review explores developments and ongoing obstacles in six areas of resource of the Sustainable Development Goal(SDG)that include water;land,soils,and food systems;forests and terrestrial carbon biodiversity governance;oceans,coasts,and fisheries;biodiversity connectivity;and extractives and energy-transition supply chains.Most of the interventions continue to ignore ecological thresholds,accruing effects,and cross-system feedbacks,whereas monitoring systems focus on measures of activity rather than confirmed results.Conversely,sustained improvement is most frequently associated with integrated governance,which incorporates open measurement,implementation,rights-based participation,and fair distribution of benefits.Local conservation is often overwhelmed by market forces of demand and structural forces,including subsidies,supply chains,and investment in infrastructure,which fail to stop leakage or war,unless accountability mechanisms are in place.Climate change also aggravates set baselines and puts forward the importance of adaptive regulation,spatial planning,and diversified portfolios,which combine engineered reliability with ecosystem resilience.The review brings out current SDG priorities,which include outcome-based indicators,causal evaluation,governance structures that enhance legitimacy,and transition planning that harmonizes the mineral sourcing,renewable deployment,biodiversity,and water limits.Combined,these observations indicate that striking the balance between ecology and economy is possible when ecological boundaries are under consideration as binding constraints and equity is perceived as a source of sustainability.
基金supported by the Shandong Province Higher Education Institutions New Technology R&D Platform—Spatiotemporal IoT Cloud Application New Technology R&D Center,Shandong Vocational Education Skill Master Studio—Zhao Yaqian Skill Master Studio,and Shandong University of Engineering and Vocational Technology.
文摘Hyperspectral remote sensing has emerged as a transformative technology for sustainable natural resource management by providing unprecedented insight into the biochemical,biophysical,and compositional properties of Earth’s surface.The high spectral resolution of hyperspectral sensors allows a very specific discrimination of materials,monitoring of environmental stress at a very early stage,and provides quantitative retrieval of ecological and geochemical parameters in a wide range of landscapes.The booming technology in sensor design,machine learning,spectral unmixing,and multi-sensor data fusion has further improved the analysis potential and application of imaging spectroscopy to a large extent.This paper involves a discussion of the oversight of such technological advances and the manner in which they are utilized in the principal fields that include forestry,agriculture,water,mineral exploration,and coastal ecosystems.Case studies allow us to identify the potential practical consequences of both spaceborne and unmanned aerial vehicles(UAV)-based hyperspectral systems and AI-based workflows that can be used to aid in more efficient and accurate environmental review.Even though the issues associated with data volume,atmospheric impacts,lack of uniformity in the calibration process,and socioeconomic limits continue to exist,the new technology in sensor miniaturization,cloud computing,and artificial intelligence indicates a fast-changing environment.All these developments make hyperspectral remote sensing a key instrument in solving global sustainability problems and evidence-based management of natural resources in an evolving world.
基金Supported by the National Science and Technology Major Project of China (No.C6-3416-M01)。
文摘Integrated sensing and communication(ISAC) systems can enhance security and reliability in full-duplex( FD) networks.This paper studies the resource allocation problem in FD networks based on ISAC.We formulate an optimization problem that jointly considers beamforming,radar waveform,and reflection coefficient of a hybrid reconfigurable intelligent surface,aiming to maximize the beampattern gain of the concealed target while maintaining the quality of service of communication users.A two-stage algorithm is designed to solve this problem: the first stage optimizes the radar waveform and beamforming,and the second focuses on the reflection coefficient design.We acquire the feasible solution using semidefinite programming relaxation.The optimality of the feasible solution for radar waveform and beamforming subproblem is ensured through Cauchy-Schwartz inequality.For the reflection coefficient design,an approximate optimal solution is acquired through successive convex approximation.Numerical results demonstrate that the proposed algorithm can achieve a superior performance trade-off between communication and radar sensing compared to the benchmarks.
文摘The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.
