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
Knowledge-based VisualQuestion Answering(VQA)requires the integration of visual information with external knowledge reasoning.Existing approaches typically retrieve information from external corpora and rely on pretra...Knowledge-based VisualQuestion Answering(VQA)requires the integration of visual information with external knowledge reasoning.Existing approaches typically retrieve information from external corpora and rely on pretrained language models for reasoning.However,their performance is often hindered by the limited capabilities of retrievers and the constrained size of knowledge bases.Moreover,relying on image captions to bridge the modal gap between visual and language modalities can lead to the omission of critical visual details.To address these limitations,we propose the Reflective Chain-of-Thought(ReCoT)method,a simple yet effective framework inspired by metacognition theory.ReCoT effectively activates the reasoning capabilities ofMultimodal Large LanguageModels(MLLMs),providing essential visual and knowledge cues required to solve complex visual questions.It simulates a metacognitive reasoning process that encompasses monitoring,reflection,and correction.Specifically,in the initial generation stage,an MLLM produces a preliminary answer that serves as the model’s initial cognitive output.During the reflective reasoning stage,this answer is critically examined to generate a reflective rationale that integrates key visual evidence and relevant knowledge.In the final refinement stage,a smaller language model leverages this rationale to revise the initial prediction,resulting in amore accurate final answer.By harnessing the strengths ofMLLMs in visual and knowledge grounding,ReCoT enables smaller language models to reason effectively without dependence on image captions or external knowledge bases.Experimental results demonstrate that ReCoT achieves substantial performance improvements,outperforming state-of-the-art methods by 2.26%on OK-VQA and 5.8%on A-OKVQA.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
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
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
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
Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources...Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.展开更多
In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dyna...In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.展开更多
The quality of cardiopulmonary resuscitation(CPR) significantly influences survival and neurological outcomes in patients with cardiac arrest(CA).Although mechanical chest compression devices and extracorporeal cardio...The quality of cardiopulmonary resuscitation(CPR) significantly influences survival and neurological outcomes in patients with cardiac arrest(CA).Although mechanical chest compression devices and extracorporeal cardiopulmonary resuscitation(ECPR) have demonstrated some benefits,high-quality manual CPR remained the essential first step,particularly in resource-limited settings.In this study,we examined whether opportunities existed to improve manual CPR performance using preliminary data from our recent survey conducted in a province in western China.We aim to emphasize the importance of improving manual CPR quality before implementing advanced interventions.展开更多
Owing to the emergence of drug resistance and high morbidity,the need for novel antiviral drugs with novel targets is highly sought after.Marine-derived compounds mostly possess potent antiviral activity and serve as ...Owing to the emergence of drug resistance and high morbidity,the need for novel antiviral drugs with novel targets is highly sought after.Marine-derived compounds mostly possess potent antiviral activity and serve as a primary source for developing novel antiviral drugs,making the rapid discovery and evaluation of marine antiviral agents particularly crucial.Thus,future research should place greater emphasis on the identification of novel antiviral targets through the combination of artificial intelligence(AI)and structural pharmacology,as well as expanding the marine resource and target databases.展开更多
Mineral resources in Asia continent and its mining industry play a significant role in the economic growth and industrialization of both Asia and the world.Asia continent boasts the most comprehensive kinds of mineral...Mineral resources in Asia continent and its mining industry play a significant role in the economic growth and industrialization of both Asia and the world.Asia continent boasts the most comprehensive kinds of minerals,with reserves of at least 38 of over 80 widely used minerals worldwide accounting for more than30%of the global total reserves.Asia continent experienced three main tectonic evolution and mineralization stages:The Precambrian,the Paleozoic,and the Mesozoic to Cenozoic.The abundant mineral resources in this continent can be divided into seven first-order metallogenic belts(metallogenic domains),18 second-order metallogenic belts(metallogenic provinces),61 third-order metallogenic belts(metallogenic zones),and nine main minerogenetic series.Asia continent exhibits the most significant metallogenic specialization among all continents.Specifically,granite belts of Asia continent manifest pronounced metallogenic specialization of tin,rare metals,and porphyry Cu-Au-Mo deposits.Its maficultramafic rock belts and ophiolite belts display notable metallogenic specialization of lateritic nickel deposits and magmatic type chromite deposits,while its Mesozoic to Cenozoic basalt belts show remarkable metallogenic specialization of lateritic bauxite deposits.Consequently,many giant metallogenic belts were formed,including the Southeast Asian tin belt,the Qinghai-Xizang Plateau rare metal metallogenic belt,the Tethyan porphyry Cu-Au-Mo metallogenic belt,the circum-Pacific porphyry Cu-Au-Mo metallogenic belt,the Southeast Asian lateritic bauxite metallogenic belt,the Deccan Plateau lateritic bauxite metallogenic belt in India,the Southeast Asian lateritic nickel metallogenic belt,and the Tethyan magmatic type chromite metallogenic belt—all of which are significant metallogenic belts in Asia continent.Future mineral exploration in Asia should focus primarily on the Precambrian mineralization of ancient cratons,the Paleozoic mineralization of the Central Asian-Mongolian orogenic belt,and the Mesozoic to Cenozoic mineralization of the Tethyan and circum-Pacific mobile belts.Asia's mining industry not only underpins its own economic growth but also propels global economic development and industrialization,contributing significantly to the world economy.Asia boasts the highest production value of minerals,the largest annual production of minerals,and the greatest trade value of mineral products among all the continents,having emerged as the trade center of global mineral products and the center of the mining industry economy.China is identified as one of the few countries that possess the most comprehensive kinds of minerals,and its mining industry has supported and driven the economic development and industrialization of Asia and even the world.Standing as the largest mineral producer worldwide,China ranked first in the production of 28 mineral commodities in the world in 2022.Besides,China exhibits the highest annual production value of minerals and the largest trade value of mineral products among all countries.Therefore,China's demand for global mineral products influences the global supply and demand patterns of minerals and the world economic situation.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.
基金supported by the National Natural Science Foundation of China(Nos.62572017,62441232,62206007)R&D Program of Beijing Municipal Education Commission(KZ202210005008).
文摘Knowledge-based VisualQuestion Answering(VQA)requires the integration of visual information with external knowledge reasoning.Existing approaches typically retrieve information from external corpora and rely on pretrained language models for reasoning.However,their performance is often hindered by the limited capabilities of retrievers and the constrained size of knowledge bases.Moreover,relying on image captions to bridge the modal gap between visual and language modalities can lead to the omission of critical visual details.To address these limitations,we propose the Reflective Chain-of-Thought(ReCoT)method,a simple yet effective framework inspired by metacognition theory.ReCoT effectively activates the reasoning capabilities ofMultimodal Large LanguageModels(MLLMs),providing essential visual and knowledge cues required to solve complex visual questions.It simulates a metacognitive reasoning process that encompasses monitoring,reflection,and correction.Specifically,in the initial generation stage,an MLLM produces a preliminary answer that serves as the model’s initial cognitive output.During the reflective reasoning stage,this answer is critically examined to generate a reflective rationale that integrates key visual evidence and relevant knowledge.In the final refinement stage,a smaller language model leverages this rationale to revise the initial prediction,resulting in amore accurate final answer.By harnessing the strengths ofMLLMs in visual and knowledge grounding,ReCoT enables smaller language models to reason effectively without dependence on image captions or external knowledge bases.Experimental results demonstrate that ReCoT achieves substantial performance improvements,outperforming state-of-the-art methods by 2.26%on OK-VQA and 5.8%on A-OKVQA.
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.
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.
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.
文摘Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.
基金supported by the National Natural Science Foundation of China(61702528,61806212,62173336)。
文摘In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.
文摘The quality of cardiopulmonary resuscitation(CPR) significantly influences survival and neurological outcomes in patients with cardiac arrest(CA).Although mechanical chest compression devices and extracorporeal cardiopulmonary resuscitation(ECPR) have demonstrated some benefits,high-quality manual CPR remained the essential first step,particularly in resource-limited settings.In this study,we examined whether opportunities existed to improve manual CPR performance using preliminary data from our recent survey conducted in a province in western China.We aim to emphasize the importance of improving manual CPR quality before implementing advanced interventions.
文摘Owing to the emergence of drug resistance and high morbidity,the need for novel antiviral drugs with novel targets is highly sought after.Marine-derived compounds mostly possess potent antiviral activity and serve as a primary source for developing novel antiviral drugs,making the rapid discovery and evaluation of marine antiviral agents particularly crucial.Thus,future research should place greater emphasis on the identification of novel antiviral targets through the combination of artificial intelligence(AI)and structural pharmacology,as well as expanding the marine resource and target databases.
基金funded by geological survey project of China Geological Survey(DD20211404)。
文摘Mineral resources in Asia continent and its mining industry play a significant role in the economic growth and industrialization of both Asia and the world.Asia continent boasts the most comprehensive kinds of minerals,with reserves of at least 38 of over 80 widely used minerals worldwide accounting for more than30%of the global total reserves.Asia continent experienced three main tectonic evolution and mineralization stages:The Precambrian,the Paleozoic,and the Mesozoic to Cenozoic.The abundant mineral resources in this continent can be divided into seven first-order metallogenic belts(metallogenic domains),18 second-order metallogenic belts(metallogenic provinces),61 third-order metallogenic belts(metallogenic zones),and nine main minerogenetic series.Asia continent exhibits the most significant metallogenic specialization among all continents.Specifically,granite belts of Asia continent manifest pronounced metallogenic specialization of tin,rare metals,and porphyry Cu-Au-Mo deposits.Its maficultramafic rock belts and ophiolite belts display notable metallogenic specialization of lateritic nickel deposits and magmatic type chromite deposits,while its Mesozoic to Cenozoic basalt belts show remarkable metallogenic specialization of lateritic bauxite deposits.Consequently,many giant metallogenic belts were formed,including the Southeast Asian tin belt,the Qinghai-Xizang Plateau rare metal metallogenic belt,the Tethyan porphyry Cu-Au-Mo metallogenic belt,the circum-Pacific porphyry Cu-Au-Mo metallogenic belt,the Southeast Asian lateritic bauxite metallogenic belt,the Deccan Plateau lateritic bauxite metallogenic belt in India,the Southeast Asian lateritic nickel metallogenic belt,and the Tethyan magmatic type chromite metallogenic belt—all of which are significant metallogenic belts in Asia continent.Future mineral exploration in Asia should focus primarily on the Precambrian mineralization of ancient cratons,the Paleozoic mineralization of the Central Asian-Mongolian orogenic belt,and the Mesozoic to Cenozoic mineralization of the Tethyan and circum-Pacific mobile belts.Asia's mining industry not only underpins its own economic growth but also propels global economic development and industrialization,contributing significantly to the world economy.Asia boasts the highest production value of minerals,the largest annual production of minerals,and the greatest trade value of mineral products among all the continents,having emerged as the trade center of global mineral products and the center of the mining industry economy.China is identified as one of the few countries that possess the most comprehensive kinds of minerals,and its mining industry has supported and driven the economic development and industrialization of Asia and even the world.Standing as the largest mineral producer worldwide,China ranked first in the production of 28 mineral commodities in the world in 2022.Besides,China exhibits the highest annual production value of minerals and the largest trade value of mineral products among all countries.Therefore,China's demand for global mineral products influences the global supply and demand patterns of minerals and the world economic situation.
