Conditions for the enrichment of karst hydrothermal resources in Bohai Bay Basin

Drilling for karst hydrothermal resources in eastern China has posed challenges,including disparities between the temperature and yield of geothermal water.It is evident that relying solely on geothermal anomalies or indications of karst reservoirs is inadequate for the exploration of karst hydrothermal resources.This study seeks to elucidate the cause of geothermal sweet spots by analyzing the interplay between geothermal anomalies and karst reservoirs and the underlying geological conditions for karst hydrothermal enrichment.Key findings include:(1)the Bohai Bay Basin has been geologically favorable for the development of karst hydrothermal resources since the Mesozoic era;(2)the karst hydrothermal enrichment varies significantly between the basin’s margin and its interior.On the basin margin,the enrichment is largely driven by groundwater activity and faults,particularly where faults facilitate the upwelling of geothermal water.In contrast,within the basin’s interior,karst hydrothermal resources are predominantly influenced by buried hills and are especially enriched in areas facilitating the discharge of deep geothermal waters.

Integrated wellbore-reservoir-geomechanics modeling for enhanced interpretation of distributed fiber-optic strain sensing data in hydraulicfracture analysis

Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS responses associated with near-wellbore hydraulic fracture properties is still unclear.To address this knowledge gap,we use coupled wellbore-reservoir-geomechanics simulations to study measured strain-change behavior and infer hydraulic fracture characteristics.The crossflow among fractures is captured through explicit modeling of the transient wellbore flow.In addition,local grid refinement is applied to accurately capture strain changes along the fiber.A Base Case model was designed with four fractures of varying properties,simulating strain change signals when the production well is shut-in for 10 d after 240 d of production and reopened for 2 d.Strain-pressure plots for different fracture clusters were used to gain insights into inferring fracture properties using DSS data.When comparing the model with and without the wellbore,distinct strain change signals were observed,emphasizing the importance of incorporating the wellbore in FO-DSS modeling.The effects of fracture spacing and matrix permeability on strain change signals were thoroughly investigated.The results of our numerical study can improve the understanding of the relation between DSS signals and fracture hydraulic properties,thus maximizing the value of the dataset for fracture diagnostics and characterization.

Defining Cities by Water: Addressing Capital Misallocation in a Race to Conserve Resources

As part of its efforts to promote a sustainable and high-quality development,China has pledged to reduce water consumption and create a water-efficient society.On the basis of identifying the institutional root causes of excessive capital allocation and excessive water consumption in China’s water-intensive industrial sectors,this study elaborates how the national water-efficient cities assessment contributes to optimized capital allocation.Our research shows that national water-efficient cities assessment has motivated local governments to compete for water efficiency.To conserve water,local governments regulated the entry and exit of water-intensive enterprises,discouraged excessive investments in water-intensive sectors,and phased out obsolete water-intensive capacities within their jurisdictions.This approach has resulted in mutually beneficial outcomes,including improved allocation of capital,enhanced water efficiency,and reduced emissions.This paper offers policy recommendations for establishing a water-efficient society throughout the 14^(th) Five-Year Plan(2021-2025)period by presenting empirical evidence on the policy effects of resource efficiency evaluation.

Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.

基于拉曼放大的长距离BOTDR传感系统研究

布里渊光时域反射(BOTDR)系统可实现沿传感光纤的全分布式温度和应变传感,然而由于光纤损耗的存在,布里渊信号光在长距离传输后往往会淹没在噪声中,信噪比极低。对此,利用拉曼泵浦对光纤全段布里渊信号的增益效果,研究拉曼放大前后系统在测量距离方面的差异和不同泵浦功率对应的放大曲线。通过实验验证了基于拉曼放大的BOTDR传感系统测量距离可提高至100 km量级;在50 m空间分辨率下,成功实现了光纤末端103 km处的温度测量,获得了2.19℃的测量精度。

Data-Driven Control of Distributed Event-Triggered Network Systems

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

MSs-MEC中基于DRL的服务缓存和任务迁移联合优化算法

多服务移动边缘计算(multiple-services mobile edge computing,MSs-MEC)能根据需求自适应调整服务缓存决策,使得部署在用户侧的边缘服务器能够灵活处理不同服务类型的任务。但在实际应用中,特定类型任务的成功迁移依赖于服务环境的提前安装。此外,同时进行任务迁移和服务缓存可能会因时间冲突而导致计算延时。因此,针对上述相关问题,首先将任务迁移和服务缓存决策进行解耦,针对深度强化学习(deep reinforcement learning,DRL)在具有高维的混合决策空间的性能提升不明显的缺点(例如资源分配时利用率不高),将DRL与Transformer结合,通过在历史数据中学习,输出当前时隙的任务迁移决策和下一时隙的任务决策,保证任务到达边缘服务器时能立即执行。其次,为了提高资源分配问题中的资源利用率,将问题分解为连续资源分配问题和离散的任务迁移与服务缓存问题,利用凸优化技术求解资源分配最优决策。广泛的数值结果表明,与其他基线算法相比,提出的算法能有效地减少任务的平均完成时延,同时在资源利用率和稳定性方面也有优异的表现。

基于Dinkelbach-Quadratic算法的高速铁路通信能效优化功率分配研究

为了提高高速铁路通信能量效率,提出一种基于Dinkelbach-Quadratic变换的迭代算法来解决高速铁路通信下行链路的功率分配问题。通过在小区内部署远程天线单元构建高速铁路通信分布式天线系统模型,基于完美信道状态信息(CSI)和列车内接入点反馈的最小速率要求建立能效优化模型;基于所建立的非凸优化模型,采用Dinkelbach变换将分数形式的非线性规划问题转换为求差形式;利用Quadratic变换特性对目标函数进一步展开;采用交替迭代的方法完成能效最优的功率分配。仿真结果表明:所提算法在满足最小传输速率和最大发射功率的同时,可以兼顾系统能量效率和频谱效率;随着发射天线数量和接收中继数量的增加,系统性能也有明显提升,相比传统算法系统的能效和频谱效率至少提升5%。

VEC中基于DRL的“端-多边-云”协作计算卸载算法

目的为了解决车载边缘计算中用户服务质量低以及边缘节点资源不足的问题,方法结合车载边缘计算和停车边缘计算技术,提出“端-多边-云”协作计算卸载模型,并设计基于DRL的协作计算卸载与资源分配算法(cooperative computation offloading and resource allocation algorithm based on DRL,DRL-CCORA)。首先,将路边停放车辆的算力构建成停车边缘服务器(parking edge server,PES),联合边缘节点为车辆任务提供计算服务,减轻边缘节点的负载;其次,将计算卸载与资源分配问题转化为马尔可夫决策过程模型,综合时延、能耗和服务质量构建奖励函数,并根据任务需要的计算资源、任务的最大容忍时延以及车辆到PES的距离对计算任务进行预分类处理,缩减问题的规模;最后,利用双深度Q网络(double deep q network,DDQN)算法获得计算卸载和资源分配的最优策略。结果结果表明,相较于对比算法,所提算法的用户总服务质量提高了6.25%,任务的完成率提高了10.26%,任务计算的时延和能耗分别降低了18.8%、5.26%。结论所提算法优化了边缘节点的负载,降低了任务完成的时延和能耗,提高了用户的服务质量。

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

基于LandTrendr算法的广东省1990-2020年森林干扰与森林恢复的动态变化分析

为准确掌握广东省森林干扰与森林恢复的动态特征,文章基于谷歌地球引擎(Google Earth Engine,GEE)云平台构建Landsat长时序年度无云地表反射率影像集,采用LandTrendr(Landsat-based detection of Trends in Distur-bance and Recovery)算法提取广东省1990—2020年森林干扰与森林恢复的时空分布特征,并分析其演变的驱动因素,比较不同森林类型的干扰与恢复特征。结果表明:(1)1990—2020年,广东省的森林干扰总面积约为1.35×10^(4)km^(2),集中分布在广东省西部、东部和中部小范围地区,干扰面积最大的3个城市分别为韶关、梅州、清远市;森林恢复总面积约为1.91×10^(4)km^(2),集中分布在广东省北部和西部地区,恢复面积最大的3个城市分别为韶关、清远、肇庆市。(2)广东省森林干扰与森林恢复均集中发生在海拔小于等于600 m的地区,高海拔地区的森林面积较为稳定;广东省森林干扰集中发生在坡度小于等于25°的地区,森林恢复集中发生在坡度小于等于35°的地区。(3)广东省森林干扰在1996年后发生较为频繁,其中2011年的森林干扰面积最大;森林恢复主要集中在2001—2016年,其中2012年的森林恢复面积最大。(4)广东省的森林干扰与森林恢复主要受雨雪冰冻灾害、台风、病虫害等自然因素以及森林火灾、城市化、采伐、林业政策等人为因素的综合影响,其中雨雪冰冻灾害对广东省北部地区的常绿针叶林的影响较大,但该类型的森林恢复也较快。

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

Reducing Operation Emissions and Improving Work Efficiency Using a Pure Electric Wheel Drive Tractor

In response to the problems of excessive greenhouse-gas and particulate emissions and the low traction efficiency of conventional diesel tractors in the field,a purely electric wheel-side drive tractor was studied,including an electric motor drive system,a battery ballast system,and an electro–hydraulic suspension system.This paper develops a dynamics model of an electric tractor-ploughing unit under complex soil conditions,leading to the proposal of an active control method for drive wheel torque and a joint control method for the traction force of the suspension system and the front-and rear-axle loads of a tractor.Finally,the tractor is prototyped and assembled,and ploughing tests are carried out.The ploughing results show that the active torque-distribution control method proposed in this study reduces the tractor slip by 14.83%and increases the traction efficiency by 10.28%compared with the average torquedistribution mode.Compared with the conventional traction control mode,the joint control method for traction and ballast proposed in this paper results in a 3.7%increase in traction efficiency,a 15.05%decrease in slip,and a 4.9%reduction in total drive motor energy consumption.This study will help to improve the operation quality and traction efficiency of electric tractors in complex soil conditions.

Indian monsoon drove the dispersal of the thoracica group of Scytodes spitting spiders

We examined the global biogeography of the Scytodes thoracica group of spitting spiders based on 23 years of sampling at the species level(61 species in the thoracica group and 84 species of Scytodes)using DNA data from six loci.Our results indicated that the thoracica group initially dispersed from Southeast Asia to East Africa between 46.5 and 33.0 million years ago,and dispersal events intensified between Southeast/South Asia and East/South Africa from the early to late Miocene.The timing of these events indicates that Asian-African faunal exchange of the thoracica group was driven by the Indian monsoon,and the pattern of dispersal suggests that colonialization took root when the Indian monsoon shifted from a North-South direction to an East-West direction from the middle Eocene.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

Deep bedrock geothermal resources in the Maichen Sag,Beibuwan Basin and their potential for exploitation and utilization

The Maichen Sag in the south-central part of the Beibuwan Basin is abundant in geothermal resources that have not been widely exploited and utilized due to inadequate research on it.This study evaluated the geothermal resources in the Maichen Sag based on the regional geological setting and geothermal conditions.Grid units for assessment and a geological model for areas with geothermal resources were established using spatial analysis techniques.The spatial distribution models of the physical and thermophysical properties of the geothermal reservoirs were also built using the Kriging interpolation method.Based on the terrestrial heat flow distribution in the target areas,the spatial distribution of the geo-temperature field through the inversion under the constraints of the temperature data from boreholes were predicted.Factors such as deep geo-temperature,thermophysical properties of rocks,and terrestrial heat flow values,were integrated into this quantitative evaluation of geothermal resources through the geological modeling-based volume method and the geothermal reservoir engineering-based numerical simulation method.The results show that the Maichen Sag has favorable heat source conditions with intersected,deep-rooted faults and widely developed Upper Paleozoic fissured granite geothermal reservoirs.The northern outer slope zone at a burial depth of 3‒5 km on the tectonic plane of the basement in the sag is suggested to be a potential target area,where,as calculated using the volume method,is likely to be the home to the total geothermal resources of 80.4×10^(9)GJ(i.e.,2.75×10^(9)tonnes of coal equivalent(tce))in the bedrock geothermal reservoirs at a burial depth of 3‒6 km.The geotemperature of 172‒201℃at a formation depth of 5 km in the sag also indicates that the deep geothermal resources are of high value for exploitation.

‘Partly'globalized networks and driving mechanism in resource-based state-owned enterprises:A case study of J Group

In the context of economic globalization,while multinational enterprises from developed countries occupy a high-end position in the global value chain,enterprises from developing countries are often marginalized in the world market.In China,resource-based state-owned enterprises(SOEs)are tasked with the mission of safeguarding resource security,and their internationalization development ideas and strategic deployment are significantly and fundamentally different from those of other non-state-owned enterprises and large multinational corporations.This study provides ideas for the globalization policies of enterprises in developing countries.We consider J Group in western China as a case and discuss its productive investment and global production network development from 2010 to 2019.We found that J Group was‘Partly'globalized,and there are multiple core nodes with the characteristics of centralized and decentralized coexistence in the production network;in addition,the overall layout centre shifted to Southeast Asia and China;however,its global production was restricted by the enterprise's investment security considerations,support and restrictions of the home country,political security risk of the host country,and sanctions from the West.These findings provide insights for future research:under the wave of anti-globalization and'internal circulation as the main body',resource SOEs should consider the potential risk of investment,especially keeping the middle and downstream industrial chain in China as much as possible.

Monitoring Surface Deformation Using Distributed Scatterers InSAR

In the past two decades,extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data.The introduction of distributed scatterers in Distributed Scatterers InSAR(DS-InSAR)has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points.This study traces the history of DS-InSAR,presents the definition and characteristics of distributed scatterers,and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps:statistically homogeneous pixel selection and phase optimization.Additionally,the latest research progress in this field is tracked and the possible development direction in the future is discussed.Through simulation experiments and two real InSAR case studies,the proposed algorithms are compared and verified,and the advantages of DS-InSAR in deformation measurement practice are demonstrated.This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.