Ecosystem Health Assessment of Honghu Lake Wetland of China Using Artificial Neural Network Approach

Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Project(HLWPRDP) has been implemented since 2004.A back propagation(BP) artificial neural network(ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland.And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project.Particularly,12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs.The output is one layer of ecosystem health index.After training and testing the BP ANNs,an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland.The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP(in 2002) to middle health after the implementation of the HLWPRDP(in 2005).It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.

Ecosystem Health and Comprehensive Ecological Benefit Assessment of an Artificial Wetland in Western Jilin Province

[Objective] This research aimed to assess the state of ecosystem health and comprehensive ecological benefit of an artificial wetland in western Jilin Province. [Method] To investigate the effects of reclaimed water from Yingtai Oil Production Plant on the wetland ecosystem, a comprehensive ecological assessment index of an artificial wetland in the west of Jilin Province was established to measure the ecological economic and social benefits. The quantitative evaluation on the ecosystem health and comprehensive ecological benefit of the artificial wetland were carried out from 2003 to 2010 and were measured by means of the square difference method. [Result] After eight years of irrigation by reclaimed water, the levels of ecosystem health and benefit of the artificial wetland improved from Grade IV to Grade II, and the ecological environment, economic and social development of the wetland tended to improve. [Conclusion] The results indicated that the use of reclaimed water for irrigation of wetland ecosystems was a suitable way to control drought in arid and semi-arid regions.

Gap Filling of Net Ecosystem CO₂Exchange (NEE) above Rain-Fed Maize Using Artificial Neural Networks (ANNs)

The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).

Emergy evaluation of the artificial forest ecosystems in the watershed of Miyun Reservoir:a case study for ecosystems valuation and environmental management

To build the artificial forest ecosystem is the major eco-economic development model in the watershed of Miyun Reservoir. It is very important to evaluate the benefits of those ecosystems. Emergy theories are very helpful for us to establish a science-based assessment framework. Emergy evaluation of the artificial forest ecosystems in the watershed of Miyun Reservoir is used to asses the relative values of several ecological functions (sometimes called ecosystem services) and main ecosystem storages (sometimes called natural capital). The main driving energies, internal processes and storages are evaluated. The main functions, including transpiration, GPP and infiltration, are evaluated, which are 609em$/ha/yr, 6,245em$/ha/yr and 340em$/ha/yr respectively. The total values of major environmental services are 4,683em$/ha/yr in the artificial forest ecosystem. The main storages of natural capital including live biomass, soil moisture, organic matter, underground water and landform are estimated, which are 112,028em$/ha, 9em$/ha, 40,718em$/ha, 34em$/ha and 6,400,514em$/ha respectively. The largest value is landform, which accounts for 97.7% of these calculated total emdollar values. The concept of replacement value is explored using the emergy values of both ecosystem services and natural capital. The total calculated replacement values are 302,160em$/ha.

Ecosystem service value of artificial reef area in Qingdao Luhaifeng sea ranching:A meta-analysis and benefit transfer valuation

This study evaluates the ecosystem service value of Qingdao Luhaifeng Sea Ranch artificial reef area through the established meta-analysis value transfer model,and the results show that the total value will be 150 million yuan in 2022.According to the regression results of the meta-analysis,the types of ecosystem services,the types of pasture sediment,the population density of the pasture area,and the economic level all have a significant impact on the wetland value.The model in this paper passes the validity test,and the conclusions are the same as many current empirical studies.This shows that the benefit transfer method meta-analysis can save assessment labor,time and capital,and the decision-makers can make judgments quickly.Meta-analysis is an effective and fast ex ante evaluation tool.The reliability of the value transfer method of meta-analysis largely depends on the quantity and quality of existing studies and the establishment of mathematical models.It is necessary to expand the number of literature searches.However,the current evaluation method of ecosystem services in China has just started,so the government needs to encourage research institutions to evaluate the value of ecosystem services to provide more effective and abundant research literature.

Study on Qianling Lake Restoration with Artificial Wetland

The ecological restoration of water quality in Qianling Lake was conducted by artificial wetland, which transformed N and P in wastewater into essential matters in organism tissues, so pollutants discharged into Qianting Lake were reduced for the purpose of restoration.

Artificial root exudates and soil organic carbon mineralization in a degraded sandy grassland in northern China

Plant root exudates contain various organic and inorganic components that include glucose, citric and oxalic acid. These components affect rhizosphere microbial and microfaunal activities, but the mechanisms are not fully known. Studies concerned from degraded grassland ecosystems with low soil carbon（C） contents are rare, in spite of the global distribution of grasslands in need of restoration. All these have a high potential for carbon sequestration, with a reduced carbon content due to overutilization. An exudate component that rapidly decomposes will increase soil respiration and CO2 emission, while a component that reduces decomposition of native soil carbon can reduce CO2 emission and actually help sequestering carbon in soil. Therefore, to investigate root exudate effects on rhizosphere activity, citric acid, glucose and oxalic acid（0.6 g C/kg dry soil） were added to soils from three biotopes（grassland, fixed dune and mobile dune） located in Naiman, Horqin Sandy Land, Inner Mongolia, China） and subjected to a 24-day incubation experiment together with a control. The soils were also analyzed for general soil properties. The results show that total respiration without exudate addition was highest in grassland soil, intermediate in fixed dune and lowest in mobile dune soil. However, the proportion of native soil carbon mineralized was highest in mobile dune soil, reflecting the low C/N ratio found there. The exudate effects on CO2-C emissions and other variables differed somewhat between biotopes, but total respiration（including that from the added substrates） was significantly increased in all combinations compared with the control, except for oxalic acid addition to mobile dune soil, which reduced CO2-C emissions from native soil carbon. A small but statistically significant increase in pH by the exudate additions in grassland and fixed dune soil was observed, but there was a major decrease from acid additions to mobile dune soil. In contrast, electrical conductivity decreased in grassland and fixed dune soil and increased in mobile dune. Thus, discrete components of root exudates affected soil environmental conditions differently, and responses to root exudates in soils with low carbon contents can differ from those in normal soils. The results indicate a potential for, e.g., acid root exudates to decrease decomposition rate of soil organic matter in low carbon soils, which is of interest for both soil restoration and carbon sequestration.

从知识困境到认知陷阱:生成式技术驱动型信息生态系统安全问题研究

生成式技术驱动型信息生态系统以生成式人工智能技术为核心,对整个信息环境中的知识传递与共享、认知流动与扩散过程发挥支撑和推动作用。然而,这种创新型的信息生态系统也伴随着知识安全和认知安全问题的出现。本文从知识环境和认知环境两个层面入手,对生成式技术驱动型信息生态系统的特点、优势与风险进行深入剖析,探索如何在符合人类价值观和社会伦理的前提下,充分发挥生成式人工智能的潜力,推动构建高效、安全、可持续发展的生成式技术驱动型信息生态系统。

人工智能赋能的居家社区养老服务发展及其生态系统优化策略研究

伴随人工智能技术在养老领域的应用,居家社区养老服务呈现协同演化的发展趋势。这既为人口老龄化下破解养老服务供给不足难题带来重大机遇,也给传统居家养老服务模式转型升级提出新的挑战。基于人工智能的技术特征,从“技术创新—要素整合—系统优化”过程维度揭示人工智能赋能的居家养老服务发展路径,并以生态系统理论为分析框架,从“宏观—中观—微观”层面剖析人工智能赋能居家社区养老服务面临规范性与引领性政策缺位、参与主体协作网络不紧密、养老服务供给和需求结构性失衡三个障碍。据此,应从宏观层面完善配套政策,加大对人工智能养老服务的支持力度;从中观层面推进数据汇聚与整合,促进多主体协同联动;从微观层面精准识别老年人需求,提升养老服务供给效率与能力。

人工混播草地土壤丛枝菌根真菌对轮牧的响应

为探究轮牧对人工混播草地生态系统中植被群落及土壤丛枝菌根(arbuscular mycorrhiza,AM)真菌群落的影响机制,以宁夏盐池县人工混播草地为试验对象,采用单因素随机区组设计,设置T1(无芒雀麦+新麦草+紫羊茅+苜蓿+菊苣)、T2(垂穗披碱草+新麦草+早熟禾+苜蓿+鹰嘴紫云英)、T3(扁穗冰草+新麦草+蒙古冰草+苜蓿)3种混播组合模式,并通过Illumina高通量测序和生物信息学分析,开展轮牧影响下3种人工混播草地土壤AM真菌群落差异性研究,分析人工混播草地生态系统中植被-土壤-AM真菌群落的相互作用关系。结果表明:连续2年轮牧对植被群落生物量产生了显著影响,与轮牧第1年相比,豆科植被群落的相对重要值降低,但禾本科植被群落的相对重要值分别增加了51.16%、81.25%和33.33%。土壤AM真菌中球囊霉属和类球囊霉属为优势属;与轮牧第1年相比,连续轮牧2年后,T1处理的土壤AM真菌群落Chao 1指数较第1年显著降低了12.35%,T3处理的土壤AM真菌群落Chao 1指数、香农-维纳指数、均匀度指数和物种数较第1年分别提升了20.73%、12.80%、7.69%和31.16%(P<0.05),说明T3处理的土壤AM真菌群落对轮牧的响应更加敏感。随轮牧年限增加,T1与T2处理的土壤AM真菌群落组成相似性增加,T1与T3处理的土壤AM真菌群落组成相似性差异较大。连续轮牧2年后,土壤养分对AM真菌群落丰富度的作用强度减弱,但植被群落多样性和植被群落生物量对AM真菌群落丰富度及其组成的作用强度增强;驱动AM真菌群落变化的环境因子由土壤有效磷(p=0.006)和碱解氮(p=0.016)转变为植被群落生物量(p=0.036)。综上所述,不同类型人工混播草地植被群落和土壤AM真菌群落对轮牧表现出不同的响应特征,其中以T3混播组合处理的效果较好。

“人G共生”范式:生成式人工智能视域下的教育转型图景

作为新一轮科技革命和产业变革的驱动力量,人工智能正深刻改变生产与生活方式,引领社会走向人机协同、跨界融合、共创分享的智能时代。生成式人工智能作为一种特定类型的人工智能,其创新发展正在引发链式突破,持续嵌入教育业务流程,推动教育教学领域的变革,深刻改变教育理念、教育空间、教学方式、教育管理模式等,创造新的教育场景和教育生态。以生成学习理论为视角,从动机、学习过程、生成过程和创造过程四个维度阐释人的学习是怎样发生的,为生成式人工智能赋能教学奠定理论基础;厘清“人G共生”范式中生成式人工智能在教学中扮演的角色和作用,包括针对性知识生成、差异化教学、量身定制的思维能力培养范式、多模态数据建构认知空间;构想生成式人工智能引领建构教育新生态的四大应用场景,即建构教育与技术的伙伴关系、助力跨学科学习、服务教育公平、智能迭代;总结提炼平等、协作、共享三大“人G共生”的关键原则,从而以一种更为开放、协作、创新的人类与AI关系范式,以及智能化的共生对象共同创生丰富、多元和智能的教育新生态。

城市生态防减灾中人工智能技术应用的进展与逻辑框架

在全球气候变化影响下,传统工程防灾难以解决自然灾害风险的不确定性问题。生态防减灾(Eco-DRR)作为国际韧性城市的前沿理论,对我国韧性城市发展有重要参考价值,人工智能(AI)技术可以很好地解决城市生态防减灾的不确定性问题,然而当前Eco-DRR理论研究与AI技术的实践应用存在断层。该文使用PRISMA2020框架调查城市Eco-DRR中AI技术应用的实践案例,通过质性研究总结城市Eco-DRR中AI技术的应用场景,从生态系统灾害调节服务评估、自然灾害风险评估、Eco-DRR方案决策以及Eco-DRR实施监测与后评估4个阶段剖析城市Eco-DRR中AI技术应用的典型案例。最后基于案例研究提出城市Eco-DRR领域AI技术应用的逻辑框架,总结其在应用过程中工具选择和数据准备的实践经验,探讨城市防灾中Eco-DRR的智能实现途径,为我国城市综合防灾和韧性城市建设提供借鉴。

美国推动人工智能产业发展策略及其对中国人工智能生态构建的启示

深度剖析美国在人工智能领域的发展轨迹和有效策略,对中国构建人工智能生态系统具有重要的指导意义。从机构设置、政策制定以及实践操作3个维度,采取政策解读与案例分析相结合的方法,全面审视美国联邦政府如何策划和促进人工智能的发展。对参与推动美国人工智能发展的美国联邦政府机构,根据其具体作用分为决策层、推进层和执行层:决策层包括总统科技顾问委员会、科技政策办公室和国家科学技术委员会,在美国人工智能战略布局中起到提出政策建议、决定政策方向和制定相关计划的科技决策作用;推进层主要包括美国国家科学基金会和联邦政府各职能机构,如能源部、商务部和国防部等,负责落实决策层的政策,启动一批推进项目;执行层则主要涉及美国国家实验室、顶尖高校实验室和优秀的科技企业。此外,美国通过持续更新国家人工智能研发规划以及实施“人工智能桥梁”等计划,推动人工智能的实践应用。结合中国人工智能发展的具体状况,从建立专业人工智能机构、打造人工智能研究中心、构建全方位多元化的人工智能人才培育体系等方面提出加速中国人工智能进展、夺取科技创新先机的策略建议。

增温与凋落物去除对人工草地土壤呼吸的影响

人工草地是重要的碳汇,但其土壤呼吸及其温度敏感性(Q10)对气候变化和干扰的响应尚不清楚。本研究在大陆性季风气候区的紫苜蓿(Medicago sativa)和无芒雀麦(Bromus inermis)2种人工草地开展了增温和凋落物处理试验,测量了土壤呼吸速率和Q10,并分析了不同草地对这些影响的响应差异。结果表明:增温使年均土壤温度显著增加约2℃(P<0.05);同时,使年均土壤湿度和电导率显著降低(P<0.05)。增温使年平均土壤呼吸速率降低8.81%;凋落物去除使年平均土壤呼吸速率降低9.33%。增温和凋落物去除均使Q10降低。不同草地对增温和凋落物处理有不同的响应,其中紫苜蓿草地对增温的响应大于无芒雀麦草地,而无芒雀麦草地对凋落物处理的响应大于紫苜蓿草地。本研究表明,试验区无芒雀麦群落相较于紫苜蓿群落更能抵抗气候变化和干扰的影响,有利于减少碳排放,是更好的建植人工草地的物种。

场景驱动型人工智能创新生态系统:逻辑与进路

以ChatGPT为代表的生成式人工智能所引发的新一代AI浪潮席卷全球,已然成为产业革命新机遇、国际竞争新焦点和新质生产力发展新引擎。如何把握数智化新机遇,加速推动AI技术创新和应用落地,成为国家竞争优势建构的关键。然而,现有研究多关注AI的治理及其使能价值,鲜有学者针对实践困境,探索适用于AI技术创新与应用的创新体系新模式。本文结合创新生态系统和场景驱动创新理论,扎根中国AI产业实践,论述场景驱动型人工智能创新生态系统的理论基础、架构和特征,提出场景驱动人工智能创新飞轮构建与生态培育的逻辑和进路,为培育中国特色、世界一流的AI创新生态,加速AI技术创新与应用、培育新质生产力的新动能,进而扎实推进高质量发展和中国式现代化提供有益的理论和政策启示。

生成式人工智能融入高等教育生态系统的风险表征、预警及化解

在生成式人工智能迅猛发展的背景下,认识、防范和化解生成式人工智能给高等教育生态系统带来的教育风险迫在眉睫。对此,文章首先剖析了生成式人工智能融入高等教育生态系统的风险表征,主要表现为师生主权消解、知识内容扭曲、安全包容破坏和人才培养异化。随后,文章构建了由风险预警主体、风险预警内容、风险预警保障、风险预警流程四个模块组成的生成式人工智能融入高等教育生态系统的风险预警机制。最后,文章提出从激发主体意识、鉴别知识内容、优化数字环境、培养创新人才等方面入手,来化解生成式人工智能融入高等教育生态系统的风险。文章的研究可为生成式人工智能教育风险治理提供理论指导,并推动生成式人工智能在高等教育中的安全应用,从而为高等教育数字化转型和高质量发展保驾护航。

深海环境海洋生态系统监测与修复新技术

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

人工智能赋能冰雪体育产业高质量发展研究

笔者采用文献资料和逻辑分析等方法,探究了人工智能赋能冰雪体育产业高质量发展的应用场景、现实困境与纾解路径。研究发现:人工智能赋能冰雪体育产业高质量发展能够在冰雪体育场馆、冰雪体育旅游、冰雪体育赛事、冰雪体育传媒、冰雪体育用品制造、冰雪教育培训等方面催生出新的应用场景。当前,人工智能赋能冰雪体育产业高质量发展存在着数据采集难和数据质量控制难、复合型人才稀缺和教育资源匮乏、技术应用成本高和技术标准阻滞等问题。基于此,笔者提出应当构建智能设备生态链、促进“政产学研”协同、研发开放式平台,形成与冰雪体育市场需求相匹配的人工智能应用场景,促进我国冰雪体育产业高质量发展的策略。

AI与IOT技术在协同商业生态系统中的影响机制——基于WSR方法的案例分析

通过使用扎根理论和“物理-事理-人理”系统方法论,构建基于技术驱动的协同商业生态模型,对Q智慧渔业公司进行案例研究,探索人工智能和物联网等高新技术对商业生态中价值创造过程的具体影响机制。研究发现,通过构建平台公司、养殖户、供应商和金融机构的商业生态系统实现商业价值的创造与分配,在此过程中人工智能和物联网技术被有效地整合为自我复制和扩展的系统,使得商业生态系统中的参与者能够依赖不断增长的商业数据发现并创造商业价值。由此认为,商业生态系统参与者之间的功能分配和利益分割合理,才能够吸引足够多的参与者,维持商业生态系统的可持续发展;人工智能的自演化机制能够极大降低商业生态系统价值共创成本。

当机器人也有体验时——人机共生体验的研究脉络、理论视角和未来研究

语音助理等智能机器的应用和普及,预示着人类已处于万物互联、实时交互的人机共生的数字生态系统中,这给所有参与者及其组合带来全新的体验,即人机共生体验(Human-machine Symbiosis Experience,简称HSX),是指在万物互联的人和机器共生的状态中,所有参与其中的各类主体即人、机器以及人和/或机器构成的各种组合实时交互后给这些主体带来的体验,这类体验具有实时性、涌现性、交互性和迭代性等特征。本文基于客户体验、人机共生的相关研究脉络、理论视角与机制研究的梳理,得出现有客户体验的理论视角包含消费者文化理论、价值共创理论和心流理论,已不足以涵盖HSX,且对HSX的形成机理和作用效能仍知之甚少,严重制约其作用的发挥。为此,本文基于人机共生相关客户体验理论即组合理论、涌现理论、螺旋理论,提出了人机共生体验的三个未来研究:①人机共生体验内涵及结构;②人机共生体验形成过程;③人机共生体验的作用机制。从而为人机共生体验理论在营销领域的发展提供未来研究方向。HSX蕴含的福利和经济价值随着“机器在与人以及彼此交互中拥有自己独特的能力和体验”而得到强化,在营造乐趣、启迪智慧、传播美感和转移关注基础上带来多元繁荣,为赋能经济发展、丰富人民生活作出贡献。