Spatiotemporal characteristics of seed rain and soil seed bank of artificial Caragana korshinskii Kom. forest in the Tengger Desert, China

Vegetation restoration and reconstruction are effective approaches to desertification control and achieving social and economic sustainability in desert areas.However,the self-succession ability of native plants during the later periods of vegetation restoration remains unclear.Therefore,this study was conducted to bridge the knowledge gap by investigating the regeneration dynamics of artificial forest under natural conditions.The information of seed rain and soil seed bank was collected and quantified from an artificial Caragana korshinskii Kom.forest in the Tengger Desert,China.The germination tests were conducted in a laboratory setting.The analysis of species quantity and diversity in seed rain and soil seed bank was conducted to assess the impact of different durations of sand fixation(60,40,and 20 a)on the progress of vegetation restoration and ecological conditions in artificial C.korshinskii forest.The results showed that the top three dominant plant species in seed rain were Echinops gmelinii Turcz.,Eragrostis minor Host.,and Agropyron mongolicum Keng.,and the top three dominant plant species in soil seed bank were E.minor,Chloris virgata Sw.,and E.gmelinii.As restoration period increased,the density of seed rain and soil seed bank increased first and then decreased.While for species richness,as restoration period increased,it gradually increased in seed rain but decreased in soil seed bank.There was a positive correlation between seed rain density and soil seed bank density among all the three restoration periods.The species similarity between seed rain or soil seed bank and aboveground vegetation decreased with the extension of restoration period.The shape of the seeds,specifically those with external appendages such as spines and crown hair,clearly had an effect on their dispersal,then resulting in lower seed density in soil seed bank.In addition,precipitation was a crucial factor in promoting rapid germination,also resulting in lower seed density in soil seed bank.Our findings provide valuable insights for guiding future interventions during the later periods of artificial C.korshinskii forest,such as sowing and restoration efforts using unmanned aerial vehicles.

An artificial intelligence diabetes management architecture based on 5G

Along with the development of 5G network and Internet of Things technologies,there has been an explosion in personalized healthcare systems.When the 5G and Artificial Intelligence(Al)is introduced into diabetes management architecture,it can increase the efficiency of existing systems and complications of diabetes can be handled more effectively by taking advantage of 5G.In this article,we propose a 5G-based Artificial Intelligence Diabetes Management architecture(AIDM),which can help physicians and patients to manage both acute complications and chronic complications.The AIDM contains five layers:the sensing layer,the transmission layer,the storage layer,the computing layer,and the application layer.We build a test bed for the transmission and application layers.Specifically,we apply a delay-aware RA optimization based on a double-queue model to improve access efficiency in smart hospital wards in the transmission layer.In application layer,we build a prediction model using a deep forest algorithm.Results on real-world data show that our AIDM can enhance the efficiency of diabetes management and improve the screening rate of diabetes as well.

Diversity of soil bacteria and fungi communities in artificial forests of the sandy-hilly region of Northwest China

Soil erosion is a serious issue in the sandy-hilly region of Shanxi Province,Northwest China.There has been gradual improvement due to vegetation restoration,but soil microbial community characteristics in different vegetation plantation types have not been widely investigated.To address this,we analyzed soil bacterial and fungal community structures,diversity,and microbial and soil environmental factors in Caragana korshinskii Kom.,Populus tomentosa Carr.,Populus simonii Carr.,Salix matsudana Koidz,and Pinus tabulaeformis Carr.forests.There were no significant differences in the dominant bacterial community compositions among the five forest types.The alpha diversity of the bacteria and fungi communities showed that ACE(abundance-based coverage estimator),Chao1,and Shannon indices in C.korshinskii forest were significantly higher than those in the other four forest types(P<0.05).Soil organic matter,total nitrogen,and urease had a greater impact on bacterial community composition,while total nitrogen,β-glucosidase,and urease had a greater impact on fungal community composition.The relative abundance of beneficial and pathogenic microorganisms was similar across all forest types.Based on microbial community composition,diversity,and soil fertility,we ranked the plantations from most to least suitable as follows:C.korshinskii,S.matsudana,P.tabulaeformis,P.tomentosa,and P.simonii.

Influence of forest road on breeding of tits in artificial nest boxes

A study was conducted to determine the influence of forest road on breeding of tits in artificial nest boxes in deciduous, coniferous and mixed forests in the Gwanak Arboretum （37° 25′ 05＂ N, 126° 56′ 85＂ E） of Seoul National University, Anyang, Korea from November 2002 to June 2003. Three tits species, varied tit （Parus varius）, marsh tit （P. palustris） and great tit （P. major）, breeding in artificial t nest boxes were investigated on number of breeding pairs, cultch size, and egg measurement. Resuls showed that the breeding pairs of varied tit was more in 75-150 m area than in 0-75m area from forest road for all the three study sites, and the clutch size and egg measurements （weight, Major axis and Minor axis） of varied tit was also higher in the area of 75-150 m than in the area of 0-75 m, while no differences in number of breeding pairs and clutch size were found for marsh tit and great tit between the two areas. Egg measurement of great tit was also higher in forest interior area than in forest edge area. It is concluded that varied tit were most significantly influenced by forest road, followed by great tit, whereas marsh tit were not influenced by forest road. Artificial nest box is roved to be good for cavity nester in disturbed areas by human activities. Supply of artificial nest can help population protection and management of bird species.

Stem radial growth indicate the options of species,topography and stand management for artificial forests in the western Loess Plateau, China

An understanding of the differences in artificial forest between tree species,slope aspects,and management options in arid environments is fundamentally important for efficient management of these artificial systems;however,few studies have quantified the spatial and temporal differences in stem radial growth of trees in the arid western Loess Plateau of China.Using dendrochronology,we assessed the growth of three woody species(the native shrub Reaumuria soongorica,the exotic shrub Tamarix ramosissima and tree Platycladus orientalis)by measuring the annual stem radial increment.We also describe the long-term growth trends and responses to climatic factors on slopes with different aspects during periods with and without irrigation.We found that precipitation during the main growing season was significantly positively correlated with ring growth for all three species and both slope aspects.In addition,supplemental water(e.g.,irrigation,rainwater harvesting)greatly relieved drought stress and promoted radial growth.Our results suggest that as the main afforestation species in the Loess Plateau used for soil and water conservation,P.orientalis is more suitable than T.ramosissima under rain-fed conditions.However,a landscape that combined a tree(P.orientalis)with a shrub(R.soongorica)and grassland appears likely to represent the best means of ecological restoration in the arid western Loess Plateau.

Evaluation of Broadleaf Tree Diversity at the Basin Scale—In Case of Artificial <i>Chamaecyparis obtusa</i>Forests

In recent years, the various functions required of forests, especially the conservation of biodiversity, have been attracting increasing attention in Japan and worldwide. In Japan, 67% of national land is covered by forest, 41% of which is artificial forest (i.e., plantations). Therefore, forest biodiversity conservation efforts should also target artificial forests. In this paper, we seek to promote sustainable forest management that considers biodiversity conservation by examining indices that can be used by forest managers to evaluate the diversity of broadleaf trees. The result was that evaluation of broadleaf tree diversity in artificial forests at a basin scale was possible by combining several types of indicators.

Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.

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.

Formation mechanisms and remediation techniques for low-efficiency artificial shelter forests on the Chinese Loess Plateau

The construction of artificial shelter forests(ASFs)has resulted in substantial ecological,economic,and societal benefits to the Chinese Loess Plateau(CLP).However,the health and benefits of ASFs are being increasingly threatened by the formation of low-efficiency artificial shelter forests(LEASFs).In this study,LEASFs are systematically analyzed in terms of their status,formation mechanisms,and developmental obstacles.The key restoration techniques and schemes were summarized to improve the quality and efficiency of LEASFs.LEASFs are formed by relatively complex mechanisms,but they arise mainly due to poor habitat conditions,improper tree species selections,mismatch between stands and habitat,extensive forest management measures,and human interferences.The restoration and improvement of LEASFs are hindered by water deficits,mismatch between stands and habitat,single management purpose,and low efficiency.LEASFs are becoming more complex due to their wide range,the challenges associated with their restoration,and insufficient technological measures for their restoration.The key techniques of the quality and efficiency improvement of LEASFs include basic forest tending methods,near-natural restoration,multifunction-oriented improvement,and systematic restoration.An understanding on the formation mechanisms of LEASFs and a scientific approach toward their restoration are urgently needed and critical for the ecological protection and high-quality development of LEASFs on the CLP.Based on these analyses,we recommend strengthening the monitoring and supervision of LEASFs,considering the bearing capacity of regional water resources,implementing multiple restoration techniques,promoting multifunction-oriented ecological development,and exploring new management concepts to achieve the sustainable development of ASFs on the CLP.

Study on the Biomass Measuration for the Artificial Forest of <i>Calotropis gigantea</i>(L.) <i>Dryand.</i>

Based on artificial forest of Calotropis gigantea (L.) Dryand. area of 20.1 hm2, planted in the base of seed management station in Yuanjiang County, Yunnan Province, according to the average standard wooden method, the stand biomass was calculated in this paper. Based on the survey data of representative sample trees, the single-tree biomass model was constructed.

An Evaluation Model for Improving Biodiversity in Artificial Coniferous Forests Invaded by Broadleaf Trees

Increasing attention is being paid to the various functions of forests, especially the conservation of biodiversity. In Japan, 67% of national land is covered by forest, 41% of which is artificial forest (i.e., plantations). Therefore, efforts to conserve forest biodiversity should also target artificial forests. In this study, we investigated the increase in biodiversity resulting from broadleaf tree invasion of artificial coniferous forests. We examined diversity indices and combinations of indices to identify which ones can aid forest managers in evaluating forest diversity. We also studied classification according to the richness of diversity, which corresponded to the growth stages of Chamaecyparisobtusa and Cryptomeria japonica plantation forests. Moreover, we developed a model that will contribute to sustainable forest management and biodiversity over an entire area. The model, based on a specific rotation scenario in a geographic information system, is easy to use and presents spatial and temporal changes at sites visually.

Application of remote sensing,an artificial neural network leaf area model,and a process-based simulation model to estimate carbon storage in Florida slash pine plantations.

Carbon sequestration in forests is of great interest due to concerns about global climate change.Carbon storage rates depend on ecosystem fluxes（photosynthesis and ecosystem respiration）,typically quantified as net ecosystem exchange（NEE）.Methods to estimate forest NEE without intensive site sampling are needed to accurately assess rates of carbon sequestration at stand-level and larger scales.We produced spatially-explicit estimates of NEE for 9 770 ha of slash pine（Pinus elliottii） plantations in North-Central Florida for a single year by coupling remote sensing-based estimates of leaf area index（LAI） with a process-based growth simulation model.LAI estimates produced from a neural-network modeling of ground plot and Landsat TM satellite data had a mean of 1.06（range 0-3.93,including forest edges）.Using the neural network LAI values as inputs,the slash pine simulation model（SPM2） estimates of NEE ranged from-5.52 to 11.06 Mg·ha^-1·a^-1with a mean of 3.47 Mg·ha^-1·a^-1Total carbon storage for the year was 33920 t,or about 3.5 tons per hectare.Both estimated LAI and NEE were highly sensitive to fertilization.

Artificial intelligence and liver transplantation:Looking for the best donor-recipient pairing

Decision-making based on artificial intelligence(AI)methodology is increasingly present in all areas of modern medicine.In recent years,models based on deep-learning have begun to be used in organ transplantation.Taking into account the huge number of factors and variables involved in donor-recipient(DR)matching,AI models may be well suited to improve organ allocation.AI-based models should provide two solutions:complement decision-making with current metrics based on logistic regression and improve their predictability.Hundreds of classifiers could be used to address this problem.However,not all of them are really useful for D-R pairing.Basically,in the decision to assign a given donor to a candidate in waiting list,a multitude of variables are handled,including donor,recipient,logistic and perioperative variables.Of these last two,some of them can be inferred indirectly from the team’s previous experience.Two groups of AI models have been used in the D-R matching:artificial neural networks(ANN)and random forest(RF).The former mimics the functional architecture of neurons,with input layers and output layers.The algorithms can be uni-or multi-objective.In general,ANNs can be used with large databases,where their generalizability is improved.However,they are models that are very sensitive to the quality of the databases and,in essence,they are black-box models in which all variables are important.Unfortunately,these models do not allow to know safely the weight of each variable.On the other hand,RF builds decision trees and works well with small cohorts.In addition,they can select top variables as with logistic regression.However,they are not useful with large databases,due to the extreme number of decision trees that they would generate,making them impractical.Both ANN and RF allow a successful donor allocation in over 80%of D-R pairing,a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease,balance of risk score,and survival outcomes following liver transplantation scores.Many barriers need to be overcome before these deeplearning-based models can be included for D-R matching.The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.

RELATIONSHIP OF ECO-ENVIONMENTAL CHANGE WITH NATURAL EROSION AND ARTIFICIALLY ACCELERATED EROSION

The natural landscape of the Loess Plateau was changed by severe soil erosion. The Ziwuling forest area provides research base for tracing back eco environmental change related to natural erosion and artificially accelerated erosion. Using methods of typical region investigations, in situ experimental study and chemical analysis of samples, impact of vegetation destruction and rehabilitation on soil erosion, characteristics of natutal erosion under conditions of natural ecological balance and artificially accelerated erosion resulting from vegetation destruction in forest area, and the processes of artificially accelerated erosion and soil degradation have been analyzed and discussed.

The use of artificial nest boxes by Siberian flying squirrels (Pteromys volans) in South Korea

In this study, we examined the use of artificial nest boxes by Siberian flying squirrels (Pteromys volans) in three coniferous and mixed forests in Gangwon Province, South Korea. Six hundred and twelve boxes with different sized entry holes (ranging from 3 to 7 cm in diameter) were placed in the forests between 2004 and 2009. Pteromys volans used nine boxes in the coniferous forests and two boxes in the mixed forests. The squirrels only used boxes with entrance holes measuring 3.5, 4, and 5 cm in diameter, showing a strong and moderate preference for boxes with 5 and 4-cm holes, respectively, and a strong avoidance for boxes with 3- and 7-cm holes. Therefore, we suggest placing artificial nest boxes with entrance holes 5 cm in diameter to encourage breeding activity. Most nests made in the artificial boxes were composed of fibrous materials from woody vines. We recommend placing artificial nest boxes with holes of 5-cm diameter in coniferous forests, which support dense populations of P. volans, to survey whether this approach would positively affect the breeding habits and population maintenance of this species.

Explainable Artificial Intelligence Solution for Online Retail

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关于“气候智慧林业”研究的思考

气候智慧林业于2020年之后兴起,是集智慧林业、气候林业和生态林业于一体的协同发展新组合。气候智慧林业是利用智能化和数字驱动的新一代信息与人工智能(AI)技术,通过改变传统的林业科学科研与管理范式,更好地理解森林生态系统与气候变化相互影响的机制;在此基础上,提出基于自然的气候解决方案,实现森林生态系统功能的高效、稳定、可持续目标,最终达到人与自然和谐发展。与传统林业和生态林业相似,气候智慧林业也需要强大的科学研究基础。气候智慧林业研究需要回答的核心科学问题是森林生态系统与气候变化相互影响的机制,其研究方向包括:1)森林生态系统碳汇的监测、计量与核算,以及碳汇对减缓气候变化的时间效应;2)森林生态系统韧性(抵抗力、恢复力、适应力)的形成及其对气候变化和自然干扰的响应机制;3)森林生态系统碳汇的形成、维持与提升机制,包括管理措施和木材生产利用对碳汇的影响、碳汇与其他功能之间的平衡;4)人工智能和多源遥感驱动的森林智能数据采集系统;5)基于信息化+AI和专家决策的气候智慧林业管理系统。加强上述研究,有利于增强森林生态系统韧性,以更好适应气候变化或减缓气候变化的负面影响,提升森林缓解气候变化的能力,为增强森林的多种生态功能与林产品供给能力提供科技支撑。

亚热带次生常绿阔叶林植物功能性状及多样性对人工抚育的响应

亚热带次生常绿阔叶林普遍存在生长滞缓、固碳功能和经济价值低下等问题,迫切需要通过人工抚育提高其生态系统功能。对植物功能性状及其多样性的深入研究,能够找寻植物共存与群落稳定发展的内在机制,为提升森林生态系统功能的实践提供基础。为了探究人工抚育对亚热带次生常绿阔叶林质量提升的影响,以杭州午潮山国家森林公园内的次生常绿阔叶林为研究对象,实施了不同强度的抚育措施,通过群落植被复查,分析群落植物功能性状以及功能多样性对不同强度人工抚育的响应。结果发现:抚育2年后,群落最大潜在树高与叶片氮、磷、钾含量显著上升,而叶片干物质含量和叶片叶绿素含量均有显著下降,比叶面积有下降趋势,两种强度的抚育之间差异不显著;叶片干物质含量、叶绿素含量与叶片氮含量间的相关性由不显著变为了显著负相关,而叶片干物质含量与叶片叶绿素含量,叶片磷、钾含量与叶片氮含量,叶片磷含量与叶片钾含量间的相关性由不显著变为显著正相关;在20%强度抚育下,功能丰富度指数均值从对照的0.52显著下降为0.16,功能均匀度指数与功能分离度指数的均值则分别上升到0.61和0.67,显著高于对照组的0.51和0.56;在10%强度抚育下,功能丰富度有所下降,功能均匀度与功能分离度均有所上升,但差异不显著。研究表明,人工抚育在一定程度上改变了群落水平的植物功能性状与功能多样性,提高了群落植物利用资源的能力和效率;而适度的抚育强度更能解放资源空间,对群落产生的影响更加明显。该结果为亚热带次生常绿阔叶林的经营与管理提供依据与参考。

人工智能在森林生态补偿政策评估与优化中的应用

该文通过研究森林生态补偿政策的评估与优化,探讨人工智能技术在该领域的应用及其作用。首先,综述森林生态补偿政策评估与优化的研究现状,指出现有研究的不足之处。接着,阐述人工智能技术在森林生态补偿政策评估与优化领域的应用,如数据采集、数据分析、预测模型构建等。然后,构建一个基于人工智能的森林生态补偿政策评估与优化模型,该模型能够更准确地评估政策效果,优化政策制定过程。最后,结合实际案例,分析人工智能在森林生态补偿政策评估与优化中的应用实践,证明人工智能技术在提升政策效果、促进森林资源可持续利用方面的积极作用。