Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threat...With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.展开更多
Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satell...Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.展开更多
The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We r...The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.展开更多
Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition wi...Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.展开更多
Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the dis...Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the distribution areas of Moso bamboo(Phyllostachys edulis)in China to explore the effects of bamboo invasion on forest structural attributes and diameter–height allometries by comparing paired plots of bamboo,mixed bamboo-tree,and non-bamboo forests along the transects.We found that bamboo invasion decreased the mean and maximum diameter at breast height,maximum height,and total basal area,but increased the mean height,stem density,and scaling exponent for stands.Bamboo also had a higher scaling exponent than tree,particularly in mixed forests,suggesting a greater allocation of biomass to height growth.As invasion intensity increased,bamboo allometry became more plastic and decreased significantly,whereas tree allometry was indirectly promoted by increasing stem density.Additionally,a humid climate may favour the scaling exponents for both bamboo and tree,with only minor contributions from topsoil moisture and nitrogen content.The inherent superiority of diameter–height allometry allows bamboo to outcompete tree and contributes to its invasive success.Our findings provide a theoretical basis for understanding the causes and consequences of bamboo invasion.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.展开更多
Background:The forest musk deer,a rare fauna species found in China,is famous for its musk secretion which is used in selected Traditional Chinese medicines.However,over-hunting has led to musk deer becoming an endang...Background:The forest musk deer,a rare fauna species found in China,is famous for its musk secretion which is used in selected Traditional Chinese medicines.However,over-hunting has led to musk deer becoming an endangered species,and their survival is also greatly challenged by various high incidence and high mortality respiratory and intestinal diseases such as septic pneumonia and enteritis.Accumulating evidence has demonstrated that Akkermannia muciniphila(AKK)is a promising probiotic,and we wondered whether AKK could be used as a food additive in animal breeding pro-grammes to help prevent intestinal diseases.Methods:We isolated one AKK strain from musk deer feces(AKK-D)using an im-proved enrichment medium combined with real-time PCR.After confirmation by 16S rRNA gene sequencing,a series of in vitro tests was conducted to evaluate the probiotic effects of AKK-D by assessing its reproductive capability,simulated gas-trointestinal fluid tolerance,acid and bile salt resistance,self-aggregation ability,hy-drophobicity,antibiotic sensitivity,hemolysis,harmful metabolite production,biofilm formation ability,and bacterial adhesion to gastrointestinal mucosa.Results:The AKK-D strain has a probiotic function similar to that of the standard strain in humans(AKK-H).An in vivo study found that AKK-D significantly amelio-rated symptoms in the enterotoxigenic Escherichia coli(ETEC)-induced murine diar-rhea model.AKK-D improved organ damage,inhibited inflammatory responses,and improved intestinal barrier permeability.Additionally,AKK-D promoted the reconsti-tution and maintenance of the homeostasis of gut microflora,as indicated by the fact that AKK-D-treated mice showed a decrease in Bacteroidetes and an increase in the proportion of other beneficial bacteria like Muribaculaceae,Muribaculum,and unclas-sified f_Lachnospiaceae compared with the diarrhea model mice.Conclusion:Taken together,our data show that this novel AKK-D strain might be a potential probiotic for use in musk deer breeding,although further extensive system-atic research is still needed.展开更多
The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and...The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.展开更多
The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A da...The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ec...The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ecosystem service value of the Northeast Forest Belt from 2005 to 2020 was assessed,focusing on spatial–temporal changes and the driving forces behind these dynamics.Using multi-source data,the equivalent factor method,and geo-graphic detectors,we analyzed natural and socio-economic factors affecting the region.which was crucial for effective ecological conservation and land-use planning.Enhanced the effectiveness of policy formulation and land use plan-ning.The results show that the ESV of the Northeast Forest Belt exhibits an overall increasing trend from 2005 to 2020,with forests and wetlands contributing the most.However,there are significant differences between forest belts.Driven by natural and socio-economic factors,the ESV of forest belts in Heilongjiang and Jilin provinces showed significant growth.In contrast,the ESV of Forest Belts in Liaoning and Inner Mongolia of China remains relatively stable,but the spatial differentiation within these regions is characterized by significant clustering of high-value and low-value areas.Furthermore,climate regulation and hydrological regulation services were identified as the most important ecological functions in the Northeast Forest Belt,contributing greatly to regional ecological stability and human well-being.The ESV in the Northeast Forest Belt is improved during the study period,but the stability of the ecosystem is still chal-lenged by the dual impacts of natural and socio-economic factors.To further optimize regional land use planning and ecological protection policies,it is recommended to prior-itize the conservation of high-ESV areas,enhance ecological restoration efforts for wetlands and forests,and reasonably control the spatial layout of urban expansion and agricul-tural development.Additionally,this study highlights the importance of tailored ecological compensation policies and strategic land-use planning to balance environmental protec-tion and economic growth.展开更多
The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection h...The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles.To unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.展开更多
Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability ...Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of forests.At the same time,detailed accounts of disturbances and forest responses are not yet well quantified in Asia.This study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast Asia.This approach allowed us to detect forest disturbance and quantify the resilience after disturbance.Results showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by disturbances.Specifically,95%of forests had robust resilience and could recover from disturbance within a few decades.The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude.In summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades.The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022.The findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.展开更多
The Turan number of a graph H,denoted by ex(n,H),is the maximum number of edges in any graph on n vertices containing no H as a subgraph.Let P_(ι)denote the path onιvertices,S_(ι-1)denote the star onιvertices and ...The Turan number of a graph H,denoted by ex(n,H),is the maximum number of edges in any graph on n vertices containing no H as a subgraph.Let P_(ι)denote the path onιvertices,S_(ι-1)denote the star onιvertices and k_(1)P_(ι)∪k_(2)S_(ι-1)denote the path-star forest with disjoint union of k_(1)copies of P_(ι)and k_(2)copies of S_(ι-1).In 2022,[Graphs Combin.,2022,38(3):Paper No.84,16 pp.] raised a conjecture about the Turan number of k_(1)P_(2ι)∪k_(2)S_(2ι-1).In this paper,we determine the Turan numbers of P_(ι)∪kS_(ι-1)and k_(1)P_(2ι)∪k_(2)S_(2ι-1)for n appropriately large,which implies the above conjecture.The corresponding extremal graphs are also completely characterized.展开更多
Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distribu...Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.展开更多
Background: Continuous Cover Forestry(CCF) is a type of forest management that is based on ecological, environmental, and biological principles. Specific definitions of CCF greatly vary and the concept usually include...Background: Continuous Cover Forestry(CCF) is a type of forest management that is based on ecological, environmental, and biological principles. Specific definitions of CCF greatly vary and the concept usually includes a number of tenets or criteria. The most important tenet of CCF is the requirement to abandon the practice of largescale clearfelling in favour of selective thinning/harvesting and natural regeneration methods.Methods: CCF is commonly believed to have its main origin in an academic debate that was conducted through publications in a number of European and North American countries towards the end of the 19th and the beginning of the 20th century. Our findings are exclusively based on a literature review of the history of CCF and they revealed that the European origins of CCF go much further back to a form of farm forestry that started to be practised in Central Europe in the 17th century. Eventually, this type of farm forestry led to the formation of the single-tree selection system as we know it today. Another influential tradition line contributing to modern CCF is individual-based forest management, which breaks forest stands down into small neighbourhood-based units. The centres of these units are dominant frame trees which form the framework of a forest stand. Consequently, management is only carried out in the local neighbourhood of frame trees. Individual-based forest management also modified inflexible area-control approaches of plantation forest management in favour of the flexible sizecontrol method.Results and conclusions: We found evidence that the three aforementioned tradition lines are equally important and much interacted in shaping modern CCF. Since CCF is an international accomplishment, it is helpful to thoroughly study the drivers and causes of such concepts. Understanding the gradual evolution can give valuable clues for the introduction and adaptation of CCF in countries where the concept is new.展开更多
Background:The heartwood(HW)proportion in the trunk of mature trees is an important characteristic not only for wood quality but also for assessing the role of forests in carbon sequestration.We have for the first tim...Background:The heartwood(HW)proportion in the trunk of mature trees is an important characteristic not only for wood quality but also for assessing the role of forests in carbon sequestration.We have for the first time studied the proportion of HW in the trunk and the distribution of carbon and extractives in sapwood(SW)and HW of 70–80 year old Pinus sylvestris L.trees under different growing conditions in the pine forests of North-West Russia.Method:We have examined the influence of conditions and tree position in stand(dominant,intermediate and suppressed trees)in the ecological series:blueberry pine forest(Blu)–lingonberry pine forest(Lin)–lichen pine forest(Lic).We have analyzed the influence of climate conditions in the biogeographical series of Lin:the middle taiga subzone–the northern taiga subzone–the transition area of the northern taiga subzone and tundra.Results:We found that the carbon concentration in HW was 1.6%–3.4%higher than in SW,and the difference depended on growing conditions.Carbon concentration in HW increased with a decrease in stand productivity(Blu-Lin-Lic).In medium-productive stands,the carbon concentration in SW was higher in intermediate and supressed trees compared to dominant trees.In the series from south to north,carbon concentration in HW increased by up to 2%,while in SW,it rose by 2.7%–3.8%.Conclusions:Our results once again emphasized the need for an empirical assessment of the accurate carbon content in aboveground wood biomass,including various forest growing conditions,to better understand the role of boreal forests in carbon storage.展开更多
The woody vegetation is an important plant community constituent of tropical inselbergs,yet it remains largely overlooked.These environments of high socio-cultural and ecological value face pressures in many places,ma...The woody vegetation is an important plant community constituent of tropical inselbergs,yet it remains largely overlooked.These environments of high socio-cultural and ecological value face pressures in many places,mainly related to mining exploitation and fires.This study provides the first systematic overview of inselberg woody vegetation in the Brazilian Atlantic Forest.We used four inselbergs as models to characterize the composition and structure of the woody vegetation.In addition,the biomass and carbon storage were estimated using the general equations for tropical regions and carbon concentration values.Ten transects(50 m×2 m)were systematically installed on each inselberg,and all woody plants with a diameter at breast height≥5 cm were measured,registered,and identified.A total of 312 individuals belonging to 26 species,23 genera,and 14 families were found.The Fabaceae family and the genus Eugenia(Myrtaceae)exhibited a higher richness.The woody community's diameters ranged from 5.0 to 116.9 cm(with a mean of 23.9 cm),and heights ranged from 1.7 to 16.0 m(with a mean of 6.2 m).All specialist lithophyte woody species found on inselbergs are wind-dispersed.Among the endemic species of the Atlantic Forest,four were endemic to inselbergs,with Pseudobombax petropolitanum and Wunderlichia azulensis being threatened.A few species dominated the communities:P.petropolitanum,Guapira opposita,Amburana cearensis,and Tabebuia reticulata.Carbon accumulated in aboveground biomass ranged from 14 to 48 Mg ha-1,indicating variability in woody vegetation structure and growing conditions among inselbergs.Lastly,we highlight target species for potential use for inselberg vegetation restoration in stone mining areas in Atlantic Forest.展开更多
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by National Key Research and Development Program of China(No.2021YFD2200405(S.R.L.))Natural Science Foundation of China(Grant No.31971653).
文摘With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.
文摘Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.
基金National Key Research and Development Program of China,No.2023YFE0208100,No.2021YFC3000201Natural Science Foundation of Henan Province,No.232300420165。
文摘The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.
基金supported by Ningbo Natural Science Foundation(No.2023J059)Ningbo Commonweal Programme Key Project(No.2023S038)Guangxi Key Research and Development Programme(No.GuikeAB21220063).
文摘Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.
基金supported by the National Natural Science Foundation of China(No.31988102)Yunnan Province Major Program for Basic Research Project(No.202101BC070002)+1 种基金Yunnan Province Science and Technology Talents and Platform Program(No.202305AA160014)Yunnan Province Key Research and Development Program of China(No.202303AC100009)。
文摘Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the distribution areas of Moso bamboo(Phyllostachys edulis)in China to explore the effects of bamboo invasion on forest structural attributes and diameter–height allometries by comparing paired plots of bamboo,mixed bamboo-tree,and non-bamboo forests along the transects.We found that bamboo invasion decreased the mean and maximum diameter at breast height,maximum height,and total basal area,but increased the mean height,stem density,and scaling exponent for stands.Bamboo also had a higher scaling exponent than tree,particularly in mixed forests,suggesting a greater allocation of biomass to height growth.As invasion intensity increased,bamboo allometry became more plastic and decreased significantly,whereas tree allometry was indirectly promoted by increasing stem density.Additionally,a humid climate may favour the scaling exponents for both bamboo and tree,with only minor contributions from topsoil moisture and nitrogen content.The inherent superiority of diameter–height allometry allows bamboo to outcompete tree and contributes to its invasive success.Our findings provide a theoretical basis for understanding the causes and consequences of bamboo invasion.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
基金This research was funded by Shaanxi Administration of Traditional Chinese Medicine Projects(2021-QYZL-01,2021-QYPT-001)Key R&D Project in Shaanxi Province(2023-YBSF-463)+1 种基金the Foundation of Science and Technology in Shaanxi Province(2020TD-050)the Fundamental Research Funds for the Central Universities(GK202205010).
文摘Background:The forest musk deer,a rare fauna species found in China,is famous for its musk secretion which is used in selected Traditional Chinese medicines.However,over-hunting has led to musk deer becoming an endangered species,and their survival is also greatly challenged by various high incidence and high mortality respiratory and intestinal diseases such as septic pneumonia and enteritis.Accumulating evidence has demonstrated that Akkermannia muciniphila(AKK)is a promising probiotic,and we wondered whether AKK could be used as a food additive in animal breeding pro-grammes to help prevent intestinal diseases.Methods:We isolated one AKK strain from musk deer feces(AKK-D)using an im-proved enrichment medium combined with real-time PCR.After confirmation by 16S rRNA gene sequencing,a series of in vitro tests was conducted to evaluate the probiotic effects of AKK-D by assessing its reproductive capability,simulated gas-trointestinal fluid tolerance,acid and bile salt resistance,self-aggregation ability,hy-drophobicity,antibiotic sensitivity,hemolysis,harmful metabolite production,biofilm formation ability,and bacterial adhesion to gastrointestinal mucosa.Results:The AKK-D strain has a probiotic function similar to that of the standard strain in humans(AKK-H).An in vivo study found that AKK-D significantly amelio-rated symptoms in the enterotoxigenic Escherichia coli(ETEC)-induced murine diar-rhea model.AKK-D improved organ damage,inhibited inflammatory responses,and improved intestinal barrier permeability.Additionally,AKK-D promoted the reconsti-tution and maintenance of the homeostasis of gut microflora,as indicated by the fact that AKK-D-treated mice showed a decrease in Bacteroidetes and an increase in the proportion of other beneficial bacteria like Muribaculaceae,Muribaculum,and unclas-sified f_Lachnospiaceae compared with the diarrhea model mice.Conclusion:Taken together,our data show that this novel AKK-D strain might be a potential probiotic for use in musk deer breeding,although further extensive system-atic research is still needed.
基金Under the auspices of National Natural Science Foundation of China(No.42201374,42071359)。
文摘The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.
基金supported by the Basic Ability Improvement Project of Young and Middle-Aged Teachers in Colleges and Universities of Guangxi(2022KY1922,2021KY1938).
文摘The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金funded by the Central University D Project(HFW230600022)National Natural Science Foundation of China(71973021)+1 种基金National Natural Science Foundation Youth Funding Project(72003022)Heilongjiang Province University Think Tank Open Topic(ZKKF2022173).
文摘The scientific assessment of ecosystem ser-vice value(ESV)plays a critical role in regional ecologi-cal protection and management,rational land use planning,and the establishment of ecological security barriers.The ecosystem service value of the Northeast Forest Belt from 2005 to 2020 was assessed,focusing on spatial–temporal changes and the driving forces behind these dynamics.Using multi-source data,the equivalent factor method,and geo-graphic detectors,we analyzed natural and socio-economic factors affecting the region.which was crucial for effective ecological conservation and land-use planning.Enhanced the effectiveness of policy formulation and land use plan-ning.The results show that the ESV of the Northeast Forest Belt exhibits an overall increasing trend from 2005 to 2020,with forests and wetlands contributing the most.However,there are significant differences between forest belts.Driven by natural and socio-economic factors,the ESV of forest belts in Heilongjiang and Jilin provinces showed significant growth.In contrast,the ESV of Forest Belts in Liaoning and Inner Mongolia of China remains relatively stable,but the spatial differentiation within these regions is characterized by significant clustering of high-value and low-value areas.Furthermore,climate regulation and hydrological regulation services were identified as the most important ecological functions in the Northeast Forest Belt,contributing greatly to regional ecological stability and human well-being.The ESV in the Northeast Forest Belt is improved during the study period,but the stability of the ecosystem is still chal-lenged by the dual impacts of natural and socio-economic factors.To further optimize regional land use planning and ecological protection policies,it is recommended to prior-itize the conservation of high-ESV areas,enhance ecological restoration efforts for wetlands and forests,and reasonably control the spatial layout of urban expansion and agricul-tural development.Additionally,this study highlights the importance of tailored ecological compensation policies and strategic land-use planning to balance environmental protec-tion and economic growth.
基金Funds for the Central Universities(grant number CUC24SG018).
文摘The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles.To unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.
基金jointly supported by the National Natural Science Foundation of China [grant number 42265012]the Funding by the Fengyun Application Pioneering Project [grant number FY-APP-ZX-2022.0221]。
文摘Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of forests.At the same time,detailed accounts of disturbances and forest responses are not yet well quantified in Asia.This study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast Asia.This approach allowed us to detect forest disturbance and quantify the resilience after disturbance.Results showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by disturbances.Specifically,95%of forests had robust resilience and could recover from disturbance within a few decades.The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude.In summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades.The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022.The findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.
文摘The Turan number of a graph H,denoted by ex(n,H),is the maximum number of edges in any graph on n vertices containing no H as a subgraph.Let P_(ι)denote the path onιvertices,S_(ι-1)denote the star onιvertices and k_(1)P_(ι)∪k_(2)S_(ι-1)denote the path-star forest with disjoint union of k_(1)copies of P_(ι)and k_(2)copies of S_(ι-1).In 2022,[Graphs Combin.,2022,38(3):Paper No.84,16 pp.] raised a conjecture about the Turan number of k_(1)P_(2ι)∪k_(2)S_(2ι-1).In this paper,we determine the Turan numbers of P_(ι)∪kS_(ι-1)and k_(1)P_(2ι)∪k_(2)S_(2ι-1)for n appropriately large,which implies the above conjecture.The corresponding extremal graphs are also completely characterized.
基金supported by the Spanish Ministry of Science and Innovation project GREEN-RISK(Evaluation of past changes in ecosystem services and biodiversity in forests and restoration priorities under global change impacts-PID2020-119933RB-C21)A.C.received a pre-doctoral fellowship funded by the Spanish Ministry of Science and Innovation(PRE2021-099642).
文摘Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.
基金supported by the Swedish Government Research Council for Sustainable Development(Formas)grant#2023-00994.
文摘Background: Continuous Cover Forestry(CCF) is a type of forest management that is based on ecological, environmental, and biological principles. Specific definitions of CCF greatly vary and the concept usually includes a number of tenets or criteria. The most important tenet of CCF is the requirement to abandon the practice of largescale clearfelling in favour of selective thinning/harvesting and natural regeneration methods.Methods: CCF is commonly believed to have its main origin in an academic debate that was conducted through publications in a number of European and North American countries towards the end of the 19th and the beginning of the 20th century. Our findings are exclusively based on a literature review of the history of CCF and they revealed that the European origins of CCF go much further back to a form of farm forestry that started to be practised in Central Europe in the 17th century. Eventually, this type of farm forestry led to the formation of the single-tree selection system as we know it today. Another influential tradition line contributing to modern CCF is individual-based forest management, which breaks forest stands down into small neighbourhood-based units. The centres of these units are dominant frame trees which form the framework of a forest stand. Consequently, management is only carried out in the local neighbourhood of frame trees. Individual-based forest management also modified inflexible area-control approaches of plantation forest management in favour of the flexible sizecontrol method.Results and conclusions: We found evidence that the three aforementioned tradition lines are equally important and much interacted in shaping modern CCF. Since CCF is an international accomplishment, it is helpful to thoroughly study the drivers and causes of such concepts. Understanding the gradual evolution can give valuable clues for the introduction and adaptation of CCF in countries where the concept is new.
基金carried out within the framework of the most important innovative project of state importance“Development of a system of ground-based and remote monitoring of carbon pools and greenhouse gas fluxes on the territory of the Russian Federation,…”(No.123030300031-6)in the northern taiga subzone and on the border of tundra and taiga under the state assignment of the Forest Institute of the Karelian Research Center of the Russian Academy of Sciences(FMEN-2021-0018)with the partial financial support from RSF(grant no.21-14-00204)。
文摘Background:The heartwood(HW)proportion in the trunk of mature trees is an important characteristic not only for wood quality but also for assessing the role of forests in carbon sequestration.We have for the first time studied the proportion of HW in the trunk and the distribution of carbon and extractives in sapwood(SW)and HW of 70–80 year old Pinus sylvestris L.trees under different growing conditions in the pine forests of North-West Russia.Method:We have examined the influence of conditions and tree position in stand(dominant,intermediate and suppressed trees)in the ecological series:blueberry pine forest(Blu)–lingonberry pine forest(Lin)–lichen pine forest(Lic).We have analyzed the influence of climate conditions in the biogeographical series of Lin:the middle taiga subzone–the northern taiga subzone–the transition area of the northern taiga subzone and tundra.Results:We found that the carbon concentration in HW was 1.6%–3.4%higher than in SW,and the difference depended on growing conditions.Carbon concentration in HW increased with a decrease in stand productivity(Blu-Lin-Lic).In medium-productive stands,the carbon concentration in SW was higher in intermediate and supressed trees compared to dominant trees.In the series from south to north,carbon concentration in HW increased by up to 2%,while in SW,it rose by 2.7%–3.8%.Conclusions:Our results once again emphasized the need for an empirical assessment of the accurate carbon content in aboveground wood biomass,including various forest growing conditions,to better understand the role of boreal forests in carbon storage.
基金the project funding from Fundacao de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIGNo: APQ-02049-21)+3 种基金the National Council for Scientific and Technological Development (CNPqgrant 312567/2021-9)the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJgrant E26/201.007/2022) for funding support
文摘The woody vegetation is an important plant community constituent of tropical inselbergs,yet it remains largely overlooked.These environments of high socio-cultural and ecological value face pressures in many places,mainly related to mining exploitation and fires.This study provides the first systematic overview of inselberg woody vegetation in the Brazilian Atlantic Forest.We used four inselbergs as models to characterize the composition and structure of the woody vegetation.In addition,the biomass and carbon storage were estimated using the general equations for tropical regions and carbon concentration values.Ten transects(50 m×2 m)were systematically installed on each inselberg,and all woody plants with a diameter at breast height≥5 cm were measured,registered,and identified.A total of 312 individuals belonging to 26 species,23 genera,and 14 families were found.The Fabaceae family and the genus Eugenia(Myrtaceae)exhibited a higher richness.The woody community's diameters ranged from 5.0 to 116.9 cm(with a mean of 23.9 cm),and heights ranged from 1.7 to 16.0 m(with a mean of 6.2 m).All specialist lithophyte woody species found on inselbergs are wind-dispersed.Among the endemic species of the Atlantic Forest,four were endemic to inselbergs,with Pseudobombax petropolitanum and Wunderlichia azulensis being threatened.A few species dominated the communities:P.petropolitanum,Guapira opposita,Amburana cearensis,and Tabebuia reticulata.Carbon accumulated in aboveground biomass ranged from 14 to 48 Mg ha-1,indicating variability in woody vegetation structure and growing conditions among inselbergs.Lastly,we highlight target species for potential use for inselberg vegetation restoration in stone mining areas in Atlantic Forest.
