Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Influence fast or later:Two types of influencers in social networks

In real-world networks,there usually exist a small set of nodes that play an important role in the structure and function of networks.Those vital nodes can influence most of other nodes in the network via a spreading process.While most of the existing works focused on vital nodes that can maximize the spreading size in the final stage,which we call final influencers,recent work proposed the idea of fast influencers,which emphasizes nodes’spreading capacity at the early stage.Despite the recent surge of efforts in identifying these two types of influencers in networks,there remained limited research on untangling the differences between the fast influencers and final influencers.In this paper,we firstly distinguish the two types of influencers:fast-only influencers and final-only influencers.The former is defined as individuals who can achieve a high spreading effect at the early stage but lose their superiority in the final stage,and the latter are those individuals that fail to exhibit a prominent spreading performance at the early stage but influence a large fraction of nodes at the final stage.Further experiments are based on eight empirical datasets,and we reveal the key differences between the two types of influencers concerning their spreading capacity and the local structures.We also analyze how network degree assortativity influences the fraction of the proposed two types of influencers.The results demonstrate that with the increase of degree assortativity,the fraction of the fast-only influencers decreases,which indicates that more fast influencers tend to keep their superiority at the final stage.Our study provides insights into the differences and evolution of different types of influencers and has important implications for various empirical applications,such as advertisement marketing and epidemic suppressing.

Mining Topical Influencers Based on the Multi-Relational Network in Micro-Blogging Sites

In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.

Examining the Impacts of Key Influencers on Community Development

In this research,we aim to identify and investigate the impacts of key influencers on community formations and developments.We assess the impacts of key influencers by analyzing the activities and structure of the social media presence of a local community.Results of our analysis show that key influencers play important roles in connecting the community,transferring information,and improving overall sentiment of the community members.Our findings suggest that community practitioners can apply social network analysis to identify value-added influencers and discover strategies for improving the community and keeping leadership roles.

A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approach minimizes the network size and eliminates undesirable connections.For that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer.Therefore,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to 1.Node with the higher value ofαgets the higher priority and vice versa.The threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems.Finally,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information.The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks.Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network.Moreover,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network.Furthermore,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

I’m Nervous about Sharing This Secret with You:Youtube Influencers Generate Strong Parasocial Interactions by Discussing Personal Issues

Purpose:Performers may generate loyalty partly through eliciting illusory personal connections with their audience,parasocial relationships(PSRs),and individual illusory exchanges,parasocial interactions(PSIs).On social media,semi-PSIs are real but imbalanced exchanges with audiences,including through comments on influencers’videos,and strong semi-PSIs are those that occur within PSRs.This article introduces and assesses an automatic method to detect videos with strong PSI potential.Design/methodology/approach:Strong semi-PSIs were hypothesized to occur when commenters used a variant of the pronoun“you”,typically addressing the influencer.Comments on the videos of UK female influencer channels were used to test whether the proportion of you pronoun comments could be an automated indicator of strong PSI potential,and to find factors associating with the strong PSI potential of influencer videos.The highest and lowest strong PSI potential videos for 117 influencers were classified with content analysis for strong PSI potential and evidence of factors that might elicit PSIs.Findings:The you pronoun proportion was effective at indicating video strong PSI potential,the first automated method to detect any type of PSI.Gazing at the camera,head and shoulders framing,discussing personal issues,and focusing on the influencer associated with higher strong PSI potential for influencer videos.New social media factors found include requesting feedback and discussing the channel itself.Research limitations:Only one country,genre and social media platform was analysed.Practical implications:The method can be used to automatically detect YouTube videos with strong PSI potential,helping influencers to monitor their performance.Originality/value:This is the first automatic method to detect any aspect of PSI or PSR.

The Different Perception and Reaction of Customers Toward Traditional Marketing and Influencer Marketing in Food

This research paper is aimed at investigating the consumer preference for traditional marketing and influencer marketing in food industry.To accomplish this,the researchers set two questionnaire surveys to collect data.These two questionnaires investigated consumers’different attitudes and reaction toward two rose-cake brands(Holiland and LI ZI QI),by showing them the two different types of advertisements:one for traditional marketing and the other for influencer marketing.After a detailed analysis of the data collected,it was discovered that both influencer marketing and traditional marketing have the capability to deliver the persuasive product information and make consumers desirable to purchase the products;however,consumers tend to pay more creditably and trust to LI ZI QI’s brand,which is the brand using influencer marketing,and are willing to spend more money on it.

An Analysis of Influencers of Energy Security for SMEs in the Greater Accra Region of Ghana

Investments in the exploitation of the continent’s raw material reserves have resulted in little (positive) impact on the energy security situation within Africa. This paper attempts to explore factors influencing the level of energy security of Small Medium Enterprises (SMEs) in the Greater Accra Region of Ghana, and from the dimension of availability, affordability, economic efficiency and environmental stewardship or sustainability of energy. The cross-sectional survey design was employed as the design for the collection and analysis of data. The study applied a quantitative research method in examining the indicators of energy security for SMEs in Accra Metropolis in the Greater Accra Region of Ghana. The population covered about 500 SMEs who have about 100 employees and total revenue of $1 million or the equivalence in Ghana Cedis. A sample of 246 was selected through a cluster and simple random sampling. The analysis adopted statistical techniques to conduct descriptive, exploratory and inferential analysis of the data collected. Factors that influence the level of energy security of SMEs are broadly categorized into the environment and economic change, political factors, physical disruptions and market conditions. Ge- nerally, the level of energy security under the availability dimension is medium among the SMEs. Issues of affordability, economic efficiency and environmental stewardship provide high medium interventions on energy security for SMEs. It is recommended that managers of Ghana’s energy sector should ensure prudent management of the country’s energy resources since the disruption of supply lines or sources form the key conditions that influence the energy security level of SMEs. SMEs should adopt energy efficiency measures to ensure that energy is conserved for future use.

The Media Images of Old Influencers on TikTok:A Multimodal Critical Discourse Analysis

Drawing on the insights from multimodal critical discourse analysis,this study mainly focuses on two super-gerontic celebrities by selecting 60 videos to elucidate how their images are represented through linguistic(e.g.,captions,and hashtags)and other semiotic resources(e.g.,accessories,semiotic tools,and etc.).The findings suggest that images of the old celebrities are characterized as being attractive,wise,stylish,healthy and young,indicating that they are breaking up the stereotypical images of old people who are unattractive,dependent and senile.The phenomenon of old influencers making themselves visible on the social media suggests that they are moving towards successful aging with the backdrop of aging society in China.This present study suggests that multimodal construction of old celebrities can inform relevant practice to combat ageism in the media and helps to empower the old people in the media discourse.In doing so,the pedagogical implication of present study lies in its education about how we view aging in the aging society.

Who should you be following? The top 100 social media influencers in orthopaedic surgery

BACKGROUND Social media has been credited with the potential to transform medicine,and Twitter was recently named“an essential tool”for the academic surgeon.Despite this,peer-to-peer and educational influence on social media has not been studied within orthopaedic surgery.This knowledge is important to identify who is controlling the conversation about orthopaedics to the public.We hypothesized that the plurality of top influencers would be sports medicine surgeons,that social media influence would not be disconnected from academic productivity,and that some of the top social media influencers in orthopaedic surgery would not be orthopaedic surgeons.AIM To identify the top 100 social media influencers within orthopaedics,characterize who they are,and relate their social media influence to academic influence.METHODS Twitter influence scores for the topic“orthopaedics”were collected in July 2018 using Right Relevance software.The accounts with the top influence scores were linked to individual names,and the account owners were characterized with respect to specialty,subspecialty,practice setting,location,board certification,and academic Hirsch index(h-index).RESULTS Seventy-eight percent of top influencers were orthopaedic surgeons.The most common locations included California(13%),Florida(8%),New York(7%),United Kingdom(7%),Colorado(6%),and Minnesota(6%).The mean academic h-index of the top influencers(n=79)was 13.67±4.12(mean±95%CI)and median 7(range 1-89)(median reported h-index of academic orthopaedic faculty is 5 and orthopaedic chairpersons is 13).Of the 78 orthopaedic surgeons,the most common subspecialties were sports medicine(54%),hand and upper extremity(18%),and spine(8%).Most influencers worked in private practice(53%),followed by academics(17%),privademics(14%),and hospital-based(9%).All eligible orthopaedic surgeons with publicly-verifiable board certification statuses were board-certified(n=74).CONCLUSION The top orthopaedic social media influencers on Twitter were predominantly board-certified,sports-medicine subspecialists working in private practice in the United States.Social media influence was highly concordant with academic productivity as measured by the academic h-index.Though the majority of influencers are orthopaedic surgeons,22%of top influencers on Twitter are not,which is important to identify given the potential for these individuals to influence patients’perceptions and expectations.This study also provides the top influencer network for other orthopaedic surgeons to engage with on social media to improve their own social media influence.

Social function scores and influencing factors in patients with residual depressive symptoms

BACKGROUND At present,the influencing factors of social function in patients with residual depressive symptoms are still unclear.Residual depressive symptoms are highly harmful,leading to low mood in patients,affecting work and interpersonal communication,increasing the risk of recurrence,and adding to the burden on families.Studying the influencing factors of their social function is of great significance.AIM To explore the social function score and its influencing factors in patients with residual depressive symptoms.METHODS This observational study surveyed patients with residual depressive symptoms(case group)and healthy patients undergoing physical examinations(control group).Participants were admitted between January 2022 and December 2023.Social functioning was assessed using the Sheehan Disability Scale(SDS),and scores were compared between groups.Factors influencing SDS scores in patients with residual depressive symptoms were analyzed by applying multiple linear regression while using the receiver operating characteristic curve,and these RESULTS The SDS scores of the 158 patients with depressive symptoms were 11.48±3.26.Compared with the control group,the SDS scores and all items in the case group were higher.SDS scores were higher in patients with relapse,discon-tinuous medication,drug therapy alone,severe somatic symptoms,obvious residual symptoms,and anxiety scores≥8.Disease history,medication compliance,therapy method,and residual symptoms correlated positively with SDS scores(r=0.354,0.414,0.602,and 0.456,respectively).Independent influencing factors included disease history,medication compliance,therapy method,somatic symptoms,residual symptoms,and anxiety scores(P<0.05).The areas under the curve for predicting social functional impairment using these factors were 0.713,0.559,0.684,0.729,0.668,and 0.628,respectively,with sensitivities of 79.2%,61.8%,76.8%,81.7%,63.6%,and 65.5%and specificities of 83.3%,87.5%,82.6%,83.3%,86.7%,and 92.1%,respectively.CONCLUSION The social function scores of patients with residual symptoms of depression are high.They are affected by disease history,medication compliance,therapy method,degree of somatic symptoms,residual symptoms,and anxiety.

Digital Influencer's choice of product endorsement:A perspective of congruence

To infer product fitness information,consumers often consult digital influencers who exert effort to learn and evaluate product features and then propagate information to their followers.However,influencers'recommendations are not limited to products that exactly match their expertise,which may lead followers to unfollow them.This study investigates which product(high or low congruence)an influencer should endorse and how much endorsement effort should be exerted.Using a theoretical model,we obtain several interesting results.First,endorsing a low-congruence product can surprisingly benefit the influencer if the penalty per lost follower is moderate or if the influencer has sufficiently high efficiency in reducing the perception error of this product.Second,although the influencer has high expertise in the high-congruence product,she will lose more followers when endorsing it than when endorsing the low-congruence product if a particularly small or large proportion of her followers are potential consumers of the low-congruence product.Third,when the influencer endorses the low-congruence product,the number of new followers increases with the size of the influencer's follower base only when the follower base is small and a large proportion of followers are potential consumers of the low-congruence product.Interestingly,as the size of the follower base increases,the influencer always exerts more effort when endorsing the high-congruence product but will exert less effort in learning about the low-congruence product if a sufficiently small proportion of followers are potential consumers.Finally,after taking consumers'knowledge about product features into account,the main results still hold,and when considering the competition between influencers,we uncover the conditions under which both influencers will choose to endorse the high-congruence product.

Invisible Influencers--Language experts' role in the frontline of the battle against COVID-19

When Ji Jin flew into Italy in March,it was the height of the novel coronavirus disease(COVID-19)epidemic there and her work meant direct exposure to high risk.She was an interpreter for the Chinese medical team that had been sent to work with Italian doctors on COVID-19 prevention and control.

基于区块链的Gossip协议优化研究

Gossip网络协议具有高效性和扩展性,广泛应用于区块链分布式系统的底层通信协议。针对联盟链中Gossip网络协议的冗余现象造成的传播效率下降,提出了Stack Gossip算法。该算法将收到的节点信息记录在信息栈中,避免向一个节点重复发送消息。实验发现,Stack Gossip算法不适用于大规模网络中节点数量过多的情况。为此,进一步提出Influence Gossip算法,其核心思路是节点通过评估邻居节点的信息影响力来选择通信的对等节点,在一定程度上避免了传统Gossip网络协议节点间传播的随机性。实验结果表明,与Random Gossip算法相比,Influence Gossip和Stack Gossip算法传播效率和产生的冗余有明显改进。

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture(hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector(OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that:(1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km^(2). In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak.(2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced.(3) The results of OPGD detection in 2015 show that the influence of each factor is slope(0.2371) > traffic accessibility(0.2098) > population(0.1403) > regional GDP(0.1325) > elevation(0.0987) > poverty alleviation(0). The results of OPGD detection in 2020 show that the influence of each factor is slope(0.2339) > traffic accessibility(0.2198) > population(0.1432) > regional GDP(0.1219) > poverty alleviation(0.0992) > elevation(0.093). Natural geographical factors(slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors(traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decisionmaking basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies.

Influencing Factors on Cardiovascular Health in China

The Annual Report on Cardiovascular Health and Diseases in China(2022)intricate landscape of cardiovascular health in China.This is the first section of the report,which dissects influential factors across diverse domains.The investigation identifies tobacco use as a paramount concern,portraying China as the global epicenter of tobacco consumption.Cigarette smoking,exacerbated by second-hand smoke exposure,emerges as a critical and preventable risk factor,contributing to a surge in attributable deaths over the past three decades.In the realm of dietary nutrition,the study discerns an overall improvement,yet discerns worrisome deviations,notably an escalating fat intake surpassing recommended guidelines.The shifting dietary structure reveals diminished consumption of cereals and vegetables juxtaposed with an uptick in animal foods,while excessive intake of cooking oil and salt persists,straying substantially from endorsed levels.The exploration of physical activity patterns unfolds a nuanced narrative.Varied trends are observed among students,with concerns arising from sedentary behaviors and inadequate adherence to recommended guidelines.The analysis spans a trajectory of declining physical activity in Chinese adults,coupled with an alarming surge in sedentary leisure time,ultimately linking these factors to heightened risks of cardiovascular diseases and increased adiposity.An examination of overweight and obesity trends uncovers a relentless upward trajectory,projecting substantial prevalence by 2030.Noteworthy prevalence rates underscore the imperative for targeted interventions to curtail this burgeoning health crisis,with the anticipated prevalence extending to nearly two-thirds of the adult population.Psychological factors,notably depression,constitute an integral facet of cardiovascular health.Prevalence rates among patients with coronary artery disease and acute myocardial infarction underscore the intricate interplay between mental health and cardiovascular outcomes.Additionally,persistent depressive symptoms are shown to significantly elevate the risk of cardiovascular diseases and mortality.This first section underscores the multifaceted challenges facing cardiovascular health in China,emphasizing the imperative for tailored interventions across tobacco control,dietary habits,physical activity,obesity management,and psychological well-being to mitigate the escalating burden of cardiovascular diseases in the population.

Geochemical identification of a source rock affected by migrated hydrocarbons and its geological significance:Fengcheng Formation,southern Mahu Sag,Junggar Basin,NW China

The Fengcheng Formation is a crucial source rock and the primary reservoir for oil accumulation in the Mahu Sag.Crude oils are distributed throughout the Fengcheng Formation,ranging from the edge to the interior of the sag in the southern Mahu Sag.These crude oils originate from in-situ source rocks in shallowly buried areas and the inner deep sag.During migration,the crude oil from the inner deep sag affects the source rocks close to carrier beds,leading to changes in the organic geochemical characteristics of the source rocks.These changes might alter source rock evaluations and oil-source correlation.Based on data such as total organic carbon(TOC),Rock-Eval pyrolysis of source rocks,and gas chromatography-mass spectrometry(GC-MS)of the saturated fraction,and considering the geological characteristics of the study area,we define the identification characteristics of source rock affected by migrated hydrocarbons and establish the various patterns of influence that migrated hydrocarbons have on the source rock of the Fengcheng Formation in the southern Mahu Sag.The source rocks of the Fengcheng Formation are mostly fair to good,containing mainly Type II organic matter and being thermally mature enough to generate oil.Source rocks affected by migrated hydrocarbons exhibit relatively high hydrocarbon contents(S1/TOC>110 mg HC/g TOC,Extract/TOC>30%,HC:hydrocarbon),relatively low Rock-Eval Tmax values,and relatively high tricyclic terpane contents with a descending and mountain-shaped distribution.Furthermore,biomarker composition parameters indicate a higher thermal maturity than in-situ source rocks.Through a comparison of the extract biomarker fingerprints of adjacent reservoirs and mudstones in different boreholes,three types of influence patterns of migrated hydrocarbons are identified:the edge-influence of thin sandstone-thick mudstone,the mixed-influence of sandstone-mudstone interbedded,and the full-influence of thick sandstone-thin mudstone.This finding reminds us that the influence of migrated hydrocarbons must be considered when evaluating source rocks and conducting oil-source correlation.

Establishment of predictive models and determinants of preoperative gastric retention in endoscopic retrograde cholangiopancreatography

BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions.To study the influence of unloading rate on rockburst,gneiss rockburst experiments were conducted under three groups of unloading rates.A high-speed photography system and acoustic emission(AE)system were used to monitor the entire process of rockburst process in real-time.The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate,which is manifested as the reduction of AE energy and fragments ejection velocity.The mechanisms are proposed to explain this effect:(i)The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst.This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate,and more energy released in the form of shear crack propagation.Then,less strain energy is converted into kinetic energy of fragments ejection.(ii)Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate,resulting in the destruction of rockburst incubation process.The enlightenments of reducing the unloading rate for the project are also described quantitatively.The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s,which was judged by the ejection velocity.

Aboveground carbon sequestration of Cunninghamia lanceolata forests:Magnitude and drivers

Understanding the spatial variation,temporal changes,and their underlying driving forces of carbon sequestration in various forests is of great importance for understanding the carbon cycle and carbon management options.How carbon density and sequestration in various Cunninghamia lanceolata forests,extensively cultivated for timber production in subtropical China,vary with biodiversity,forest structure,environment,and cultural factors remain poorly explored,presenting a critical knowledge gap for realizing carbon sequestration supply potential through management.Based on a large-scale database of 449 permanent forest inventory plots,we quantified the spatial-temporal heterogeneity of aboveground carbon densities and carbon accumulation rates in Cunninghamia lanceolate forests in Hunan Province,China,and attributed the contributions of stand structure,environmental,and management factors to the heterogeneity using quantile age-sequence analysis,partial least squares path modeling(PLS-PM),and hot-spot analysis.The results showed lower values of carbon density and sequestration on average,in comparison with other forests in the same climate zone(i.e.,subtropics),with pronounced spatial and temporal variability.Specifically,quantile regression analysis using carbon accumulation rates along an age sequence showed large differences in carbon sequestration rates among underperformed and outperformed forests(0.50 and 1.80 Mg·ha^(-1)·yr^(-1)).PLS-PM demonstrated that maximum DBH and stand density were the main crucial drivers of aboveground carbon density from young to mature forests.Furthermore,species diversity and geotopographic factors were the significant factors causing the large discrepancy in aboveground carbon density change between low-and high-carbon-bearing forests.Hotspot analysis revealed the importance of culture attributes in shaping the geospatial patterns of carbon sequestration.Our work highlighted that retaining largesized DBH trees and increasing shade-tolerant tree species were important to enhance carbon sequestration in C.lanceolate forests.