A number of risk ranking systems for contaminated sites have been developed by different jurisdictions. While the intent of each of these systems is similar, it is not clear whether they provide results that are compa...A number of risk ranking systems for contaminated sites have been developed by different jurisdictions. While the intent of each of these systems is similar, it is not clear whether they provide results that are comparable. In this paper, 20 contaminated sites are used to assess the United States’ Preliminary Assessment (PA) system, Sweden’s Methods for Inventories of Contaminated Sites (MICS) and New Zealand’s Risk Screening System (RSS) methods. The results were compared with each other and with Canada’s National Classification System for Contaminated Sites (NCSCS) as well as preliminary quantitative risk assessment (PQRA) results. The objectives were to determine if the systems yield similar recommendations regarding further actions, and to assess if there are acceptable correlations between different methods. The study concludes that PA, MICS and NCSCS methods can achieve similar conclusions, although there is a certain degree of inconsistency that is present, RSS can distinguish the very high and very low risk sites and, acceptable correlations exists among the methods except for PA and PQRA.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical couplin...To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.展开更多
Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong ...Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.展开更多
Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these subsystems.It is important to rank the weaknesses of subsystems and ide...Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these subsystems.It is important to rank the weaknesses of subsystems and identify the weakest subsystem to optimize products and improve their reliabilities.However,traditional ranking methods based on failure mode effect and critical analysis(FMECA)does not consider the complex maintenance of products.Herein,a weakness ranking method for the subsystems of heavy-duty machine tools is proposed based on generalized FMECA information.In this method,eight reliability indexes,including maintainability and maintenance cost,are considered in the generalized FMECA information.Subsequently,the cognition best worst method is used to calculate the weight of each screened index,and the weaknesses of the subsystems are ranked using a technique for order preference by similarity to an ideal solution.Finally,based on the failure data collected from certain domestic heavy-duty horizontal lathes,the weakness ranking result of the subsystems is obtained to verify the effectiveness of the proposed method.An improved weakness ranking method that can comprehensively analyze and identify weak subsystems is proposed herein for designing and improving the reliability of complex electromechanical products.展开更多
Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous repercussions.An Intrusion Detection System(IDS)plays an essential role in network security since it helps to pr...Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous repercussions.An Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks.Although extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still challenging.Therefore,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm rate.Initially,to improve training data quality,various preprocessing techniques are utilized.The Adaptive Synthetic oversampling technique generates synthetic samples for minority classes.In the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its rank.Then by simple plurality voting,the most optimal features are retrieved.Then the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the performance.The experiments were carried out on the original imbalanced data and balanced data.The outcomes demonstrate that the balanced data scenario knocked out the imbalanced data.Finally,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD),Canadian Institute for Cybersecurity(CIC-IDS 2017),and UNSW-NB15,datasets respectively.In addition,the suggested technique has been examined and contrasted with the state of art methods on three datasets.展开更多
In the study of recommendation systems,many methods based on predicting ratings have been put forward.However,the rating-predicting methods have some shortages.It pays too much attention to predicting,instead of the n...In the study of recommendation systems,many methods based on predicting ratings have been put forward.However,the rating-predicting methods have some shortages.It pays too much attention to predicting,instead of the nature of recommendation,which is predicting the order of ratings.Thus,we use a pairwise-based learning algorithm to learn our model and take the zero-sampling method to improve our model.In addition,we propose a text modeling method making the recommendations more explicable.It is proved that our system performs better than other state-of-art展开更多
On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rank...On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.展开更多
On the basis of ESI data,all universities are ranked in 96 out of 109 world-class disciplines.There is no ESI data(either publications or citations)in the rest of 13 world-class disciplines.
Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floo...Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floods is a key strategy to mitigate their impact.Accurate analysis of flash flood hazards can greatly enhance prevention efforts and inform critical decision-making processes,ultimately improving our ability to protect communities from these fast-onset disasters.This study analyzed the driving forces of flash flood disaster-causing factors in Heilongjiang Province.Meanwhile,nine different categories of variables affecting the occurrence of flash floods were selected,and the degree of influence of each driving factor on flash floods was quantitatively analyzed,and the driving force analysis of the driving factors of flash floods in Hei-longjiang Province was carried out by using the geographic probe model.This paper employs an uncertainty approach,utilizing a statistical-based interval weight deter-mination technique for evaluation indices and a two-dimensional information-based interval number sorting method.These methodologies are combined to construct a comprehensive flash flood risk assessment model.On this basis,the model was implemented in six regions within China's Heilongjiang province to evaluate and prioritize flash flood risks.The resulting risk ranking for these areas was as follows:Bayan≻Shuangcheng≻Boli≻Suibin≻Hailun≻Yian.The findings demonstrate that the interval number-based evaluation method effectively handles uncertainty,providing a more reliable risk grading system.This approach,by leveraging modern scientific advances and risk quantification techniques,is crucial for improving disaster management and mitigating flash flood impacts.展开更多
Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which cont...Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which contradicts its prominent scientific standing.The purpose of this study is to investigate the underlying causes of such inaccurate assessments and to propose methods for conducting more reliable evaluations.Design/methodology/approach:The study evaluates the effectiveness of top-percentile citation metrics as indicators of breakthrough research.Using case studies of selected countries and research topics,the study examines how deviations from lognormal citation distributions impact the accuracy of these percentile indicators.A similar analysis is conducted using university data from the Leiden Ranking to investigate citation distribution deviations at the institutional level.Findings:The study finds that inflated lower tails in citation distributions lead to undervaluation of research capabilities in advanced technological countries,as captured by some percentile indicators.Conversely,research-intensive universities exhibit the opposite trend:a reduced lower tail relative to the upper tail,which causes percentile indicators to overestimate their actual research capacity.Research limitations:The descriptions are mathematical facts that are self-evident.Practical implications:The ratios between the number of papers in the global top 10%and 1%by citation count to the total number of papers are commonly used to describe research performance.However,due to variations in citation patterns across countries and institutions with reference to the global pattern,these ratios can be misleading and lose their value as research indicators.Originality/value:Size-independent research performance indicators,obtained as the ratios between paper counts in top percentiles and the total numbers of publications,are widely used by public and private institutions.This study demonstrates that the use of these ratios for research evaluations and country rankings can be highly misleading.展开更多
In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web page...In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.展开更多
This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial ra...This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.展开更多
文摘A number of risk ranking systems for contaminated sites have been developed by different jurisdictions. While the intent of each of these systems is similar, it is not clear whether they provide results that are comparable. In this paper, 20 contaminated sites are used to assess the United States’ Preliminary Assessment (PA) system, Sweden’s Methods for Inventories of Contaminated Sites (MICS) and New Zealand’s Risk Screening System (RSS) methods. The results were compared with each other and with Canada’s National Classification System for Contaminated Sites (NCSCS) as well as preliminary quantitative risk assessment (PQRA) results. The objectives were to determine if the systems yield similar recommendations regarding further actions, and to assess if there are acceptable correlations between different methods. The study concludes that PA, MICS and NCSCS methods can achieve similar conclusions, although there is a certain degree of inconsistency that is present, RSS can distinguish the very high and very low risk sites and, acceptable correlations exists among the methods except for PA and PQRA.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.3122019191).
文摘To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.
文摘Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.
基金Supported by National Nat ural Science Foundation of China(Grant Nos.51675227,51975249)Jilin Province Science and Technology Development Funds(Grant Nos.20180201007GX,20190302017GX)+2 种基金Technology Development and Research of Jilin Province(Grant No.2019C037-01)Changchun Science and Technology Planning Project(Grant No.19SS011)National Science and technology Major Project(Grant No.2014ZX04015031).
文摘Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these subsystems.It is important to rank the weaknesses of subsystems and identify the weakest subsystem to optimize products and improve their reliabilities.However,traditional ranking methods based on failure mode effect and critical analysis(FMECA)does not consider the complex maintenance of products.Herein,a weakness ranking method for the subsystems of heavy-duty machine tools is proposed based on generalized FMECA information.In this method,eight reliability indexes,including maintainability and maintenance cost,are considered in the generalized FMECA information.Subsequently,the cognition best worst method is used to calculate the weight of each screened index,and the weaknesses of the subsystems are ranked using a technique for order preference by similarity to an ideal solution.Finally,based on the failure data collected from certain domestic heavy-duty horizontal lathes,the weakness ranking result of the subsystems is obtained to verify the effectiveness of the proposed method.An improved weakness ranking method that can comprehensively analyze and identify weak subsystems is proposed herein for designing and improving the reliability of complex electromechanical products.
文摘Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous repercussions.An Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks.Although extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still challenging.Therefore,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm rate.Initially,to improve training data quality,various preprocessing techniques are utilized.The Adaptive Synthetic oversampling technique generates synthetic samples for minority classes.In the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its rank.Then by simple plurality voting,the most optimal features are retrieved.Then the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the performance.The experiments were carried out on the original imbalanced data and balanced data.The outcomes demonstrate that the balanced data scenario knocked out the imbalanced data.Finally,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD),Canadian Institute for Cybersecurity(CIC-IDS 2017),and UNSW-NB15,datasets respectively.In addition,the suggested technique has been examined and contrasted with the state of art methods on three datasets.
文摘In the study of recommendation systems,many methods based on predicting ratings have been put forward.However,the rating-predicting methods have some shortages.It pays too much attention to predicting,instead of the nature of recommendation,which is predicting the order of ratings.Thus,we use a pairwise-based learning algorithm to learn our model and take the zero-sampling method to improve our model.In addition,we propose a text modeling method making the recommendations more explicable.It is proved that our system performs better than other state-of-art
文摘On the basis of research evaluation of Chinese universities,Golden Apple Ranking(GAR)was initiated by Research Center of Chinese Science Evaluation(RCCSE)at Wuhan University in 2003.The GAR consists of four major rankings:Chinese University Ranking,Chinese Graduate School Ranking,World University Ranking and Scholarly Journal Ranking.The annual reports of all these four rankings are published bythe Science Press,which have been recognized by the academia and China's government.
文摘On the basis of ESI data,all universities are ranked in 96 out of 109 world-class disciplines.There is no ESI data(either publications or citations)in the rest of 13 world-class disciplines.
基金Basic Scientific Research Expense Project of IWHR-Extreme rainstorm development trends and prediction techniques,Grant/Award Number:JZ0145B142024National Natural Science Foundation of China,Grant/Award Number:42271095。
文摘Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floods is a key strategy to mitigate their impact.Accurate analysis of flash flood hazards can greatly enhance prevention efforts and inform critical decision-making processes,ultimately improving our ability to protect communities from these fast-onset disasters.This study analyzed the driving forces of flash flood disaster-causing factors in Heilongjiang Province.Meanwhile,nine different categories of variables affecting the occurrence of flash floods were selected,and the degree of influence of each driving factor on flash floods was quantitatively analyzed,and the driving force analysis of the driving factors of flash floods in Hei-longjiang Province was carried out by using the geographic probe model.This paper employs an uncertainty approach,utilizing a statistical-based interval weight deter-mination technique for evaluation indices and a two-dimensional information-based interval number sorting method.These methodologies are combined to construct a comprehensive flash flood risk assessment model.On this basis,the model was implemented in six regions within China's Heilongjiang province to evaluate and prioritize flash flood risks.The resulting risk ranking for these areas was as follows:Bayan≻Shuangcheng≻Boli≻Suibin≻Hailun≻Yian.The findings demonstrate that the interval number-based evaluation method effectively handles uncertainty,providing a more reliable risk grading system.This approach,by leveraging modern scientific advances and risk quantification techniques,is crucial for improving disaster management and mitigating flash flood impacts.
文摘Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which contradicts its prominent scientific standing.The purpose of this study is to investigate the underlying causes of such inaccurate assessments and to propose methods for conducting more reliable evaluations.Design/methodology/approach:The study evaluates the effectiveness of top-percentile citation metrics as indicators of breakthrough research.Using case studies of selected countries and research topics,the study examines how deviations from lognormal citation distributions impact the accuracy of these percentile indicators.A similar analysis is conducted using university data from the Leiden Ranking to investigate citation distribution deviations at the institutional level.Findings:The study finds that inflated lower tails in citation distributions lead to undervaluation of research capabilities in advanced technological countries,as captured by some percentile indicators.Conversely,research-intensive universities exhibit the opposite trend:a reduced lower tail relative to the upper tail,which causes percentile indicators to overestimate their actual research capacity.Research limitations:The descriptions are mathematical facts that are self-evident.Practical implications:The ratios between the number of papers in the global top 10%and 1%by citation count to the total number of papers are commonly used to describe research performance.However,due to variations in citation patterns across countries and institutions with reference to the global pattern,these ratios can be misleading and lose their value as research indicators.Originality/value:Size-independent research performance indicators,obtained as the ratios between paper counts in top percentiles and the total numbers of publications,are widely used by public and private institutions.This study demonstrates that the use of these ratios for research evaluations and country rankings can be highly misleading.
基金The Natural Science Foundation of South-Central University for Nationalities(No.YZZ07006)
文摘In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(No.ZCLQN25A0103)。
文摘This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.
文摘目的基于“脑-肠-骨轴”初步探讨左归丸对老年性骨质疏松症(senile osteoporosis,SOP)模型小鼠海马区神经元退行性病变、肠道菌群变化及骨量丢失的作用。方法连续12周腹腔注射D-半乳糖(120 mg/kg)构建SOP模型,将小鼠随机分为空白组、模型组、左归丸高剂量组、左归丸低剂量组;予以左归丸药物干预8周,随后进行Morris水迷宫实验检测小鼠认知功能。取材后检测各组脑组织氧化应激指标,尼氏染色法检测海马尼式神经元完整性,16 S rRNA检测肠道菌群多样性,小动物X光机检测小鼠骨量丢失情况,小鼠血清检测骨代谢指标,免疫组织化学法检测骨代谢通路相关蛋白,采用spearman分析法对血清骨代谢因子、脑组织氧化应激因子与肠道菌群中的差异菌群进行关联分析。结果与Model组相比,左归丸给药组可显著缩短逃避潜伏期时间(P<0.05);显著增加海马区尼式小体数量(P<0.01);提高脑组织抗氧化酶含量(P<0.05);在门水平上,Model组小鼠肠道菌群中Bacteroidota相对丰度显著增高(P<0.01),左归丸给药组Verrucomicrobiota等菌群相对丰度显著升高(P<0.05),Bacteroidota相对丰度显著下降(P<0.05)。小鼠股骨干骺端与骨干区的骨量丢失得到改善,显著改善血清骨代谢指标(P<0.01),显著提高OPG/RANKL蛋白含量比值(P<0.01)。相关性分析显示,过氧化氢酶与肠道菌群中的Proteobacteria呈正相关(P<0.01),与Prevotellaceae_NK3B31_group呈负相关(P<0.01)。结论左归丸依据“脑-肠-骨轴”改善SOP小鼠肠道菌群丰度,进而缓解海马神经元退行性病变,调节OPG/RANK/RANKL通路缓解骨量丢失。
