ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering

To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition(VMD),fuzzy entropy(FE)and fuzzy clustering(FC).Firstly,based on the OTDR curve data collected in the field,VMD is used to extract the different modal components(IMF)of the original signal and calculate the fuzzy entropy(FE)values of different components to characterize the subtle differences between them.The fuzzy entropy of each curve is used as the feature vector,which in turn constructs the communication optical fibre feature vector matrix,and the fuzzy clustering algorithm is used to achieve fault diagnosis of faulty optical fibre.The VMD-FE combination can extract subtle differences in features,and the fuzzy clustering algorithm does not require sample training.The experimental results show that the model in this paper has high accuracy and is relevant to the maintenance of communication optical fibre when compared with existing feature extraction models and traditional machine learning models.

Simulation Method and Feature Analysis of Shutdown Pressure Evolution During Multi-Cluster Fracturing Stimulation

Multistage multi-cluster hydraulic fracturing has enabled the economic exploitation of shale reservoirs,but the interpretation of hydraulic fracture parameters is challenging.The pressure signals after pump shutdown are influenced by hydraulic fractures,which can reflect the geometric features of hydraulic fracture.The shutdown pressure can be used to interpret the hydraulic fracture parameters in a real-time and cost-effective manner.In this paper,a mathematical model for shutdown pressure evolution is developed considering the effects of wellbore friction,perforation friction and fluid loss in fractures.An efficient numerical simulation method is established by using the method of characteristics.Based on this method,the impacts of fracture half-length,fracture height,opened cluster and perforation number,and filtration coefficient on the evolution of shutdown pressure are analyzed.The results indicate that a larger fracture half-length may hasten the decay of shutdown pressure,while a larger fracture height can slow down the decay of shutdown pressure.A smaller number of opened clusters and perforations can significantly increase the perforation friction and decrease the overall level of shutdown pressure.A larger filtration coefficient may accelerate the fluid filtration in the fracture and hasten the drop of the shutdown pressure.The simulation method of shutdown pressure,as well as the analysis results,has important implications for the interpretation of hydraulic fracture parameters.

Investigation of point defect evolution and Voronoi cluster analysis for magnesium during nanoindentation

The present study investigates the effect of nanoindentation on single-crystal magnesium specimens using the embedded-atom method potential in molecular dynamics simulation.Analyses are done under dynamic loading where the load-bearing capacity and change in the structural configuration are studied on the basal(Z-direction)and two prismatic planes(X-and Y-directions)with varying indenter velocities.The investigation of structural evolution is done using atomic displacement analyses to measure the net magnitude of displacement,atomic strain analyses to evaluate the shear strain developed in the process,and Wigner-Seitz defect analyses to calculate the total vacancies at varied timesteps.Furthermore,Voronoi analyses are done when indented on the basal plane to identify the cluster distribution at different planar depths of the specimen.From the analyses,it has been observed that the load-bearing capacity of the specimen varies with the indentation velocity and the direction of indentation on the specimen.Additionally,it is seen that the observed shear and total atomic displacement in the Z-direction is the least in comparison to the other two axes.The partial dislocation 1/3<-12-10>is seen to be majorly present and the population of dislocation loops is more abundant for lower indenter velocities.Furthermore,clusters<0,4,4,6>and<0,6,0,8>are the major indices developed during nanoindentation on the basal plane where they exhibit symmetrical distribution as observed from the Z-direction.

Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

Fuzzy cluster analysis of water mass in the western Taiwan Strait in spring 2019

The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.

基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.

Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information.Precision agriculture uses datamining to advance agricultural developments.Many farmers aren’t getting the most out of their land because they don’t use precision agriculture.They harvest crops without a well-planned recommendation system.Future crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical data.Most existing research still needs to address data preprocessing and crop categorization/classification.Furthermore,statistical analysis receives less attention,despite producing more accurate and valid results.The study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH values.The research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high productivity.The presented algorithm converts discrete numerals to factors first,then reduces levels.Second,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation.The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and geo-location.Finally,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

Lagrangian coherent structure analysis on transport of Acetes chinensis along coast of Lianyungang,China

Spatial heterogeneity or“patchiness”of plankton distributions in the ocean has always been an attractive and challenging scientific issue to oceanographers.We focused on the accumulation and dynamic mechanism of the Acetes chinensis in the Lianyungang nearshore licensed fishing area.The Lagrangian frame approaches including the Lagrangian coherent structures theory,Lagrangian residual current,and Lagrangian particle-tracking model were applied to find the transport pathways and aggregation characteristics of Acetes chinensis.There exist some material transport pathways for Acetes chinensis passing through the licensed fishing area,and Acetes chinensis is easy to accumulate in the licensed fishing area.The main mechanism forming this distribution pattern is the local circulation induced by the nonlinear interaction of topography and tidal flow.Both the Lagrangian coherent structure analysis and the particle trajectory tracking indicate that Acetes chinensis in the licensed fishing area come from the nearshore estuary.This work contributed to the adjustment of licensed fishing area and the efficient utilization of fishery resources.

Global trends and knowledge-relationship of symptom clusters in cancer research:a bibliometric analysis over the past 20 years

Objective:To use Cite Space and VOSviewer to investigate the scientific production in the field of symptom clusters in cancer research.Methods:The search was performed using the terms“symptom clusters,”“cancer,”and“oncology”on the Web of Science Core Collection database.The retrieval time was from 2001 to 2021,which covers the last 2 decades.Based on the production theory of scientific knowledge and the data mining of citations,data pertaining to the annual publications,journals,countries,organizations,authors,and keywords that produce symptom clusters in cancer research,as well as their cooperation(collaboration network),were extracted,and then both were clarified by the software tools VOSviewer(version 1.6.16)and Cite Space(version 6.1.R2).Results:A total of 1796 publications were retrieved between 2001 and 2021,and 473 relevant publications were included after screening.The results showed an increasing trend in published articles.The United States had the largest number of publications(261/473,55.18%),followed by China and Canada.The University of California,San Francisco,was the most productive institution.Current research hotspots included the analysis of symptom clusters and symptom management in patients with breast cancer and lung cancer,as well as any advanced cancer and cancer cachexia;fatigue-related symptom clusters and depression-anxiety symptom cluster;and the impacts of symptom clusters on quality of life.The research frontiers included analysis between health-related quality of life and symptom clusters,data mining in symptom clusters,research on the mental health status of cancer patients,and study of the mechanism and biological pathways of symptom clusters.Conclusions:The study provides insight into the global research perspective for the scientific progress on cancer symptom clusters,which suggests a growing scientific interest in this field,and more studies are warranted to guide symptom management.

A Review on Clustering Methods for Climatology Analysis and Its Application over South America

South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influence of distinct atmospheric systems. While some studies have characterized the prevailing systems over South America, they often lacked the utilization of statistical techniques for homogenization. On the other hand, other research has employed multivariate statistical methods to identify homogeneous regions regarding temperature and precipitation, but their focus has been limited to specific areas, such as the south, southeast, and northeast. Surprisingly, there is a lack of work that compares various multivariate statistical techniques to determine homogeneous regions across the entirety of South America concerning temperature and precipitation. This paper aims to address this gap by comparing three such techniques: Cluster Analysis (K-means and Ward) and Self Organizing Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5, ERA5-Land, and CPC). Spatial patterns and time series were generated for each region over the period 1981-2010. The results from this analysis of spatially homogeneous regions concerning temperature and precipitation have the potential to significantly benefit climate analysis and forecasts. Moreover, they can offer valuable insights for various climatological studies, guiding decision-making processes in diverse fields that rely on climate information, such as agriculture, disaster management, and water resources planning.

Review and Analysis: United States Cluster Munitions and Unexploded Ordnance Left in Laos after the Second Indochina War

Cluster munitions release dozens of smaller bomblets that rain deadly ammunition on armored tanks, vegetation and troops, effectively striking broad areas of war zone landscapes in one launch. However, only about 60% of bomblets detonate immediately and those that fail to detonate fall to the ground and can lie dormant for years. The legacy of cluster munitions in Laos from the Second Indochina War is unexploded bomblets across the landscape that unexpectedly detonate years later, injuring and killing children, farmers, and other civilians long after the war is over. In Laos, the United States (US) military operation against the Ho Chi Minh Trail, a network of foot and bicycle paths, waterways, and truck routes along the Laos, Cambodia and Vietnam borders linking North and South Vietnam began in 1959. By the 1960s, as the war escalated, trail traffic was interdicted frequently by CIA and US Air Force using tactical herbicide spraying to defoliate dense vegetation and bombing to disrupt supplies and North Vietnamese troops dispersed along the 16,000-kilometer trail. Unexploded ordinance (UXO), including cluster munitions, from U.S. bombings continued in recent years to detonate, kill, maim and injure Laotians and render agricultural lands too hazardous to cultivate. The primary objectives of this study are to document: 1) the long-term consequences and impacts of the US Air Force bombing of Laos during the Second Indochina War (1959 to 1973);2) the United States removal of unexploded ordnance and cluster munitions;and 3) worldwide relief efforts to help the Laotians maimed by unexploded ordnance and cluster munitions.

A New Hybrid Hierarchical Parallel Algorithm to Enhance the Performance of Large-Scale Structural Analysis Based on Heterogeneous Multicore Clusters

Heterogeneous multicore clusters are becoming more popular for high-performance computing due to their great computing power and cost-to-performance effectiveness nowadays.Nevertheless,parallel efficiency degradation is still a problem in large-scale structural analysis based on heterogeneousmulticore clusters.To solve it,a hybrid hierarchical parallel algorithm(HHPA)is proposed on the basis of the conventional domain decomposition algorithm(CDDA)and the parallel sparse solver.In this new algorithm,a three-layer parallelization of the computational procedure is introduced to enable the separation of the communication of inter-nodes,heterogeneous-core-groups(HCGs)and inside-heterogeneous-core-groups through mapping computing tasks to various hardware layers.This approach can not only achieve load balancing at different layers efficiently but can also improve the communication rate significantly through hierarchical communication.Additionally,the proposed hybrid parallel approach in this article can reduce the interface equation size and further reduce the solution time,which can make up for the shortcoming of growing communication overheads with the increase of interface equation size when employing CDDA.Moreover,the distributed sparse storage of a large amount of data is introduced to improve memory access.By solving benchmark instances on the Shenwei-Taihuzhiguang supercomputer,the results show that the proposed method can obtain higher speedup and parallel efficiency compared with CDDA and more superior extensibility of parallel partition compared with the two-level parallel computing algorithm(TPCA).

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.