Does Biochar Addition Influence the Change Points of Soil Phosphorus Leaching?

Phosphorus change point indicating the threshold related to P leaching, largely depends on soil properties. Increasing data have shown that biochar addition can improve soil retention capacity of ions. However, we have known little about weather biochar amendment influence the change point of P leaching. In this study, two soils added with 0, 5, 10, 20, and 50 g biochar kg-1 were incubated at 25℃ for 14 d following adjusting the soil moisture to 50% water-holding capacity （WHC）. The soils with different available P values were then obtained by adding a series of KH2PO4 solution （ranging from 0 to 600 mg P kg-1 soil）, and subjecting to three cycles of drying and rewetting. The results showed that biochar addition significantly lifted the P change points in the tested soils, together with changes in soil pH, organic C, Olen-P and CaC12-P but little on exchangeable Ca and Mg, oxalate-extractable Fe and Al. The Olsen-P at the change points ranged from 48.65 to 185.07 mg kg-1 in the alluvial soil and 71.25 to 98.65 mg kg^-1 in the red soil, corresponding to CaCl2-P of 0.31-6.49 and 0.18-0.45 mg L~, respectively. The change points of the alluvial soil were readily changed by adding biochar compared with that of the red soil. The enhancement of change points was likely to be explained as the improvement of phosphate retention ability in the biochar-added soils.

RATE OF CONVERGENCE FOR MULTIPLE CHANGEPOINTS ESTIMATION OF MOVING-AVERAGE PROCESSES

In this paper, the least square estimator in the problem of multiple change points estimation is studied. Here, the moving-average processes of ALNQD sequence in the mean shifts are discussed. When the number of change points is known, the rate of convergence of change-points estimation is derived. The result is also true for p-mixing, φ-mixing, a-mixing, associated and negatively associated sequences under suitable conditions.

基于XGBoost建模和Change-Point残差处理的风电机组齿轮箱温度预警

风电机组具有单机容量小,台数多,状态信息复杂的特点;基于SCADA数据的风电机组齿轮箱预警普遍存在预测精度和计算效率低,资源占用高的问题,选取有效方法以克服上述问题成为齿轮箱预警的关键。极限梯度提升(eXtreme Gradient Boosting,XGBoost)算法本身结合多线程、数据压缩、分片的形式,其在处理大数据量样本时仍具有较高的通用性。采用XGBoost算法建立齿轮箱正常工作状态的温度模型,使用XGBoost模型与其它4类回归预测模型分别进行齿轮箱温度预测模型实验,结果表明XGBoost模型在齿轮箱温度预测中综合性能要优于其它4类模型。当齿轮箱发生故障时,模型的样本特性会发生异常变化,导致模型的预测残差发生明显改变。为提高齿轮箱故障预警的可靠性,构建了新的Change-Point算法,利用Change-Point技术进行XGBoost模型预测残差序列分析,可定位齿轮箱状态劣化时间点,回溯状态劣化原因。

Sedimentation and morphological changes at Yuantuojiao Point, estuary of the North Branch, Changjiang River

The North Branch, separated by the Chongming Island, was once the main channel in the estuary of the Changjiang River. Reclamation and a decrease in runoffto the North Branch had led to the narrowing and shallowing of the channel. The Yuantuojiao Point is located at the intersecting point connecting the North Branch of the Changjiang River and the Jiangsu coastline. Erosion cliffs are developed between the typical silty-muddy tidal flat and the salt marsh occupied by Spartina alterniflorea, and this has changed rapidly over the past few years. The sediment grain size analysis results of the surficial and two core samples indi- cate that the Yuantuoiiao Point tidal fiat experienced continuous accretional processes. Based upon 137Cs analysis results of the YT and YY Cores sampled from the tidal flat at the Yuantuojiao Point, the average sed- imentation rate of the YT Core was 2.30 cm/a from 1963 to 2007, and 2.38 cm/a from 1954 to 2007 for the YY Core. The sedimentation rates of both core locations have declined since the 1960s corresponding to the seaward reclamation at the Yuantuojiao Point. The average sedimentation rates at the Yuantnojiao Point were similar to that of the silty-muddy tidal flat at the northern ]iangsu coast, but lower than that of the south of the Changjiang River Estuary. According to field morphological investigations from 2006 to 2008 on the salt marsh at the Yuantuojiao Point, cliffs retreated markedly by storm surges and disappeared gradu- ally because of the rapid sedimentation on the silty-muddy tidal flat. The maximum annual retreat reached 10 m. The recent sedimentation and morphological changes of the Yuantuojiao Point tidal flat not only displayed the retreat of the salt marsh and the disappearance of cliffs, but also was accompanied by rapid sedimentation of the silty-muddy tidal flat and the salt marsh, indicating the responses to the tidal currents, storm surges, Spartina alterniflorea trapping sediments and large-scale reclamation. The sediment grain size and their trends, southward coastal flow, and sandspits of the longshore bars suggest that the main sediment source at the Yuantuoijao Point, estuary of the North Branch was possibly from the Changjiang River before 1958, since then, it has been from the south of the submarine radial sand ridges of the southern Huanghai Sea （Yellow Sea）.

Discrete Software Reliability Growth Modeling for Errors of Different Severity Incorporating Change-point Concept

Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

Analysis of Change Point in Surface Temperature Time Series Using Cumulative Sum Chart and Bootstrapping for Asansol Weather Observation Station, West Bengal, India

This paper aims to detect the short-term as well as long-term change point in the surface air temperature time series for Asansol weather observation station, West Bengal, India. Temperature data for the period from 1941 to 2010 of the said weather observatory have been collected from Indian Meteorological Department, Kolkata. Variations and trends of annual mean temperature, annual mean maximum temperature and annual minimum temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping were used for the detection of abrupt changes in the time series data set. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperatures occurred around 1986 (0.57°C) at the period of 25 years in the long-term regional scale. On the other side, the annual mean maximum and annual mean minimum temperatures have distinct change points at level 1. There are abrupt changes in the year 1961 (Confidence interval 1961, 1963) for the annual mean maximum and 1994 (Confidence interval 1993, 1996) for the annual mean minimum temperatures at a confidence level of 100% and 98%, respectively. Before the change, the annual mean maximum and annual mean minimum temperatures were 30.90°C and 23.99°C, respectively, while after the change, the temperatures became 33.93°C and 24.84°C, respectively. Over the entire period of consideration (1941-2010), 11 forward and backward changes were found in total. Out of 11, there are 3 changes (1961, 1986 and 2001) in annual mean temperatures, 4 changes (1957, 1961, 1980 and 1994) in annual mean maximum temperatures, and rest 4 changes (1968, 1981, 1994 and 2001) are associated with annual mean minimum temperature data set.

A Note on Change Point Detection Using Weighted Least Square

This paper is concerned with the application of weighted least square method in change point analysis. Testing shift in the mean normal observations with time varying variances as well as of a GARCH time series are considered. For both cases, the weighted estimators are given and their asymptotic behaviors are studied. It is also described that how the resampling methods like Monte Carlo and bootstrap may be applied to compute the finite sample behavior of estimators.

Environmental changes affect picoplanktonic composition in Antarctic Peninsula ponds

Antarctic Peninsula is experiencing one of the largest global warming events worldwide.Shallow water bodies generated by the melting of snow in summer are numerous,and they might act as sentinels of climate change due to their rapid response and ability to integrate catchment information.Shifts in climate can influence the structure of microbial communities which dominate these freshwaters ecosystems.Here,we characterize three ponds at Cierva Point(Antarctic Peninsula)by examining their physico-chemical and morphological characteristics and we explored how different factors modify the structure of the microbial community.We studied the abundance and biomass of heterotrophic bacteria,picocyanobacteria and picoeukaryote algae during January and February of two consecutive summers(2017 and 2018).We found that ponds had different limnological characteristics,due to their location,geomorphological features and presence of the surrounding flora and fauna.Physico-chemical parameters as well as microbial community differed between ponds,months and years.In 2017,most ponds were oligo to mesotrophic states.The larger accumulated rainfall(as a result of environmental changes on the Antarctic Peninsula)during 2018,particularly in February,causes nutrient runoff into water bodies.This affects those ponds with the highest seabird circulation,such as gentoo penguin,increasing eutrophication.As a result,picoplanktonic abundances were higher,and the community structure shifts to a largely heterotrophic bacteria dominated one.These results suggest that these communities could act as sentinels to environmental changes,anticipating a future with mostly hypertrophic ponds.

On-line outlier and change point detection for time series

The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.

Spatio-Temporal Change of Dispersal Areas of Greater Kudu (Tragelaphus strepsiceros) in Lake Bogoria Landscape, Kenya

Decline in wildlife populations is manifest globally, regionally and locally. A wildlife decline of 68% has been reported in Kenya’s rangelands with Baringo County experiencing more than 85% wildlife loss in the last four decades. Greater Kudu (Tragelaphus strepsiceros) is endemic to Lake Bogoria landscape in Baringo County and constitutes a major tourist attraction for the region necessitating use of its photo on the County’s logo and thus a flagship species. Tourism plays a central role in Baringo County’s economy and is a major source of potential growth and employment creation. The study was carried out to assess spatio-temporal change of dispersal areas of Greater Kudu (GK) in Lake Bogoria landscape in the last four years for enhanced adaptive management and improved livelihoods. GK population distribution primary data collected in December 2022 and secondary data acquired from Lake Bogoria National Game Reserve (LBNGR) for 2019 and 2020 were digitized using in a Geographic Information System (GIS). Measures of dispersion and point pattern analysis (PPA) were used to analyze dispersal of GK population using GIS. Spatio-temporal change of GK dispersal in LBNR was evident thus the null hypothesis was rejected. It is recommended that anthropogenic activities contributing to GK’s habitat degradation be curbed by providing alternative livelihood sources and promoting community adoption of sustainable technologies for improved livelihoods.

Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.

Consistency of the <i>φ</i>-Divergence Based Change Point Estimator

This paper utilizes a change-point estimator based on the φ-divergence. Since we seek a near perfect translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of stationary model is too restrictive especially for long time series. The estimator is shown to be consistent through asymptotic theory and finally proven through simulations. The estimator is applied to the generalized Pareto distribution to estimate changes in the scale and shape parameters.

Performance assisted enhancement based on change point detection and Kalman filtering

A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contamination" caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.

The SCUSUM Estimation of Variance Change Point in Linear Process

This paper considers the estimation problem of a variance change-point in linear process.Consistency of a SCUSUM type change-point estimator is proved and its rate of convergence is established.The mean-unknown case is also considered.

基于Change-Point的风电数据挖掘算法研究

风电机组运行过程中,一些故障导致设备状态发生改变,状态的改变发生在一个持续的时间序列中,找到变化点的时间对于故障回溯及根本原因分析具有重要价值。该文研究风电信号及状态时序变化的特点,引入统计学中的Change-Point算法,通过划分不同置信区间求取置信度方法解决奇异变点的不确定度问题。通过实验对算法进行验证,得出以下结论:Change-Point算法能够有效挖掘到历史数据中的一维及二维模型数据的变化,并给出变点;Change-Point算法思想是挖掘出数据本身的规律性,不受其他条件限制,因此可广泛应用于风电机组数据采集与监视控制(supervisory control and data acquisition,SCADA)系统变量数据挖掘中的问题回溯,快速定位SCADA数据状态变化点。

The Role of Social Change Factors in the Criminal Saturation Law

In the crime saturation law,the crime rate of a country depends on the specific time and space conditions of the country,and its distribution and size are limited to the“saturation”of social conditions.Therefore,the process of social change is the process of the criminial rate change.First,based on the regression analysis of the longitudinal crime rate data and relevant social development data in China from 1981 to 2015,this paper searches for the cointegration relationship between the variable sequences.Second,by the Quandt-Andrews breakpoint test,this paper finds the potential structural breakpoints within the sample interval.Finally,based on the structural breakpoints,this paper compares and analyzes the regression results between the sub regions,the sub intervals and overall sample intervals.The study finds that the increase of per capita GDP level,the widening income gap between urban and rural areas,the decline of social education level and the increase of unemployment rate all contribute to the growth of crime rate.China’s relevant departments should implement measures to maintain stability in the period of rapid economic growth,pay attention to social equity problems,increase investment in education,and reduce social unemployment rate.

Time Sequence Change-Point Model of Electrostatic State Parameters of Aircraft Engine

Electrostatic monitoring technology of particle charging information can facilitate online monitoring of aero-engine,which effectively enhances engine fault diagnosis and health managements.Unlike traditional engine state monitoring technologies,aircraft engine monitoring by gas path electrostatic monitoring not only covers the predicted information source itself,but also detects the information that can provide an early warnings for initial fault states through gas path charging levels.This paper establishes a non-stationary time sequence change-point model for anomaly recognition of electrostatic signals based on change-point theory combined with difference method of non-stationary time series.Finally,electrostatic induction data were utilized by the engine life test for a particular aircraft to validate the proposed algorithm.The results indicate that the activity level and the event rate were0.5—0.8(nc)and 50%,respectively,which were far greater than 4—12(pc)and 0—4% under normal working conditions of the engine.

Damage Position of Concrete Beam Based on Test of Even Change Point

The quantitative analysis of damage position of concrete beam is established.The method of damage position free of models used in this paper is based on the variations of strain mode and test of even change point.The discrete model and the position criterion are presented.Numerical example is conducted,the result demonstrates that the method of damage position is correct and effective.

Recognition Method for Change Point of Traffic Flow Linear Regressions

Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the field data of Liantong Road of Zibo city to verify the validity and the feasibility of the theory. The results show that change point method of multiple linear regression can make out the rule of quantitative changes in traffic flow more accurately than ordinary methods. So, the change point method can be applied to traffic information management system more effectively.