Centroid Values Research Articles

MAPREDUCE FUZZY C-MEANS ENSEMBLE CLUSTERING WITH GENTLE ADABOOST FOR BIG DATA ANALYTICS

Big data clustering is one of the significant processes employed in numerous application domains. Existing clustering algorithms do not cope with large-scale data, resulting in higher false positive rate. In order to cluster such large datasets with higher accuracy, MapReduce gradient descent gentle AdaBoost clustering (MGDGAC) technique is proposed. The MGDGAC technique designs MapReduce fuzzy C-means (MFCM) clustering where the large dataset is initially subdivided into a number of chunks which are executed in parallel on different nodes to effectively perform clustering processes with minimal time. The data with larger membership value are grouped in the cluster with help of mappers. Then, reducer in MFCM clustering re-estimates the centroid value and iteratively fed to the mapper again until it attains a particular iteration and groups the similar data together. Finally, MGDGAC technique applies gentle AdaBoost with intention of reducing the training error of large data clustering.

Detection of Chemically Matured Mango Fruits Using Laplacian Descriptors and Scale Determinants

Nowadays machine vision systems are largely used for Fruit harvesting, fruit tracking, fruit quality assessment, and detection of artificial ripening of fruits. Existing research concentrates on shape-based fruit harvesting using robots, 3D reconstruction of images for in field fruit tracking systems, non – destructive methods for quality assessment of fruits, and detection of artificially ripened fruits. Besides shape, the color of the fruit is a unique and protruding feature. Though a lot of effort is focused on harvesting, tracking, and quality assessment of mango fruits using these features, fewer efforts are made in the path of the identification of artificially matured mango fruits. Simpler, robust, and easily accessible solutions are desired by exploiting both RGB descriptors and feature point locators. Here a new context for detection of artificially matured mango fruit based on SURF descriptors is proposed. In particular, Feature Point Location (FPL) centroid values, approximated Laplacian matrix values, and Scale determinant values are used to identify artificial matured mango fruit. The results of the experiment are conducted on two different datasets which demonstrates the efficiency of the method proposed.

Improved point center algorithm for K-Means clustering to increase software defect prediction

The k-means is a clustering algorithm that is often and easy to use. This algorithm is susceptible to randomly chosen centroid points so that it cannot produce optimal results. This research aimed to improve the k-means algorithm’s performance by applying a proposed algorithm called point center. The proposed algorithm overcame the random centroid value in k-means and then applied it to predict software defects modules’ errors. The point center algorithm was proposed to determine the initial centroid value for the k-means algorithm optimization. Then, the selection of X and Y variables determined the cluster center members. The ten datasets were used to perform the testing, of which nine datasets were used for predicting software defects. The proposed center point algorithm showed the lowest errors. It also improved the k-means algorithm’s performance by an average of 12.82% cluster errors in the software compared to the centroid value obtained randomly on the simple k-means algorithm. The findings are beneficial and contribute to developing a clustering model to handle data, such as to predict software defect modules more accurately.

Hole trapping capability of silicon carbonitride charge trap layers

We have evaluated the hole trapping capability of the silicon carbonitride (SiCN) dielectric film for application in metal-oxide-nitride-oxide-silicon (MONOS)-type non-volatile memory devices. After a great number of holes were injected to the SiCN charge trap layer of memory capacitors at high applied voltages, the flat-band voltage shift ΔV fb,h of the capacitors was saturated and the charge centroid location of holes trapped in the SiCN layer was found to reach at 1.8–2.0 nm from the blocking oxide-charge trap layer interface. Using the obtained ΔV fb,h and charge centroid values, the maximum density of holes trapped in the SiCN layer was estimated to be 1.2 × 1013 holes/cm2, which was higher than that trapped in a silicon nitride charge trap layer (=1.0 × 1013 holes/cm2). It is concluded that the high density of trapped holes caused large ΔV fb,h in the memory capacitors with the SiCN layer.

K-Means Clustering for Determining Quality of Outdoor Temperature Based on BMKG Datasets

The purpose of this study was to determine the quality of outdoor temperatures that are good for conducting activities. One of the factors that cause the body's healthy or not human temperature, either indoors or outdoors. The data used is the BMKG weather dataset. BMKG weather dataset is a type of quantitative data because it is numeric or nominal and can be calculated. At each day, BMKG also provides information about the temperature according to the state of each area. It is starting from the minimum temperature, maximum, average humidity, to the maximum wind speed. The method used is clustering using the k-means clustering with centroid value. This research resulted in outdoor temperature clustering, which is good, INTERMEDIATE, and bad quality. In the range of values 124.7 indicate good temperature, range values 133.1 indicate moderate temperature, range of values 146.8 indicate bad temperature. Based on research in 32 days produced 28 days of moderate temperature quality, two days of good temperature quality, and two days of poor quality.

The Importance of the Northeastern Gulf of Mexico to Foraging Loggerhead Sea Turtles

Identification of high-use foraging sites where imperiled sea turtles are resident remains a globally-recognized conservation priority. In the biodiverse Gulf of Mexico (GoM), recent telemetry studies highlighted post-nesting foraging sites for federally threatened loggerhead turtles (Caretta caretta). Our aim here was to discern loggerhead use of additional northern GoM regions that may serve as high-use foraging sites. Thus, we used satellite tracking and switching state-space modeling to show that the Big Bend region off the northwest Florida coast is a coastal foraging area that supports imperiled adult female loggerhead turtles tracked from different nesting subpopulations. From 2011 – 2016, we satellite-tagged 15 loggerheads that nested on four distinct beaches around the GoM: Dry Tortugas National Park, FL; Everglades National Park, FL; St. Joseph Peninsula, FL; and Gulf Shores, AL. Turtles arrived at their foraging ground in the Big Bend region between June and September and remained resident in their respective foraging sites for an average of 198 tracking days, where they established mean home ranges (95% kernel density estimate) 232.7 km2. Larger home ranges were in deeper water; 50% kernel density estimate centroid values were a mean 26.4 m deep and 52.7 km from shore. The Big Bend region provides a wide area of suitable year-round foraging habitat for loggerheads from at least 3 different nesting subpopulations. Understanding where and when threatened loggerheads forage and remain resident is key for designing both surveys of foraging resources and additional protection strategies that can impact population recovery trajectories for this imperiled species.

Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

A problem that appears in marketing activities is how to identify potential customers. Marketing activities could identify their best customer through customer segmentation by applying the concept of Data Mining and Customer Relationship Management (CRM). This paper presents the Data Mining process by combining the RFM model with K-Means, K-Medoids, and DBSCAN algorithms. This paper analyzes 334,641 transaction data and converts them to 1661 Recency, Frequency, and Monetary (RFM) data lines to identify potential customers. The K-Means, K-Medoids, and DBSCAN algorithms are very sensitive for initializing the cluster center because it is done randomly. Clustering is done by using two to six clusters. The trial process in the K-Means and K-Medoids Method is done using random centroid values ??and at DBSCAN is done using random Epsilon and Min Points, so that a cluster group is obtained that produces potential customers. Cluster validation completes using the Davies-Bouldin Index and Silhouette Index methods. The result showed that K-Means had the best level of validity than K-Medoids and DBSCAN, where the Davies-Bouldin Index yield was 0,33009058, and the Silhouette Index yield was 0,912671056. The best number of clusters produced using the Davies Bouldin Index and Silhouette Index are 2 clusters, where each K-Means, K-Medoids, and DBSCAN algorithms provide the Dormant and Golden customer classes.

Functional near-infrared spectroscopy (fNIRS) as a tool to assist the diagnosis of major psychiatric disorders in a Chinese population.

Advances in neuroimaging have promised the development of specific and objective biomarkers for the diagnosis and treatment of psychiatric disorders. Recently, functional near-infrared spectroscopy (fNIRS) has been used during cognitive tasks to measure cortical dysfunction associated with mental illnesses such as Schizophrenia (SCH), Major-Depressive disorder (MD) and Bipolar Disorder (BD). We investigated the ability of fNIRS as a clinically viable tool to successfully distinguish healthy individuals from those with major psychiatric disorders. 316 patients with major psychiatric disorders (198 SCH/54 MD/64 BP) and 101 healthy controls were included in this study. Changes in oxygenated-hemoglobin during a Chinese language verbal fluency test were measured using a 52-channel fNIRS machine over the bilateral temporal and frontal lobe areas. We evaluated the ability of two task-evoked features selected from prior studies the Integral and Centroid values, to identify individuals with major diagnoses. Both the integral value of frontal and centroid value of temporal showed sensitivity in classifying individuals with mental disorders from healthy controls. However, using a combined index featuring both the integral value and centroid value to differentiate psychiatric disorders from healthy controls with an AUC of 0.913, differentiate individuals with mood disorders from healthy controls showed an AUC of 0.899, while for schizophrenia the AUC was 0.737. Our data suggest that fNIRS can be used as a candidate biomarker during differential diagnosis individuals with mood or psychosis disorders and offer a step towards individualization of treatment.

Outcome of toric intraocular lenses implanted in eyes with previous corneal refractive surgery.

To evaluate the outcome of toric intraocular lens (IOL) implantation in eyes with previous laser in situ keratomileusis/photorefractive keratectomy (LASIK/PRK). Cullen Eye Institute, Baylor College of Medicine, Houston, Texas, USA. Retrospective case series. Consecutive cases that had previous myopic or hyperopic LASIK/PRK and had undergone cataract surgery with toric IOL implantation were retrospectively reviewed. Included were eyes that had (1) preoperative ocular biometry measurements with the Lenstar, (2) no intraoperative or postoperative complications, and (3) available postoperative manifest refraction at ≥3 weeks with corrected distance visual acuity of 20/30 or better. Vector analysis was used to assess the preoperative corneal and postoperative refractive astigmatism. In 56 eyes with previous myopic LASIK/PRK and 19 eyes with previous hyperopic LASIK/PRK, respectively, the mean magnitudes of corneal astigmatism were 1.34 ± 0.62 diopters (D) and 1.66 ± 0.80 D, 5% and 0% of eyes had anterior corneal astigmatism ≤0.50 D, and the centroid values were 0.31 D at 19 degrees ± 1.45 D and 0.74 D at 92 degrees ± 1.72 D preoperatively. Postoperatively, the mean magnitudes of refractive astigmatism were 0.36 ± 0.31 D and 0.34 ± 0.34 D, 80% and 84% of eyes had refractive astigmatism of ≤0.50 D, and the centroid values were 0.12 D at 152 degrees ± 0.46 D and 0.05 D at 172 degrees ± 0.48 D (all P < .05). Toric IOLs were effective to correct preexisting corneal astigmatism in eyes with previous excimer laser corneal refractive surgery.

Microseismic Noise before and after Strong Earthquakes: Case Study of Chilean Subduction Zone

Abstract—In this work, we study the parameters of microseismic noise in the vicinity of the Chilean subduction zone in order to detect oscillations of a block–fault system, similar to those identified by processing the results of the previous laboratory experiment. The analysis is based on the data recorded by the broadband seismic stations of the international seismic network IRIS. We calculated the spectral parameters of microseismic noise before and after several earthquakes. It is established that before the earthquakes with Мw ≥ 8, the calculated value of spectral centroid decreases by 0.12–0.26 Hz. The decrease in the value of spectral centroid in the range from 0.008 to 0.45 Hz can be as long as up to two days. The proposed approach can be a new instrument useful for real-time monitoring of active faults of various scales.

Reinforcement of brands of faith with the paradox of cultural divergence in Indian perspective

PurposeSelf-esteem values, with the new art of living, in the minds of Indians, lead to establish faith among the spiritual organization. Later on, the spiritual organizations brand their names and market the products in their branded name. These brands, which are inspired by faith and created by Indian spiritual gurus, have even disrupted the fast-moving consumer goods (FMCG) market by being customer-centric instead of being geared by lucrative returns. It is in this context that this paper aims to find the cultural divergence factors that lead to change the consumption pattern of FMCG and how such brands of faith have been segmented in the Indian perspective by spiritual gurus. The research concludes that cultural divergence variables such as power distance, collectivism, uncertainty avoidance and long-term orientation influence the brands that are inspired by faith. Spiritual gurus in India are using sociocultural marketing activities such as social endorsement and cause-related marketing strategies for segmenting the markets.Design/methodology/approachThe primary data were collected from the 1,678 customers of the Isha products. The respondents were selected based on the snowball sampling. The responses were collected from the followers who visited the foundation at least three times during the period of two years in Coimbatore and purchased Isha products worth more than Rs 500. The data were collected between the period August 2016 and April 2018. Of 1,678 responses, 1,465 responses were validated after coding. Of 1,465 responses that were validated, 1,126 responses were found reliable.Findings“Cause-related marketing” and “social group endorsement” activities of the firms tend to create a brand image. To find out which of the above activities highly influence the brands of faith, realistic-operating characteristic (ROC) analysis was carried out. ROC curves were drawn to assess the brands of faith centroid values with social group endorsement and cause-related marketing variables. ROC curves explored the precision of diagnostic tests and were used to find the best “cut-off” value for impressive and unimpressive cluster test results.Research limitations/implicationsCultural divergence variables such as power distance, collectivism, uncertainty avoidance and long-term orientation are influencing the brands of faith. The followers of the spiritual foundation have their own distinct culture, and their social affinity values increase the brands of faith. Social group endorsement and cause-related marketing are the marketing strategies suitable for spiritual foundation (to market their product/ service). Age, education and occupation are demographic values that influence the brands of faith. The spiritual foundations are segmenting their customers based on the occupational values, and they use the cause-related marketing strategies to increase the values of brands of faith.Practical implicationsAs the cultural values related to the art of living have been recognized by society as the measure of social well-being, the spiritual leaders can enhance their brands of faith. The social media communication about cause-related marketing can create trust in society. On the other hand, societal marketing activities cannot target the entire society. Hence, it is to be stratified. While stratifying, the players of diversity markets have to target a group based on the values generated by the stakeholders in the foundation. The diversified markets created by the Indian spiritual gurus are providing cultural diversity.Social implicationsThe business value created by spiritual foundations is increasing the social values which are essential to uplift society. The author concludes that if business values and societal values are integrated by any group of people, it improves economic value to that society and they can use the social currency in the form of “brands of faith”.Originality/valueThe cultural values of a society are measured and compared with national and global index. The enumerate method is an original one.

Rating the Participation in Demand Response Programs for a More Accurate Aggregated Schedule of Consumers after Enrolment Period

Aggregation of small size consumers and Distributed Generation (DG) units have a considerable impact to catch the full flexibility potential, in the context of Demand Response programs. New incentive mechanisms are needed to remunerate consumers adequately and to recognize the ones that have more reliable participation. The authors propose an innovative approach to be used in the operation phase, to deal with the uncertainty to Demand Response events, where a certain target is requested for an energy community managed by the Aggregator. The innovative content deals with assigning and updating a Reliability Rate to each consumer according to the actual response in a reduction request. Three distinct methods have been implemented and compared. The initial rates assigned according to participation in the Demand Response events after one month of the enrolment period and the ones with higher reliability follow scheduling, performed using linear optimization. The results prove that using the proposed approach, the energy community manager finds the more reliable consumers in each period, and the reduction target achieved in DR events. A clustering algorithm is implemented to determine the final consumer rate for one month considering the centroid value.

Applied optical flow algorithm (OFA) in automatic target tracker system to control weapon movements in the warship

This research has made a tracking system to track the target, the purpose of this prototype is being able to be applied in the warship cannon guns. The system principal is tracking the targets that have been chosen to be targeted by the operator. This device works with the camera as the main sensor, the camera works to provide visual data, the visual data as an object was selected and processed to be targeted. The visual data is processed using the Optical Flow Algorithm (OFA) method by calculating the brightness intensity of the reference. The next step is finding the centroid value as the centre point for distance measurement. These results were converted into a microcontroller program that will move the direction of the tracker to follow the target which captured by the camera. The results of the object tracking test on this tool from 10 attempts can detect objects with an average accuracy of 100 percent at a distance of 1 to 9 meters. For testing the target lock, this system can reach the set point value within 2 seconds with the steady state error of 0.625 percent and the selection of the best proportional constant of 0.2.

Dynamic K-Means Clustering of Workload and Cloud Resource Configuration for Cloud Elastic Model

Cloud elasticity involves timely provisioning and de-provisioning of computing resources and adjusting resources size to meet the dynamic workload demand. This requires fast, and accurate resource scaling methods at minimum cost (e.g. pay as you go) that match with workload demands. Two dynamic changing parameters must be defined in an elastic model, the workload resource demand classes, and the data center resource reconfiguration classes. These parameters are not labeled for cloud management system while data center logs are being captured. Building an advance elastic model is a critical task, which defines multiple classes under these two categories i.e. for workload and for provisioning. A dynamic method is therefore required to define (during configuration time window) the workload classes and resource provisioning classes. Unsupervised learning model such as K-Means has many challenges such as time complexity, selection of optimum number of clusters (representing the classes), and determining centroid values of the clusters. All clustering methods depend on minimizing mean square error between center of population in same class member. These methods are often enhanced using guidelines to find out the centroids, but they suffer from K-Means limitations. For the application of clustering cloud log traces, most of the reported work use K-Means clustering to label workload types. However, there is no work reported that label data center scaling classes. In this work, a novel method is proposed to analyze the characteristics of both workloads and datacenter configurations using clustering method, and is based on random variable model transformation (kernel density estimator) guide. This method enhances K-Means clustering by automatically determining optimum number of classes and finding the mean centroids for the clusters. In addition, it improves the accuracy and the time complexity of standard K-Means clustering model, by best correlating between clustering attributes using statistical correlation methods.

Modified ACS Centroid Memory for Data Clustering

Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm’s memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant’s movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.

Segmentation of Fetal Images using Kernel Fuzzy C Means Clustering with Whale Optimisation Algorithm

Image segmentation is considered as a critical process in medical imaging that are facilitated using automated computation. The segmentation process partitions the images into subsets based on its location or intensity. However, segmentation of fetal images faces poor segmentation due to the presence of noise and poor spatial intensities. In this paper, the study decomposes the fetal image into several parts for the purpose of segmentation and then performs the change in representations. The segmentation process is improved in this method using Kernel Fuzzy C Means (KFCM) based Whale Optimisation Algorithm (WOA). The segmentation process uses modified KFCM, where the centroid values are estimated using WOA. The segmentation method segments the input fetal image into appropriate regions using KFCM-WOA. The simulation result shows that the proposed method attains improved performance than other kernel based methods. The results of the performance metrics shows that the proposed method attains a sensitivity of 99.8273%, specificity of 99.7350%, accuracy of 99.9385%, positive predictive value (PPV) of 99.3964, Negative Predictive value (NPV) of 0.3805, Dice Coefficient of 48.5518, Rand Index (RI) of 0.9983 and Global consistency error (GCE) of 0.0460, which are higher than other kernel based methods.

Clustering Student's Satisfaction in Complex Adaptive Blended Learning with the Six Value System Using the K-Means Algorithm

Complex adaptive blended learning with the six value system is expected to improve the quality of learning. It is also expected to improve high order thinking skills. But the level of student’s satisfaction in Complex adaptive blended learning with the six value system varies in results. This study aims to determine the grouping of student’s satisfaction in complex adaptive blended learning with the six value system. The object of the study was conducted in 3 (three) vocational high schools (SMK) in Cirebon City, West Java Province, Indonesia for digital simulation subjects with a purposive sample of 150 students. Data about student’s satisfaction is grouped using the K-Means algorithm with the optimization of generation method. Several stages are carried out in grouping student’s satisfaction, starting with randomly determining initial centroid values. The K-Means algorithm process ends if there is no change in centroid value between one iteration and another iteration. Furthermore, performance measurements are performed using the Cluster Distance Performance method. The results are obtained by the performance Vector with parameters K = 4. The average distance in the centroid is -0.107, the average distance in the center of cluster 0 is -0.109, the average distance in the center of cluster 1 is -0.109, the average distance in the center of cluster 2 of -0,100, and the average distance in the center of cluster 3 is -0,106, with Davies Bouldin index of -1,049.

AnalisaTingkat Kepuasan Mahasiswa Terhadap Layanan Pembelajaran Menggunakan K-Means dan Algoritma Genetika

The level of student satisfaction with learning services in higher education is one factor in the quality of college learning. To determine the level of student satisfaction with learning services in higher education, it is necessary to analyze the level of student satisfaction with learning services. K-Means method is a technique of grouping data based on the level of similarity of each member. K-Means can be used to classify the student satisfaction index on learning services. The K-Means method can also be optimized with genetic algorithms to determine the best centroid value. K-means optimization with Genetic Algorithms can be used as a technique to determine the level of student satisfaction with learning services. Obtained by Davies Bouldien Index from the K-Means and Genetics method is 1.593 with cluster number 5

Penetapan Instruktur Diklat Menggunakan Metode Clustering K-Means dan Topsis Pada PT PLN (Persero) Udiklat Jakarta

Instructor is the main aspect that exists in the implementation of the training. The increasing number of instructors and the need for training is also increasing every year there is no system that can help the process of determining quickly and precisely. In need of a method that can classify the instructor data in accordance with the title of training materials and can be assigned instructor each of the training materials and do not ignore aspects of assessment of the instructor. In this study data mining techniques are used to help recommend instructors for each subject matter of the training based on the cluster data group approach. So it can be used in determining the instructor's assignment per training materials in the future. K-Means clustering method is used to group data into clusters by looking at the centroid value that has been determined. And the Topsis method is used to assign one instructor's name through the rankings of preference values. In this research CRISP-DM method is used as software engineering method system work done in sequence or linearly. In the testing process has been generated if the manual data and data processing if the application system is the same. This application to facilitate the Supervisor and Learning Development staff in setting instructors per training materials.

Earthquake rupture characteristics along a developing transform boundary

SUMMARYThe 280-km-long San Cristobal Trough (SCT), created by the tearing of the Australia plate as it subducts under the Pacific Plate near the Solomon and Vanuatu subduction zones, has hosted strike-slip earthquake sequences in 1993 and 2015. Both sequences, which likely represent a complete seismic cycle, began along the oldest section of the SCT—the portion farthest from the tear that has experienced the most cumulative displacement—and migrated to the younger sections closer to the tear. The SCT's abundant seismicity allows us to study transform boundary development—a process rarely observed along a single fault system—through observations of earthquake rupture properties. Using the spectral ratio method based on empirical Green's functions (EGFs), we calculate the corner frequencies of three Mw ∼7 2015 earthquakes and colocated smaller earthquakes. We utilize two different spectral ratio stacking methods and fit both Brune and Boatwright models to the stacked spectral ratios. Regardless of stacking methods and spectral models, we find that the corner frequencies of the 2015 Mw ∼7 earthquakes decrease slightly with distance from the tear. Assuming a constant rupture velocity and an omega-square spectral model, this corner frequency decrease may be due to an increase in rupture length with distance from the tear. The spectrum of the 2015 earthquake farthest from the tear also deviates from the omega-square model, which may indicate rupture complexity. Stress drop estimates from the corner frequencies of the 2015 Mw ∼7 earthquakes range between 1 and 7 MPa, whereas stress drop estimates of their EGFs range from ∼0.05 to 10 MPa with most values between 0.1 and 1 MPa. Independent evidence from a second moments analysis of the 2015 earthquake sequence also indicates a possible increase in rupture length with distance from the tear, confirming the results from the spectral ratio analysis. We also observe an increase in normalized centroid time-delay values, a first-order proxy for rupture behaviour, with distance from the tear for the 2015 sequence. A similar trend for the 1993 sequence suggests that earthquake rupture varies systematically along the SCT. Since distance from the tear corresponds to cumulative fault displacement, these along-strike rupture variations may be due to a displacement-driven fault maturation process.