scholarly journals Healthy Knee Kinematic Phenotypes Identification Based on a Clustering Data Analysis

2021 ◽  
Vol 11 (24) ◽  
pp. 12054
Author(s):  
Neila Mezghani ◽  
Rayan Soltana ◽  
Youssef Ouakrim ◽  
Alix Cagnin ◽  
Alexandre Fuentes ◽  
...  
Keyword(s):  
Data Analysis ◽  
Knee Kinematics ◽  
Data Representation ◽  
Three Dimensions ◽  
Statistical Hypothesis ◽  
Joint Analysis ◽  
Global Approach ◽  
Flexion Extension ◽  
Healthy Knee ◽  
Clustering Data

The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task. We investigated two data representation approaches that are based on the joint analysis of the three dimensions. The first is a global approach that is considered a concatenation of the kinematic data without any dimensionality reduction. The second is a local approach that is considered a set of 69 biomechanical parameters of interest extracted from the 3D kinematic curves. The data representations are followed by a clustering process, based on the BIRCH (balanced iterative reducing and clustering using hierarchies) discriminant model, to separate 3D knee kinematics into homogeneous groups or clusters. Phenotypes were obtained by averaging those groups. We validated the clusters using inter-cluster correlation and statistical hypothesis tests. The simulation results showed that the global approach is more efficient, and it allows the identification of three descriptive 3D kinematic phenotypes within a healthy knee population.

In Vitro Kinematic Analysis of Single Axis Radius Posterior-Substituting Total Knee Arthroplasty

2020 ◽  
Author(s):  
Paul Arauz ◽  
Yun Peng ◽  
Tiffany Castillo ◽  
Christian Klemt ◽  
Young-Min Kwon
Keyword(s):  
Knee Flexion ◽  
Knee Kinematics ◽  
Lateral Collateral Ligament ◽  
Collateral Ligament ◽  
Flexion Extension ◽  
Healthy Knee ◽  
Joint Biomechanics ◽  
Total Knee ◽  
Intact Knee

AbstractThis is an experimental study. As current posterior-substituting (PS) total knee arthroplasties have been reported to incompletely restore intrinsic joint biomechanics of the healthy knee, the recently designed single axis radius PS knee system was introduced to increase posterior femoral translation and promote ligament isometry. As there is a paucity of data available regarding its ability to replicate healthy knee biomechanics, this study aimed to assess joint and articular contact kinematics as well as ligament isometry of the contemporary single axis radius PS knee system. Implant kinematics were measured from 11 cadaveric knees using an in vitro robotic testing system. In addition, medial collateral ligament (MCL) and lateral collateral ligament (LCL) forces were quantified under simulated functional loads during knee flexion for the contemporary PS knee system. Posterior femoral translation between the intact knee and the single axis radius PS knee system differed significantly (p < 0.05) at 60, 90, and 120 degrees of flexion. The LCL force at 60 degrees (9.06 ± 2.81 N) was significantly lower (p < 0.05) than those at 30, 90, and 120 degrees of flexion, while MCL forces did not differ significantly throughout the range of tested flexion angles. The results from this study suggest that although the contemporary single axis radius PS knee system has the potential to mimic the intact knee kinematics under muscle loading during flexion extension due to its design features, single axis radius PS knee system did not fully replicate posterior femoral translation and ligament isometry of the healthy knee during knee flexion.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

scholarly journals In VivoHealthy Knee Kinematics during Dynamic Full Flexion

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Satoshi Hamai ◽  
Taka-aki Moro-oka ◽  
Nicholas J. Dunbar ◽  
Hiromasa Miura ◽  
Yukihide Iwamoto ◽  
...  
Keyword(s):  
Knee Flexion ◽  
Knee Kinematics ◽  
The Other ◽  
Medial Compartment ◽  
Knee Flexion Angle ◽  
Knee Prostheses ◽  
Healthy Knee ◽  
Total Knee ◽  
Full Flexion ◽  
2D Model

Healthy knee kinematics during dynamic full flexion were evaluated using 3D-to-2D model registration techniques. Continuous knee motions were recorded during full flexion in a lunge from 85° to 150°. Medial and lateral tibiofemoral contacts and femoral internal-external and varus-valgus rotations were analyzed as a function of knee flexion angle. The medial tibiofemoral contact translated anteroposteriorly, but remained on the center of the medial compartment. On the other hand, the lateral tibiofemoral contact translated posteriorly to the edge of the tibial surface at 150° flexion. The femur exhibited external and valgus rotation relative to the tibia over the entire activity and reached 30° external and 5° valgus rotations at 150° flexion. Kinematics’ data during dynamic full flexion may provide important insight as to the designing of high-flexion total knee prostheses.

scholarly journals Striped Data Analysis Framework

2020 ◽  
Vol 245 ◽  
pp. 06042
Author(s):  
Oliver Gutsche ◽  
Igor Mandrichenko
Keyword(s):  
Data Analysis ◽  
Data Storage ◽  
High Energy Physics ◽  
Data Access ◽  
High Energy ◽  
Data Representation ◽  
General Idea ◽  
Common Data Model ◽  
Local File ◽  
Energy Physics

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

scholarly journals IMPLEMENTATION OF CHARACTER EDUCATION IN BUILDING SCHOOL CULTURE

2021 ◽  
Vol 13 (1) ◽  
pp. 11
Author(s):  
Aunurrahman Aunurrahman
Keyword(s):  
Elementary Schools ◽  
Data Analysis ◽  
School Culture ◽  
Character Education ◽  
Three Dimensions ◽  
Disciplinary Culture ◽  
Interactive Analysis ◽  
Qualitative Approaches ◽  
Analysis Technique ◽  
Quantitative Data Analysis

This research aims to find out about the implementation of character education in building school culture. The research was conducted at elementary schools in Sambas Regency with representation of a number of elementary schools from several sub-districts in Sambas Regency. The data were collected by combining quantitative and qualitative approaches, with data collection techniques using interviews, FGDs and questionnaires. The qualitative data analysis used the interactive analysis technique of Miles and Huberman, while the quantitative data analysis used factor analysis and percentage. The results showed that in general the implementation of character education in building school culture in the Sambas Regency elementary schools in several dimensions was well implemented. However, in certain parts, especially in the dimensions of the program and evaluation, it has not been fully actualized as expected. Meanwhile, school culture as a whole has developed well, especially in the dimensions of disciplinary culture, exemplary culture and culture of order and cleanliness. These three dimensions of culture are the fundamental foundation of building school culture. In the disciplinary culture dimension, the biggest contribution is to hold the midday prayer in congregation, while in the dimension of order and cleanliness culture, the biggest contribution is to hold a school ceremony.Key words: Character Education, School Culture

The Implications

2019 ◽  
pp. 106-125
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Data Interpretation ◽  
Data Representation ◽  
Research Data ◽  
Heuristic Inquiry ◽  
Research Experiences ◽  
Doctoral Research ◽  
Data Analyses

This chapter presents current research insights into various forms and representations of the results of self-as-subject data analyses from both expert and doctoral research experiences. Distinctions between heuristic inquiry and autoethnography are highlighted as well as differences between self-as-subject data analysis approaches when compared with convention methods used within other forms of qualitative research. In self-as-subject research, data representation and data interpretation are often also simultaneous or overlap in presentation to illustrate key findings and insights into the phenomenon of inquiry.

Appraisal of Transactional Data Through Visualisation for SMEs

2018 ◽  
pp. 119-153
Author(s):  
Wajid Khan ◽  
Siti Aishah Mohd Selamat ◽  
Manoharan Ramachandran
Keyword(s):  
Data Analysis ◽  
Large Volume ◽  
Analysis Data ◽  
Data Representation ◽  
Pivotal Role ◽  
Multidimensional Data ◽  
Information Visualisation ◽  
Economy Growth ◽  
Interactive Interfaces ◽  
Transactional Data

E-commerce has proven to play a pivotal role in the economy growth. One of the key e-commerce functions is the collection of the vast amount of useful transactional data to help businesses in understanding their consumers' behaviour. With the rapid and large volume of data collected, it is posing a great challenge for businesses to analyse the data on a day-to-day basis. The key issue is not in the generation or collection of data; it is in the manipulation of the collected data to churn out new and insightful information. Information visualisation is an effective tool in converting data into interactive interfaces to unearth hidden trends. It provides a platform to explore the data in a more rapid and intuitive approach. There are several existing techniques to analyse multidimensional data. This chapter seeks to introduce a comprehensive and robust visualisation model and framework for adoption. The visualisation model consists of four major layers, which include acquisition and data analysis, data representation, user and computer interaction, and result storage.

A Primer on Text-Data Analysis

2011 ◽  
pp. 3111-3118 ◽  
Author(s):  
Imad Rahal ◽  
Baoying Wang ◽  
James Schnepf
Keyword(s):  
Information Retrieval ◽  
Data Analysis ◽  
Language Processing ◽  
Information Filtering ◽  
Data Representation ◽  
Research Area ◽  
Research Field ◽  
Text Data ◽  
Document Collections ◽  
Text Data Analysis

Since the invention of the printing press, text has been the predominate mode for collecting, storing and disseminating a vast, rich range of information. With the unprecedented increase of electronic storage and dissemination, document collections have grown rapidly, increasing the need to manage and analyze this form of data in spite of its unstructured or semistructured form. Text-data analysis (Hearst, 1999) has emerged as an interdisciplinary research area forming a junction of a number of older fields like machine learning, natural language processing, and information retrieval (Grobelnik, Mladenic, & Milic-Frayling, 2000). It is sometimes viewed as an adapted form of a very similar research field that has also emerged recently, namely, data mining, which focuses primarily on structured data mostly represented in relational tables or multidimensional cubes. This article provides an overview of the various research directions in text-data analysis. After the “Introduction,” the “Background” section provides a description of a ubiquitous text-data representation model along with preprocessing steps employed for achieving better text-data representations and applications. The focal section, “Text-Data Analysis,” presents a detailed treatment of various text-data analysis subprocesses such as information extraction, information retrieval and information filtering, document clustering and document categorization. The article closes with a “Future Trends” section followed by a “Conclusion” section.

scholarly journals The Influence of Website Navigational Design on Improving Tourism Performance: Empirical Studies on Sport Tourism Providers in Indonesia

2019 ◽  
Vol 10 (3) ◽  
pp. 408
Author(s):  
Vanessa Gaffar ◽  
Oce Ridwanudin ◽  
Bambang Trinugraha ◽  
Ari Riswanto
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Path Analysis ◽  
Service Provider ◽  
Empirical Studies ◽  
The Other ◽  
Three Dimensions ◽  
Sport Tourism ◽  
Purposive Sampling ◽  
Analysis Technique

The purpose of this study is to examine and to explore the influence of website navigational design as a part of ICT, on decision to choose off road adventure service provider, as a sport tourism provider. The sample is 125 companies from varieties of type and category, by using purposive sampling. Data collected through literature review, observation and questionnaires. Path analysis is used as a data analysis technique with SPSS 20. Results shows that only search options influenced decision to choose off road adventure service provider. The other three dimensions don’t have influence in decision to choose off road adventure service provider. It shows that majority of people use website only to find information and tend to see navigation bar, individual hyperlink and image maps as not important. This means that the level of website literacy among consumer is still low. It is important for companies to educate their consumers on how website could benefit them.

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