Statistics, damned statistics and nanoscience – using data science to meet the challenge of nanomaterial complexity

2016 ◽  
Vol 1 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Baichuan Sun ◽  
Michael Fernandez ◽  
Amanda S. Barnard
Keyword(s):  
Digital Technology ◽  
Data Science ◽  
Structure Property ◽  
Nano Structure ◽  
Detailed Understanding ◽  
Structure Property Relationships ◽  
Modern Methods ◽  
Using Data ◽  
The Way

Combining advances in digital technology and modern methods in statistics with a detailed understanding of nano-structure/property relationships can pave the way for more realistic predictions of nanomaterials performance.

Micromechanical characterization of soft, biopolymeric hydrogels: stiffness, resilience, and failure

Soft Matter ◽  
2018 ◽  
Vol 14 (18) ◽  
pp. 3478-3489 ◽  
Author(s):  
Shruti Rattan ◽  
Linqing Li ◽  
Hang Kuen Lau ◽  
Alfred J. Crosby ◽  
Kristi L. Kiick
Keyword(s):  
Tissue Engineering ◽  
Local Structure ◽  
Structure Property ◽  
Detailed Understanding ◽  
Structure Property Relationships ◽  
Micromechanical Characterization

Detailed understanding of the local structure–property relationships in soft biopolymeric hydrogels can be instrumental for applications in regenerative tissue engineering.

scholarly journals Cooking Up Knowledge From Big Data Using Data Science

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrea Rau
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Programming Language ◽  
Data Science ◽  
Weather Forecasting ◽  
Original Question ◽  
Data Scientist ◽  
Using Data ◽  
The Way

Data collected in very large quantities are called big data, and big data has changed the way we think about and answer questions in many different fields, like weather forecasting and biology. With all this information available, we need computers to help us store, process, analyze, and understand it. Data science combines tools from fields like statistics, mathematics, and computer science to find interesting patterns in big data. Data scientists write step-by-step instructions called algorithms to teach computers how to learn from data. To help computers understand these instructions, algorithms must be translated from the original question asked by a data scientist into a programming language—and the results must be translated back, so that humans can understand them. That means that data scientists are data detectives, programmers, and translators all in one!

Use of Data Analytics to Increase the Efficiency of Last Mile Logistics for Ecommerce Deliveries

2021 ◽  
pp. 167-180
Author(s):  
Gaurav Nagpal ◽  
Gaurav Kumar Bishnoi ◽  
Harman Singh Dhami ◽  
Akshat Vijayvargia
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Operational Efficiency ◽  
Use Of Data ◽  
Last Mile ◽  
Digital Transactions ◽  
Using Data ◽  
Operational Excellence ◽  
The Way

With the increasing share of digital transactions in the business, the way of operating the businesses has changed drastically, leading to an immense opportunity for achieving the operational excellence in the digital transactions. This chapter focusses on the ways of using data science to improve the operational efficiency of the last mile leg in the delivery shipments for e-commerce. Some of these avenues are predicting the attrition of field executives, identification of fake delivery attempts, reduction of mis-routing, identification of bad addresses, more effective resolution of weight disputes with the clients, reverse geo-coding for locality mapping, etc. The chapter also discusses the caution to be exercised in the use of data science, and the flip side of trying to quantify and dissect the phenomenon that is so complex and subjective in nature.

scholarly journals Using data-driven activities with ChemEd X Data to practice structure-property relationships in General Chemistry

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Xavier Prat-Resina
Keyword(s):  
General Chemistry ◽  
Student Performance ◽  
Graphical Representation ◽  
Data Driven ◽  
Structure Property ◽  
Web Interface ◽  
Structure Property Relationships ◽  
Property Patterns ◽  
Using Data ◽  
Atomic Crystal

AbstractGeneral Chemistry covers a wide variety of structure-property relationships that rely upon electronic, atomic, crystal or molecular factors. Giving students experimental data will allow them to identify the structure-property patterns as well as identify the limit of predictability of such patterns. “ChemEd X Data” is a web interface designed by the author that facilitates the navigation, filtering and graphical representation of chemical and physical data. It can assist students at identifying trends in structure-property relationships, they can create controlled experiments to test a relationship as well as investigating how different molecular factors may affect a single macroscopic property. In particular, since the site offers unstructured but dynamically searchable data, it is designed to have students learn control of variable strategies (CVS). This paper describes the implementation of a five-step sequence of activities related to structure-property relationships in a General Chemistry semester. ChemEd X Data is used for the open-ended or data-driven steps of this sequence. Student performance is analyzed with the objective of understanding which activities require a higher cognitive skill, as well as identify student previous performances that correlate with success in the activities and in the course in general.

Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks

2020 ◽  
Vol 5 (5) ◽  
pp. 962-975
Author(s):  
Yixing Wang ◽  
Min Zhang ◽  
Anqi Lin ◽  
Akshay Iyer ◽  
Aditya Shanker Prasad ◽  
...  
Keyword(s):  
Neural Networks ◽  
Polymer Nanocomposites ◽  
Data Driven ◽  
Learning Approach ◽  
Structure Property ◽  
Element Analysis ◽  
Structure Property Relationships ◽  
Structure Property Relationship ◽  
Using Data ◽  
Relationship Of

In this paper, a data driven and deep learning approach for modeling structure–property relationship of polymer nanocomposites is demonstrated. This method is applicable to understand other material mechanisms and guide the design of material with targeted performance.

Structure–Property Correlation Study for Organic Photovoltaic Polymer Materials Using Data Science Approach

2020 ◽  
Vol 124 (24) ◽  
pp. 12871-12882 ◽  
Author(s):  
Yue Huang ◽  
Jingtian Zhang ◽  
Edwin S. Jiang ◽  
Yutaka Oya ◽  
Akinori Saeki ◽  
...  
Keyword(s):  
Data Science ◽  
Correlation Study ◽  
Polymer Materials ◽  
Organic Photovoltaic ◽  
Structure Property ◽  
Property Correlation ◽  
Science Approach ◽  
Using Data

Structure-property relationships of PE/PP sandwiched films

1987 ◽  
Vol 45 ◽  
pp. 454-455
Author(s):  
J. Petermann ◽  
G. Broza ◽  
U. Rieck ◽  
A. Jaballah ◽  
A. Kawaguchi
Keyword(s):  
Mechanical Tests ◽  
Polymer Materials ◽  
Ionic Crystals ◽  
Structure Property ◽  
Polymer Systems ◽  
Structure Property Relationships ◽  
Oriented Films ◽  
Materials Used ◽  
Polymer Laminates ◽  
Heated Glass

Oriented overgrowth of polymer materials onto ionic crystals is well known and recently it was demonstrated that this epitaxial crystallisation can also occur in polymer/polymer systems, under certain conditions. The morphologies and the resulting physical properties of such systems will be presented, especially the influence of epitaxial interfaces on the adhesion of polymer laminates and the mechanical properties of epitaxially crystallized sandwiched layers.Materials used were polyethylene, PE, Lupolen 6021 DX (HDPE) and 1810 D (LDPE) from BASF AG; polypropylene, PP, (PPN) provided by Höchst AG and polybutene-1, PB-1, Vestolen BT from Chemische Werke Hüls. Thin oriented films were prepared according to the method of Petermann and Gohil, by winding up two different polymer films from two separately heated glass-plates simultaneously with the help of a motor driven cylinder. One double layer was used for TEM investigations, while about 1000 sandwiched layers were taken for mechanical tests.

Sign in / Sign up

Export Citation Format

Share Document