Simon Haslett, Professor of Physical Geography and Director of the Centre for Excellence in Learning and Teaching at the University of Wales, Newport, outlines sea ice formation and its influence on climate. The climate change impacts are examined, particularly the albedo effect, and also includes clouds. The presentation includes a video of a flight Professor Haslett took over the North Atlantic and a visit to eastern Canada to discuss sea ice, clouds, and their albedo effect on climate, and contribution to climate change.
REGULATORY PROVISION RELATING TO MANUFACTURING OF COSMETICSourav Mohanto
REGULATORY PROVISION RELATING TO MANUFACTURING OF COSMETIC.
Minimum requirement of space, equipment and machinery for manufacturing of cosmetic have been prescribed under M II to the Drugs and Cosmetics Rule,1945
Simon Haslett, Professor of Physical Geography and Director of the Centre for Excellence in Learning and Teaching at the University of Wales, Newport, outlines sea ice formation and its influence on climate. The climate change impacts are examined, particularly the albedo effect, and also includes clouds. The presentation includes a video of a flight Professor Haslett took over the North Atlantic and a visit to eastern Canada to discuss sea ice, clouds, and their albedo effect on climate, and contribution to climate change.
REGULATORY PROVISION RELATING TO MANUFACTURING OF COSMETICSourav Mohanto
REGULATORY PROVISION RELATING TO MANUFACTURING OF COSMETIC.
Minimum requirement of space, equipment and machinery for manufacturing of cosmetic have been prescribed under M II to the Drugs and Cosmetics Rule,1945
Parabens are group of preservative ingredients used in pharmaceutical, cosmetics, personal hygiene and food products.
Parabens prevent the growth of potentially harmful microbes such as bacteria ,fungi or yeast, thereby increasing shelf-life of cosmetics.
This slideshare describes the study of quality of raw materials used in cosmetics and general methods of analysis of raw materials used in cosmetic manufacture as per BIS
Parabens are group of preservative ingredients used in pharmaceutical, cosmetics, personal hygiene and food products.
Parabens prevent the growth of potentially harmful microbes such as bacteria ,fungi or yeast, thereby increasing shelf-life of cosmetics.
This slideshare describes the study of quality of raw materials used in cosmetics and general methods of analysis of raw materials used in cosmetic manufacture as per BIS
Sensors Without Borders and Sensing Local presented our early analysis of the on-going BTM study on March 18th at Bengaluru's Airpocalypse: Air Pollution Problems & Solutions. Hosted by Co MEDIA LAB and Radio Active CR 90.4 MHz
Comparing and Contrasting Leading Tools for Evaluating ChemicalsSustainable Brands
Brands are increasingly concerned about the chemicals used in their products. Transparency is growing, but knowing something is there doesn't mean you know how it will affect your customers. To fill this void, a number of chemical evaluation tools (e.g. GreenScreen, GoodGuide, GreenWERCS) and product evaluation certifications have emerged. Expert Tony Kingsbury and his team looked at 32 of these tools and certifications to determine how robust their evaluation is, how many hazard endpoints they take into account, how costly they are, how transparent they are, and whether you need a PhD to use them. Find out which tools are right for your organization and what limitations they carry.
Consensus Models to Predict Endocrine Disruption for All Human-Exposure Chemi...Kamel Mansouri
AAAS annual meeting (Boston, Feb 2017)
Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an external validation set collected from the literature. In order to overcome the limitations of single models, a consensus was built weighting models based on their prediction accuracy scores (including sensitivity and specificity against training and external sets). Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. The final consensus predicted 4001 chemicals as actives to be considered as high priority for further testing and 6742 as suspicious chemicals. The same approach is now being applied on a larger scale project to predict the potential androgen receptor (AR) activity of chemicals. This project called CoMPARA (Collaborative Modeling Project for Androgen Receptor Activity) is a collaboration between 35 international groups working on a common set of ~55k chemicals.
This abstract does not necessarily reflect U.S. EPA policy
Mr. Chiew Teck Wee, director of Riverstone Environmental Sdn Bhd and strategic business partner of Trinity Consultants, presented at the 2nd ICEOH Conference in Malaysia on the use of BREEZE Risk Analyst for environmental health impact assessments.
In this presentation, Mr. Chiew Teck Wee discusses the many uses and features of Risk Analyst, a human health and ecological risk assessment modeling suite. Risk Analyst has been developed to perform multi-media fate, transport and exposure modeling and is also used for estimating potential adverse impacts to human health and ecological receptors. Topics discussed also include how Risk Analyst fully implements the HHRAP, a U.S. EPA guidance document for performing site-specific, multi-media and multi-pathway human health risk assessments (HHRA) and examples of case studies where the tool has been used to evaluate cancer risk.
International Journal of Pharmaceutical Science Invention (IJPSI)inventionjournals
International Journal of Pharmaceutical Science Invention (IJPSI) is an international journal intended for professionals and researchers in all fields of Pahrmaceutical Science. IJPSI publishes research articles and reviews within the whole field Pharmacy and Pharmaceutical Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
19. US
• 2007-2008 NHANES survey
Bodyweight and height data for
8,861 US subjects
EU
• NHANES data scaled based
on EU country average
weights and heights
Bodyweight and Height
21. Surface Area Calculations
Body Part Surface Area
Scalp 1/2 Head
Neck 1/10 Trunk
Chest 1/4 Trunk
Stomach 1/5 Trunk
Back 3/10 Trunk
Arms Arms – (1/4 Hands)
Wrists 1/4 Hands
Palms 1/2 Hands
Intimate parts 1/100 Total Body
Body Part Surface Area Reference
Face 1/2 Head – 28.8cm2 Api et al. (2008); 28.8 cm2 refers to combined surface areas of eyes and lips
Eyes 24cm2 Bremme et al. (2003)
Lips 4.8cm2 Ferrario et al. (2000)
Mouth 212cm2 Collins and Dawes, (1987), Ferrario et al. (2000)
Behind ears 36cm2 Estimated based on expert judgement
Underarms 200cm2 Bremme et al. (2003)
Relative
Absolute
22. • Europe → COLIPA (Hall et al., 2007; 2011)
• USA → CTFA (Loretz et al., 2005; 2006; 2008)
• Hydro alcoholics → Tozer et al. (2004)
Amount Used
23. Retention
Product Dermal Retention Factor (%)
Body lotion (mass, prestige, or other) 100
Deodorant spray 23.5
Deodorant roll-on 100
Body spray 100
Toothpaste 10
Mouthwash 1
Lipstick 100
Liquid makeup foundation 100
Hair styling 10
Eau de toilette 100
Eau de parfum 100
Aftershave 100
Shower gel 1
Shampoo 1
Rinse-off conditioner 1
Face moisturiser 100
Hand cream 100
60. • Risk Assessment
– Determine high-risk products or application sites
– Assess safety of contaminants or by-products
• Assigning safe concentrations
• Product Development
– Bringing new fragrances/substances/products to market
– Integrate with existing market data
Predictive Uses
63. • Updated survey data: 2013 – 2014
• New countries: Italy and Poland
• New product categories:
– Eye cream
– Facial washes/cleansers/toners
– Makeup remover
– Shaving preps
• New age groups: 10 – 17 year olds
• Household cleaning products
• Browser-based version
Future Directions and Expansions
64. • Regulatory Toxicology and Pharmacology:
– Novel database for exposure to fragrance ingredients in cosmetics and
personal care products
• Comiskey et al 2015
– Use of an aggregate exposure model to estimate consumer exposure to
fragrance ingredients in personal care and cosmetic products
• Safford et al 2015
– Application of the expanded Creme RIFM consumer exposure model to
fragrance ingredients in cosmetic, personal care and air care products
• Safford et al 2017
References
65. Expert Models for Decision Makers
Thank You
Cian O’ Mahony, Chief Science Officer
cian.omahony@cremeglobal.com
@CianOnData
66. Expert Models for Decision Makers
Thank You
Cian O’ Mahony, Chief Science Officer
cian.omahony@cremeglobal.com
@CianOnData
67. Expert Models for Decision Makers
Thank You
Cian O’ Mahony, Chief Science Officer
cian.omahony@cremeglobal.com
@CianOnData
Editor's Notes
(So far, we have gather detailed fragrance concentration data on 41 fragrances materials culminating in more than 150 thousand level 1 data points)
We also have data on the concentration of fragrances in mixtures and the concentrations of mixtures that go into the products
So far, we have gathered detailed fragrance concentration data on 61 fragrances materials and the concentration of mixtures in all products culminating is 0.5 a million datum points to probabilistically calculate the likely concentration of fragrances ingredients in all the products
(So far, we have gather detailed fragrance concentration data on 41 fragrances materials culminating in more than 150 thousand level 1 data points)
We also have data on the concentration of fragrances in mixtures and the concentrations of mixtures that go into the products
So far, we have gathered detailed fragrance concentration data on 61 fragrances materials and the concentration of mixtures in all products culminating is 0.5 a million datum points to probabilistically calculate the likely concentration of fragrances ingredients in all the products
(So far, we have gather detailed fragrance concentration data on 41 fragrances materials culminating in more than 150 thousand level 1 data points)
We also have data on the concentration of fragrances in mixtures and the concentrations of mixtures that go into the products
So far, we have gathered detailed fragrance concentration data on 61 fragrances materials and the concentration of mixtures in all products culminating is 0.5 a million datum points to probabilistically calculate the likely concentration of fragrances ingredients in all the products
Random sampling and probabilistic analysis can be used for all parameters