The adoptability tool aims to predict the rate and level of adoption of new farming practices and inform research, development, and extension strategies. It involves researchers from CSIRO and other organizations who are developing a model-based tool to assess factors influencing adoption. An early concept version was tested with potential end-users and Version 2 was built in June 2010 and has been used in CRC projects. Further testing and validation of the tool is underway with the goal of having a validated version ready by mid-2011. The tool involves a 5 page data entry process asking 22 questions about key adoption factors and generates an adoption report describing the innovation and its predicted adoptability.
The Kenya Ushahidi Evaluation Project was 9-month Ushahidi evaluation project in partnership with the Harvard Humanitarian Initiative supported by the Knight Foundation. Jennifer Chan and Melissa Tully conducted research which lead to the creation of case studies and toolboxes. (2011) This is Toolbox #3: Real-Time Evaluation.
The Kenya Ushahidi Evaluation Project was 9-month Ushahidi evaluation project in partnership with the Harvard Humanitarian Initiative supported by the Knight Foundation. Jennifer Chan and Melissa Tully conducted research which lead to the creation of case studies and toolboxes. (2011) This is Toolbox #3: Real-Time Evaluation.
Future farm business workshop a pictorial introductionGreg Lawrence
Overview of the Future Farm Business (FFB) is an interactive, group-based simulation exercise developed to help participants learn about managing a farm business via risk and uncertainty scenarios subject to natural resource constraints.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. Aim
PREDICT - Producing a model-based tool and process to
p
predict likely rate and level of adoption for specific
y p p
practices and inform R,D & E strategies.
INFORM - Making adoptability knowledge and
considerations readily available, understandable and
available
applicable for R, D & E managers and practitioners.
ENGAGE - Collaborating with projects to assist with
development of adoptable technology packages
3. Who is involved?
FTE
Researchers
Geoff Kuehne (CSIRO)
( ) 0.95
Rick Llewellyn (CSIRO) 0.20
Perry Dolling (DAFWA) 0.10
David Pannell (UWA) 0.05
Roger Wilkinson (VicDPI) 0.05
Allan Curtis (CSU) 0.05
Diana Federenko (DAFWA) 0.10
4. Progress
• Started April last year
• Concept version produced and tested with
potential end-users (09-10)
• V i 2 b ilt (J
Version built (June 2010)
• Use of version 2 in CRC projects (2010)
• Validated version of tool due mid 2011
5. Testing
T ti
• Concept tested as part of the GRDC Grain &
Graze 2 program development
p g p
• Used by GRDC project coordinators in 5 regions
(agronomists, consultants,
(agronomists consultants farmer groups)
• Post-use evaluation with project coordinators
and funders (2 GRDC program managers)
6. So h t is
S what i it?
5 Pages data entry
g y
22 Questions
7. Example
Innovation trialability:
To what extent can a farmer trial the innovation on their farm
before deciding to invest in full adoption?
1. Not trialable at all 5. Very easily trialable
