This document discusses duty cycle concepts in reliability engineering. It begins with definitions of time-based and stress-condition-based duty cycles. Time-based duty cycle is the proportion of time a system is active, while stress-condition-based duty cycle considers the level of stress applied. The document then discusses how duty cycle manifests differently across various industries and how it is used to calculate reliability, with duty cycle affecting mission time, failure mechanisms, and characteristic life. Examples are provided for hard disk drives to illustrate the effects of duty cycle on acceleration factors and mean time to failure.
Physics of Failure (also known as Reliability Physics) is a science-based approach for achieving Reliability by Design. The approach is based on research to identify and understand the processes that initiate and propagate mechanisms that ultimately results in failure. This knowledge when used in Computer Aided Engineering (CAE) durability simulations and reliability assessment can evaluate if a new design, under actual operating is susceptible to the root causes of failure such as fatigue, fracture, wear, and corrosion during the intended service life of the product.
The objective is to identify and eliminate potential failure mechanisms in order to prevent operational failures through stress-strength analysis to produce a robust design and aid in the selection of capable manufacturing practices. This is accomplished by modeling the material strength and architecture of the components and technologies a product is based upon to evaluating their ability to endure the life-cycle usage and environmental stress conditions the product is expected to encounter over its service life in the field or during durability or reliability qualification tests.
The ability to identify and quantify the specific hazard risks timeline of specifics failure risks in a new product while it is still on the drawing board (or CAD screen) enables a product team to design reliability into a product by revising the design to eliminate or mitigate failure risks. This capability results in a form of Virtual Validation and Virtual Reliability Growth during the a product’s design phase that can be implemented faster and at lower costs than the traditional Design-Build-Test-Fixed approach to Reliability Growth during a product’s development and test phase.
This webinar compares classical reliability concepts and relates them to the PoF approach as applied to Electrical/Electronic (E/E) System and technologies. This webinar is intended for E/E Product Engineers, Validation/Test Engineers, Quality, Reliability and Product Assurance Personnel, CAE Modeling Analysts, R&D Staff and their supervisor.
Cognitive Predictive Maintenance for AutomotiveAnita Raj
In a world grappling with unanswered challenges and hidden data, the need of the hour is to unravel the dark mysteries that surround the auto industry today. It’s time to change the machine game and unlock the true human potential. It’s time to improve the overall quality of life for workers and passengers and ensure no leaf is unturned when it comes to safety and efficiency. Cognitive predictive maintenance is the game changer, that holds the key to minimizing auto recall.
AI revolutionizes predictive maintenance by leveraging machine learning algorithms to analyze data from sensors, predicting equipment failures before they occur. This proactive approach enhances efficiency, reduces downtime, and optimizes maintenance schedules, leading to substantial cost savings. AI's role in anomaly detection and predictive analytics transforms traditional maintenance practices, ensuring businesses stay competitive in an ever-evolving industrial landscape.
Physics of Failure (also known as Reliability Physics) is a science-based approach for achieving Reliability by Design. The approach is based on research to identify and understand the processes that initiate and propagate mechanisms that ultimately results in failure. This knowledge when used in Computer Aided Engineering (CAE) durability simulations and reliability assessment can evaluate if a new design, under actual operating is susceptible to the root causes of failure such as fatigue, fracture, wear, and corrosion during the intended service life of the product.
The objective is to identify and eliminate potential failure mechanisms in order to prevent operational failures through stress-strength analysis to produce a robust design and aid in the selection of capable manufacturing practices. This is accomplished by modeling the material strength and architecture of the components and technologies a product is based upon to evaluating their ability to endure the life-cycle usage and environmental stress conditions the product is expected to encounter over its service life in the field or during durability or reliability qualification tests.
The ability to identify and quantify the specific hazard risks timeline of specifics failure risks in a new product while it is still on the drawing board (or CAD screen) enables a product team to design reliability into a product by revising the design to eliminate or mitigate failure risks. This capability results in a form of Virtual Validation and Virtual Reliability Growth during the a product’s design phase that can be implemented faster and at lower costs than the traditional Design-Build-Test-Fixed approach to Reliability Growth during a product’s development and test phase.
This webinar compares classical reliability concepts and relates them to the PoF approach as applied to Electrical/Electronic (E/E) System and technologies. This webinar is intended for E/E Product Engineers, Validation/Test Engineers, Quality, Reliability and Product Assurance Personnel, CAE Modeling Analysts, R&D Staff and their supervisor.
Cognitive Predictive Maintenance for AutomotiveAnita Raj
In a world grappling with unanswered challenges and hidden data, the need of the hour is to unravel the dark mysteries that surround the auto industry today. It’s time to change the machine game and unlock the true human potential. It’s time to improve the overall quality of life for workers and passengers and ensure no leaf is unturned when it comes to safety and efficiency. Cognitive predictive maintenance is the game changer, that holds the key to minimizing auto recall.
AI revolutionizes predictive maintenance by leveraging machine learning algorithms to analyze data from sensors, predicting equipment failures before they occur. This proactive approach enhances efficiency, reduces downtime, and optimizes maintenance schedules, leading to substantial cost savings. AI's role in anomaly detection and predictive analytics transforms traditional maintenance practices, ensuring businesses stay competitive in an ever-evolving industrial landscape.
Introduction to DAS
Objectives of a DAS
Block diagram and explanation
Methodology
Hardware and software for DAS
Merits and Demerits of DAS/DQS
Conclusion
Predictive Maintenance - Predict the UnpredictableIvo Andreev
Predictive maintenance is one of the hottest topics on the way to digitalization of all industry areas. Manufacturers have developed different levels of maturity. From visual inspection, through real-time condition monitoring, to recent times when big data analytics with the aid of machine learning allows identify meaningful patterns in vast amounts of data and generate new, actionable insights.
This session will step on a couple of real project challenges to propose credible approach towards utilization of latest generation technologies for predictive maintenance in Industry 4.0. Although Machine Learning in Azure will be used for simplicity and demonstration, the majority of takeaways are valid for a wide range of technologies.
Survey of up and coming technologies and issues facing designers, builders and users of industrial automation and systems across all technologies. (CMAFH) Drive for Technology 2010 presentation
This presentation is an introduction into Multiple Over Stress Testing. A method to design robust and reliable products. It is a relaibility method that requires much insight in the Physics of Failure of the product in development
A major revolution in the field of instrumentation and control technology is well underway. Research, development and deployment activities are focused on making quantum leaps in industrial automation performance. Called Industry 4.0, this includes a new generation of low-cost wireless sensors, improved real-time data analytics and control systems, and advancements in high-fidelity process modeling. These innovations will include systems that improve industrial manufacturing efficiencies, and integrate and network subsystems across manufacturing processes.
This seminar session provides an overview of major aspects of reliability engineering, including general introduction of reliability engineering (definition of reliability, function of reliability engineering, a brief history of reliability, etc.), reliability basics (metrics used in reliability, commonly-used probability distributions in reliability, bathtub curve, reliability demonstration test planning, confidence intervals, Bayesian statistics application in reliability, strength-stress interference theory, etc.), accelerated life testing (ALT) (types of ALT, Arrhenius model, inverse power law model, Eyring model, temperature-humidity model, etc.), reliability growth (reliability-based growth models, MTBF-based growth model, etc.), systems reliability & availability (reliability block diagram, non-repairable or repairable systems, reliability modeling of series systems, parallel systems, standby systems, and complex systems, load sharing reliability, reliability allocation, system availability, Monte Carlo simulation, etc.), and degradation-based reliability (introduction of degradation-based reliability, difference between traditional reliability and degradation-based reliability, etc.).
Introduction to DAS
Objectives of a DAS
Block diagram and explanation
Methodology
Hardware and software for DAS
Merits and Demerits of DAS/DQS
Conclusion
Predictive Maintenance - Predict the UnpredictableIvo Andreev
Predictive maintenance is one of the hottest topics on the way to digitalization of all industry areas. Manufacturers have developed different levels of maturity. From visual inspection, through real-time condition monitoring, to recent times when big data analytics with the aid of machine learning allows identify meaningful patterns in vast amounts of data and generate new, actionable insights.
This session will step on a couple of real project challenges to propose credible approach towards utilization of latest generation technologies for predictive maintenance in Industry 4.0. Although Machine Learning in Azure will be used for simplicity and demonstration, the majority of takeaways are valid for a wide range of technologies.
Survey of up and coming technologies and issues facing designers, builders and users of industrial automation and systems across all technologies. (CMAFH) Drive for Technology 2010 presentation
This presentation is an introduction into Multiple Over Stress Testing. A method to design robust and reliable products. It is a relaibility method that requires much insight in the Physics of Failure of the product in development
A major revolution in the field of instrumentation and control technology is well underway. Research, development and deployment activities are focused on making quantum leaps in industrial automation performance. Called Industry 4.0, this includes a new generation of low-cost wireless sensors, improved real-time data analytics and control systems, and advancements in high-fidelity process modeling. These innovations will include systems that improve industrial manufacturing efficiencies, and integrate and network subsystems across manufacturing processes.
This seminar session provides an overview of major aspects of reliability engineering, including general introduction of reliability engineering (definition of reliability, function of reliability engineering, a brief history of reliability, etc.), reliability basics (metrics used in reliability, commonly-used probability distributions in reliability, bathtub curve, reliability demonstration test planning, confidence intervals, Bayesian statistics application in reliability, strength-stress interference theory, etc.), accelerated life testing (ALT) (types of ALT, Arrhenius model, inverse power law model, Eyring model, temperature-humidity model, etc.), reliability growth (reliability-based growth models, MTBF-based growth model, etc.), systems reliability & availability (reliability block diagram, non-repairable or repairable systems, reliability modeling of series systems, parallel systems, standby systems, and complex systems, load sharing reliability, reliability allocation, system availability, Monte Carlo simulation, etc.), and degradation-based reliability (introduction of degradation-based reliability, difference between traditional reliability and degradation-based reliability, etc.).
As technology increases, so does the need for BGA (Ball Grid Array) components. Screaming Circuits is excited to offer a presentation on BGA layout. This topic will cover why to use BGA's and specific considerations to have while designing your pcb.
This is a two parts lecture series. Many companies have begun their lean journey and have implemented lean manufacturing methods. The next step is applying lean to other processes including product development. While Lean New Product Development (Lean NPD) does focus on customer value and eliminating waste, it is also a front loaded, knowledge based approach. From a quality and reliability perspective, this should be viewed positively because it offers the opportunity to do the up front tasks needed to ensure robust and reliable products.
This 2-part webinar provides in introduction to Lean NPD and shows how it can be applied to reliability requirements definition, design decisions, risk assessment and mitigation, critical characteristics and process control, product testing, and failure analysis / corrective action to improve product reliability and robustness.
Part 2 covers Lean FMEA and Design Review by Failure Modes (DRBFM) as methods of risk assessment and mitigation. Critical characteristics and process control are also addressed. Design for Reliability, Robustness Testing, and Physics of Failure approaches build the essential strength into the product. Finally, using FRACAS to capture learning and build the knowledge base required for follow-on lean product development is addressed.
Introduction to x-rays and x-ray inspection, Safety Operating X-Ray Cabinet Systems, Size and Weight of X-Ray Inspection Systems, How do we image the X-rays?, Magnification, Resolution, Field of View, X-Ray Inspection Area, Power of X-Ray Tube, X-Ray Sensor, Sample Positioning, x-ray applications, LED Packaging and Assembly, Semiconductor Failure Analysis, Component Counterfeit Detection, Electronic Component Manufacturing, PCB / PTH (barrel fill) Analysis, Smart Phone Design and Manufacturing, BGA Void and Head – in Pillow Analysis, RF Components and Systems, Automotive Parts, Non Destructive Testing and Evaluation, Parts – Presents- Placement, Plastic / Aluminum Molding, Medical Device Design and Manufacturing, Small Animal Imaging, Seed and Agricultural Imaging, Identification of defects in soldered components – excess voiding or excess solder, Quality control of medical temperature sensors. X-Ray images taken with TruView X-Ray Inspection systems.
Reliability testing is critical for new component qualification, design change validation, or field failure simulation for root cause analysis. In many cases, with tight project schedules and scarce available resources, some important critical characteristics of a component or subsystem are overlooked. This will potentially result in new failure modes after implementing changes in production. The author will explain how to develop an effective test plan using the 6σ (Six Sigma) problem solving process, IDOV (Identify, Design, Optimize and Validation), to make the testing simple but efficient.
Design for reliability (DFR) is an industry-wide practice and a philosophy of considering reliability in an early stage of product design and development, to achieve a highly-reliable product while with sustainable cost. Physical of Failure (PoF) is recognized as a key approach of implementing DFR in a product design and development process. The author will present a case study to illustrate predicting and identifying product failure early in the design phase with the help of a quantitative PoF model based analysis tool.
A project sponsored in 2010 by the Aerospace Vehicle Systems Institute (AVSI), AFE 74, engaged a community of reliability subject matter experts to develop a reliability prediction technology roadmap based on a collaborative quality function deployment (QFD) industry assessment. The QFD provided a means to capture multiple viewpoints in a detailed enumeration of the needs, priorities and potential solutions for new reliability prediction methods to better support reliable system design processes. The discussions that were inspired by conducting this QFD provided an opportunity to open communications on some very divisive reliability prediction issues and helped bring the community together to solve the challenges of improving the utility of reliability predictions for the future. This presentation summarizes the findings of each step of the QFD, the reliability predictions roadmap derived from the QFD and discusses steps being taken to implement the roadmap..
This is a three parts lecture series. The parts will cover the basics and fundamentals of reliability engineering. Part 1 begins with introduction of reliability definition and other reliability characteristics and measurements. It will be followed by reliability calculation, estimation of failure rates and understanding of the implications of failure rates on system maintenance and replacements in Part 2. Then Part 3 will cover the most important and practical failure time distributions and how to obtain the parameters of the distributions and interpretations of these parameters. Hands-on computations of the failure rates and the estimation of the failure time distribution parameters will be conducted using standard Microsoft Excel.
Part 2. Reliability Calculations
1.Use of failure data
2.Density functions
3.Reliability function
4.Hazard and failure rates
Objectives
To understand Weibull distribution
To be able to use Weibull plot for failure time analysis and
diagnosis
To be able to use software to do data analysis
Organization
Distribution model
Parameter estimation
Regression analysis
In our efforts to add what we believe to be useful functionality to products and systems, we frequently get to the point where all the added usefulness becomes either a reliability or maintenance headache. It’s the nature of many technical professions, especially engineering, to want to ADD things to systems to improve their usefulness. They we balance the added utility with the complexity and “optimize” the design to minimize the inconvenience. What if we could have our cake and eat it too? Have the added utility but without the complexity? This webinar will review the basics of the “TRIZ” (Theory of Inventive Problem Solving) process and how its use in product design at the early stages can eliminate the need to have to make those compromises later.
Productivity and Competitiveness of RMG Industry and policy for ImprovementAshikul Kabir Pias
BANGLADESH IS A DEVELOPING COUNTRY.RMG PLAY A VITAL ROLE IN OUR ECONOMY. THE APPAREL INDUSTRY IS ONE OF THE PILLAR INDUSTRIES OF BANGLADESH. BANGLADESH IS THE 3RD LARGEST APPAREL EXPORTING COUNTRY IN THE WORLD. THE READYMADE GARMENTS (RMG) INDUSTRY IS THE LARGEST SINGLE ECONOMIC SECTOR IN BANGLADESH WHICH CONTRIBUTES TO 76% OF NATIONAL EXPORTS AND 90% OF MANUFACTURING GOODS EXPORTS .
How Service-Oriented Drive Deployments improve VSD Driveline UptimeSchneider Electric
Variable Speed Drives (VSDs) have proliferated and are now installed in large numbers throughout various industries. However, since these technologies are relatively new, not much thought has been given to the proper integration of these drives, nor have their potential energy savings and business continuity entitlements been fully realized. This paper examines how the intelligence within VSDs can be leveraged to perform predictive maintenance so that plant uptime can improve.
Using Machine Learning to Quantify the Impact of Heterogeneous Data on Transf...Power System Operation
Using large-scale distributed computing and a variety of heterogeneous data sources including real-time sensor measurements, dissolved gas measurements, and localized historical weather, we construct a predictive model that allows us to accurately predict remaining useful life and failure probabilities for a fleet of network transformers. Our model is robust to highly variable data types, including both static and dynamic data, sparse and dense time series, and measurements of internal and external processes (such as weather). By comparing the predictive performance of models built on different combinations of these data sources, we can quantify the marginal benefit of including each additional data source in our model.
In order to relate each type of data to the risk of failure across a fleet of transformers, we have developed a novel class of survival models, the convex latent variable (CLV) model. This type of specialized survival model has several advantages. Rather than an opaque and subjective "health index", it produces interpretable predictions like the probability of failure within a given time window or the expected RUL of an asset. Our framework supports accurate estimates of the risk of equipment failure across a wide range of time-scales, from a few weeks to many years in the future, and can model not just the instantaneous risk of failure due to an event like a storm, but also the long-term impact on the risk of failure.
Mastering Backup and Disaster Recovery: Ensuring Data Continuity and ResilienceMaryJWilliams2
Discover the essential strategies and tools for effective backup and disaster recovery. Learn how to safeguard your data against unexpected events and ensure business continuity. Explore the latest technologies and best practices in backup and disaster recovery management. To Know more:https://stonefly.com/white-papers/backup-disaster-recovery-solutions-governments/
Disaster Recovery: Understanding Trend, Methodology, Solution, and StandardPT Datacomm Diangraha
Disaster Recovery (DR)
Provides the technical ability to maintain critical services in the event of any unplanned incident that threatens these services or the technical infrastructure required to maintain them.
A comprehensive guide to Bluelock's IT Recovery Suite
The key to having a high-performing IT disaster recovery plan is having the right mix of solutions to achieve our organization's need for speedy recovery an maximum value. Achieve your ultimate goal of IT service availability and data protection.
7 Habits for High Effective Disaster Recovery AdministratorsQuorumLabs
Quorum and Forrester discuss the 7 habits for highly effective Disaster Recovery administrators. Topics such as RPO, RTO, performance, and networking will be discussed as part of a due diligence list prior to making the 7 habits highly effective.
Resiliency consists of both the ability to resist failure and to rapidly recover from failure.
Both sides of grid resiliency as it applies to the transmission grid can possibly be addressed by
dynamic line rating (DLR). The purpose of this paper is to present for discussion the use of
DLR as a means to improve grid resiliency in a way that is cost effective, quick to deploy, and
which provides ongoing operational benefits when not being used for resiliency purposes.
To prepare a technical feasibility proposal http://lindsey-usa.com/wp-content/uploads/2015/10/LINDSEY-ERS-Questionnaire-100812.pdf
for the Lindsey Emergency Restoration System (ERS). The information
requested in this questionnaire is the minimum required for assembling a
proposal. A worksheet should be prepared for each voltage level as well as for
each critical line. Any additional information or expansion on any item would be
beneficial.
This presentation deals with the Performance testing implemented in the Project.
We have to use Certain Tools in the POC process.
1) VSTS
2) JMeter
finally, VSTS has been Implemented.
I hope this presentation helps the viewer to get the overview of the tools which Accenture Deals.
Reducing Straddle Carrier accidents at the Portiosrjce
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Updated guidance on Portable Positive Protection for short duration and short term work zones detailing more accurate information on the most advanced and commonly used devices.
Simulating Operability of Wheel Loaders: Operator Models and Quantification o...Reno Filla
In this paper make the case that operability needs to be considered early on in the development of wheel loaders, alongside such established design targets as productivity and energy efficiency. We summarise research that shows how proper operator models can introduce a “human element” into dynamic simulations, providing more relevant answers with respect to operator-influenced complete-machine properties such as productivity and energy efficiency. We then show two ways of also drawing conclusions on the operability of wheel loaders by analysing either measurement data from physical tests or simulation results.
Objectives
To provide an introduction to the statistical analysis of
failure time data
To discuss the impact of data censoring on data analysis
To demonstrate software tools for reliability data analysis
Organization
Reliability definition
Characteristics of reliability data
Statistical analysis of censored reliability data
With the increase in global competition, more and more costumers consider reliability as one of their primary deciding factors, when purchasing new products. Several companies have invested in developing their own Design for Reliability (DFR) processes and roadmaps in order to be able to meet those requirements and compete in today’s market. This presentation will describe the DFR roadmap and how to effectively use it to ensure the success of the reliability program by focusing on the following DFR elements.
Improved QFN Reliability Process by John Ganjei. John will talk about the improvements in the reliability process in this webinar.
It is free to attend - see www.reliabilitycalendar.org/webinars/ to register for upcoming events.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.