Sigve Hamilton Aspelund has a M.Sc. in Petroleum Engineering from the University of Stavanger. His 2001 thesis was titled "Lorenz equations: An introduction at m.sc. level" and provided an introduction to the chaotic dynamic system represented by the Lorenz equations. It studied the stability of critical points with varying r parameters and bifurcations leading to unstable critical points and sensitivity to initial conditions. Aspelund has over 15 years of experience in reservoir simulation and modeling. He provides his curriculum vitae, references, recommendations, and LinkedIn profile for review.
Thermostatics vs. electrodynamics preprints 10.20944.v1Bob Eisenberg
Thermodynamics has been the foundation of many models of biological and technological systems. But thermodynamics is static and is misnamed. A more suitable name is thermostatics.
Thermostatics does not include time as a variable and so has no velocity, flow or friction. Indeed, as usually formulated, thermostatics does not include boundary conditions. Devices require boundary conditions to define their input and output. They usually involve flow and friction. Thermostatics is an unsuitable foundation for understanding technological and biological devices. A time dependent generalization of thermostatics that might be called thermal dynamics is being
developed by Chun Liu and collaborators to avoid these limitations. Electrodynamics is not restricted like thermostatics, but in its classical formulation involves drastic assumptions about polarization and an over-approximated dielectric constant. Once the Maxwell equations are rewritten without a dielectric constant, they are universal and exact. Conservation of total current,including displacement current, is a restatement of the Maxwell equations that leads to dramatic simplifications in the understanding of one dimensional systems, particularly those without branches, like the ion channel proteins of biological membranes and the two terminal devices of electronic systems. The Brownian fluctuations of concentrations and fluxes of ions become the spatially independent total current, because the displacement current acts as an unavoidable low pass filter, a consequence of the Maxwell equations for any material polarization. Electrodynamics and thermal dynamics together form a suitable foundation for models of technological and biological systems.
The presentation is about how to evaluate the probability of finding the system in any particular state at any later time when the simple Hamiltonian was added by time dependent perturbation. So now the wave function will have perturbation-induced time dependence.
Thermostatics vs. electrodynamics preprints 10.20944.v1Bob Eisenberg
Thermodynamics has been the foundation of many models of biological and technological systems. But thermodynamics is static and is misnamed. A more suitable name is thermostatics.
Thermostatics does not include time as a variable and so has no velocity, flow or friction. Indeed, as usually formulated, thermostatics does not include boundary conditions. Devices require boundary conditions to define their input and output. They usually involve flow and friction. Thermostatics is an unsuitable foundation for understanding technological and biological devices. A time dependent generalization of thermostatics that might be called thermal dynamics is being
developed by Chun Liu and collaborators to avoid these limitations. Electrodynamics is not restricted like thermostatics, but in its classical formulation involves drastic assumptions about polarization and an over-approximated dielectric constant. Once the Maxwell equations are rewritten without a dielectric constant, they are universal and exact. Conservation of total current,including displacement current, is a restatement of the Maxwell equations that leads to dramatic simplifications in the understanding of one dimensional systems, particularly those without branches, like the ion channel proteins of biological membranes and the two terminal devices of electronic systems. The Brownian fluctuations of concentrations and fluxes of ions become the spatially independent total current, because the displacement current acts as an unavoidable low pass filter, a consequence of the Maxwell equations for any material polarization. Electrodynamics and thermal dynamics together form a suitable foundation for models of technological and biological systems.
The presentation is about how to evaluate the probability of finding the system in any particular state at any later time when the simple Hamiltonian was added by time dependent perturbation. So now the wave function will have perturbation-induced time dependence.
Characterizing Luminescent Properties of Thin Films by Farisch HanoemanFarisch Hanoeman
Thesis at Delft University of Technology. Fundamental Aspects of Materials and Energy (FAME), Radiation Science and Technology department, Faculty of Applied Sciences. Supervisor: dr. E. van der Kolk, co-reader: prof. dr. P. Dorenbos.
Linear and Weakly Non-Linear Stability Analyses of Double-Diffusive ElectroCo...iosrjce
The linear and weakly non-linear stability analyses of double diffusive electro-convention in a micropolar fluid
layer heated and saluted from below and cooled from above is studied. The linear and non-linear analyses are, respectively
based on normal mode technique and truncated representation of Fourier series. The influence of various parameters on the
onset of convection has been analyzed in the linear case. The resulting autonomous Lorenz model obtained in non-linear
analysis is solved numerically to quantify the heat and mass transforms through Nusselt and Sherwood number. It is
observed that the increase in concentration of suspended particles and electric field and electric Rayleigh number increases
the heat and mass transfer
If you want to work in my team you can download this MOU and fill in the marked in red and sign it and send it to sigvehamiltonaspelund@gmail.com I will return it with my signature.
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.
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.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
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/
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
Leading Change strategies and insights for effective change management pdf 1.pdf
Education
1. <br />Sigve Hamilton Aspelund <br />Sivilingeniør/ M. Sc. Petroleum engineering with applied science program<br />Reevegen 43, 4340 Bryne<br />Mail: sigve.aspelund@lyse.net<br />Phone: +47 92647129<br />http://www.google.com/profiles/aspelundsigve<br />Application letter<br />Curriculum Vitae<br />Courses, references and recomendations<br />My Linkedin profile<br />University of Stavanger 1997-2001+2003:<br />Thesis: Lorenz equations: An introduction at m.sc. level (2.1) 2001 Supervisor: Paul Papatzacos<br />Abstract: objective of this thesis was to give an introduction to the chaotic dynamic system that Lorenz-equations represent. First I gave an introduction to strange attractors followed by historical overview and how Lorenz discovered sensitivity to initial values. Details to strange attractors are studied before butterfly-effect is explained. Chapter 2: Important characteristics to the differential equations, where stability to the critical points are a central theme. Global theory is introduced and the Poincare-Bendixon theorem is involved to show the limitations to a continuing dynamic system in two dimensions. Bifurcations are described at the end of the chapter. Chapter 3: The characteristics sensitivity to initial values to a chaotic dynamical system is described. Lyapunov exponents that are used to measure dynamical systems sensitivity and the fractal dimension are involved. The definition of a continuous dynamic dissipative system is studied and a chaotic path and a chaotic attractor are defined. Chapter 4: First a short introduction then important characteristics to Lorenz-equations. Stability analysis of the critical points is a central theme. The critical points are unstable for some values of the r-parameter. This lead to a system show extreme sensitivity to initial values. These characteristics are the definitions of a chaotic dynamic system and the reason for discarding a longtime forecast of the weather. The differential equations system as this thesis is impossible to solve analytical, but it is possible to solve the system numerically. This solution is not exact, but the general general appearance of the solution will not change significantly. At the end of the chapter an overview over Lorenz equation are from conventions in the atmosphere followed by questions regarding the thesis. Chapter 5: Conclusion: In this thesis I have given an introduction to the chaotical dynamical system that the Lorenz equations represent. I have studied stability to the critical points with variable r parameter with constant parameters σ and b. For some of the values of the r parameter the critical points are stable. I have shown that bifurcation implies unstable critical points and is the most known property of this system. Instability to the critical points leads to sensitivity regarding to perturbations in the initial values. This is the most important property for the system and is the reason for being called chaotic. Lorenz concluded that a long term weather forecast was impossible.<br />Subjects (Grades in parenthesis 1.0 is the best): <br />Reservoir simulation 1 (3.1) <br />Reservoir simulation 2 (Pass) (Eclipse)<br />Reservoir geophysics and visualization (D)<br />Well testing (3.7)<br />Numerical mathematics (1.2) <br />Thermodynamics (1.1) <br />Physics laboratory (1.9) <br />Chemistry and environment (3.4) <br />Computer science (Pass)<br />Well logging (3.5) <br />Petroleum geology (2.0) <br />Reservoir technology 1 (3.0) <br />Drilling and well fluids (2.0) <br />General chemistry (2.4) <br />Oscillations and waves (3.8) <br />Mathematics 5: complex analysis (1.6) <br />Introduction to reservoir simulation (2.5) <br />Mathematical modeling 1 (2.3) <br />Chaotical dynamical systems (1.2) <br />Fluid mechanics (2.0) <br />Statistical physics (2.2) <br />Petroleum physics (2.1) <br />Numerical linear algebra (3.0) <br />Numerical functional analysis (3.4) <br />Mathematical modeling 2 (1.3) <br />Material physics (2.2) <br />Experimental methods at laboratory (2.2) <br />PVT analysis (3.3) <br />Numerical treatment of differential equations (1.6) <br />Stochastically modeling (2.7) <br />Statistical physics 2 (1.9) <br />Fluid dynamics (1.4)<br />University of Bergen 1998<br />Examen philosophicum (2.6)<br />Norwegian University of Technical Science 1995-1997<br />General physics 1 (1.8) <br />General physics 2 (1.7) <br />Mechanics (2.0) <br />Electricity and magnetism (2.7) <br />Quantum physics and statistical physics (3.1) <br />Introduction to analysis (2.0)<br />Linear algebra (2.6) <br />Multidimensional analysis (4.0) <br />Differential analysis and Fourier analysis (1.3) <br />Probability and statistics 1 (3.9)<br />