Thin-disk laser scaling limit due to thermal-lens induced misalignment instability

We present an obstacle in power scaling of thin-disk lasers related with self-driven growth of misalignment due to thermal lens effects. This self-driven growth arises from the changes of the optical phase difference at the disk caused by the excursion of the laser eigen-mode from the optical axis. We found a criterion based on a simplified model of this phenomenon which can be applied to design laser resonators insensitive to this effect. Moreover, we propose several resonator architectures which are not affected by this effect.Comment: 19 pages, 13 figure

Spatial hole burning in thin-disk lasers and twisted-mode operation

Spatial hole burning prevents single-frequency operation of thin-disk lasers when the thin disk is used as a folding mirror. We present an evaluation of the saturation effects in the disk for disks acting as end-mirrors and as folding-mirrors explaining one of the main obstacles towards single-frequency operation. It is shown that a twisted-mode scheme based on a multi-order quarter-wave plate combined with a polarizer provides an almost complete suppression of spatial hole burning and creates an additional wavelength selectivity that enforces efficient single-frequency operation.Comment: 14 pages, 16 figure

Towards the decentralized coordination of multiple self-adaptive systems

When multiple self-adaptive systems share the same environment and have common goals, they may coordinate their adaptations at runtime to avoid conflicts and to satisfy their goals. There are two approaches to coordination. (1) Logically centralized, where a supervisor has complete control over the individual self-adaptive systems. Such approach is infeasible when the systems have different owners or administrative domains. (2) Logically decentralized, where coordination is achieved through direct interactions. Because the individual systems have control over the information they share, decentralized coordination accommodates multiple administrative domains. However, existing techniques do not account simultaneously for both local concerns, e.g., preferences, and shared concerns, e.g., conflicts, which may lead to goals not being achieved as expected. Our idea to address this shortcoming is to express both types of concerns within the same constraint optimization problem. We propose CoADAPT, a decentralized coordination technique introducing two types of constraints: preference constraints, expressing local concerns, and consistency constraints, expressing shared concerns. At runtime, the problem is solved in a decentralized way using distributed constraint optimization algorithms implemented by each self-adaptive system. As a first step in realizing CoADAPT, we focus in this work on the coordination of adaptation planning strategies, traditionally addressed only with centralized techniques. We show the feasibility of CoADAPT in an exemplar from cloud computing and analyze experimentally its scalability

Justifying design decisions with theory-based design principles

Although the role of theories in design research is recognized, we show that little attention has been paid on how to use theories when designing new artifacts. We introduce design principles as a new methodological approach to address this problem. Design principles extend the notion of design rationales that document how a design decision emerged. We extend the concept of design rationales by using theoretical hypotheses to support or object to design decisions. At the example of developing a new conceptual modeling grammar we demonstrate two main benefits of using design principles. First, the link between theory and design decision enables the design researcher to reason about the resulting behavior of the IT artifact prior to instantiation. Second, design principles allow deducing empirically testable hypotheses to foster the rigorous evaluation of IT artifacts

Towards a Blockchain Technology Framework – Literature Review on components in blockchain implementations

The goal of this work is to obtain a framework that represents the technological core aspects of blockchain, separated into components, their subcategories and related basic technologies. In order to gain a holistic view of blockchain, with the help of the framework, technologies constructs should be made identifiable as blockchain. For this purpose, a literature review will be conducted to investigate previous approaches to the component-wise division of blockchain technologies. Subsequently, a literature analysis will be conducted in which five established blockchain systems will be analysed and their implementations will be assigned to the general components. For evaluation, a further sixth blockchain technology is used to confirm the basic framework. It becomes apparent that the framework allows a classification of blockchain systems into technologies. The framework has potential for expansion by adding further technology features to make the framework even more useful

Decentralised Autonomous Organisations in Organisational Design Theory

As Decentralised Autonomous Organisation (DAO) is a new emerging form of organisation with unrevealed characteristics, this study examines how DAO can be classified in terms of organisational design theory and provides an overview of its characteristics. The investigation could further provide guidance on what types of organisations can be easily transformed into a DAO. A deeper look into the knowledge base on organisational design theory and organisational forms is essential as well as characteristic properties. In regard of DAO, the underlying concepts of blockchain and smart contracts are marked as they serve a better understanding, which is needed to characterise DAO. An analysis of DAO will specify how they are classified in terms of organisational design and how they differ compared to traditional organisation forms, which will be displayed in an overview. The results are discussed and concluded, also comprising the highlighting of potential paths for future work

TOWARDS A RESEARCH METHOD FOR THEORYDRIVEN DESIGN RESEARCH

In this paper we outline a new methodical approach for integrating theories into the design research process. Incorporating theories in design projects allows design researchers to reason on the effects of the IT artifact prior to its realization. We argue that design decisions should be transparent claims of utility based on theory-grounded arguments. Documenting design decisions requires the design researcher to integrate appropriate theories and document the rationale behind a particular design decision. Overall, we demonstrate on the example of constructing a new modeling grammar how to integrate theories in the design research process and discuss conflicts which occur when applying these theories

A User Study on Explainable Online Reinforcement Learning for Adaptive Systems

Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty. Online RL facilitates learning from actual operational data and thereby leverages feedback only available at runtime. However, Online RL requires the definition of an effective and correct reward function, which quantifies the feedback to the RL algorithm and thereby guides learning. With Deep RL gaining interest, the learned knowledge is no longer explicitly represented, but is represented as a neural network. For a human, it becomes practically impossible to relate the parametrization of the neural network to concrete RL decisions. Deep RL thus essentially appears as a black box, which severely limits the debugging of adaptive systems. We previously introduced the explainable RL technique XRL-DINE, which provides visual insights into why certain decisions were made at important time points. Here, we introduce an empirical user study involving 54 software engineers from academia and industry to assess (1) the performance of software engineers when performing different tasks using XRL-DINE and (2) the perceived usefulness and ease of use of XRL-DINE.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0593

The Linkage to Business Goals in Data Science Projects

Modern data analytics equips businesses to make data-driven decisions by revealing patterns and insights that enhance strategic planning, operational efficiency, and process optimization. Its applications encompass personalized marketing through customer segmentation, predictive modelling for fraud detection, and enhancing security. A significant methodology in this realm is the Cross-Industry Standard Process for Data Mining (CRISP-DM), where the Business Understanding phase aims to ensure data science projects align with overarching business goals. However, challenges arise when these business objectives are ambiguous, ill-defined, or evolving. The complexity of data analytics projects underscores the need for domain expertise and robust collaboration between data scientists, business stakeholders, and domain experts. The imperative is to bridge the technical and business perspectives, manage expectations, and define project scopes. The short paper at hand addresses the question how data analytic goals can systematically align with business objectives in data science projects. By incorporating methods from Enterprise Architecture Management, we propose a structured approach for goal determination in data science projects, ensuring business and data mining objectives are seamlessly integrated