This study set out to explore how Official Development Assistance was practised in South Africa. An exploratory narrative design was followed to uncover the ‘strategy in the skin’ of strategy practitioners in the unit of analysis and to respond, therefore, to the research questions. This study has contributed to the body of knowledge in that it has brought together an alternative confluence of three theoretical perspectives of strategy as practice; complex adaptive systems and organisational hypocrisy and has explored the impact of the practice lens on these standpoints. While there has been extensive research on each of the theoretical perspectives, there has not yet been a study that has drawn together the three perspectives in relation to an empirical unit of analysis such as Official Development Assistance practices and practitioners. The study responded to a knowledge gap in relation to how public sector organisations, such as government units and the strategy practitioners of such units, practice strategy beyond the reified, formalised conceptions of strategy and in relation to their inhabiting complex, political organisational systems. The study arrived at two central theoretical findings. Firstly, that strategising represents a calibration of strategic practices towards strategic outcomes through the activities of complex adaptive practitioners v within the more politically inclined organisation. Secondly, that beyond the text of strategy, there is sub-text that is equally part of the micro strategy towards strategic outcomes.The skilful and sometimes delicate balancing act, that strategists perform to legitimise the calibrated combinations of action and politics in organisational strategy, equally needs nuanced, subtle and more complex forms of organisational communication. The study, therefore, makes the claim that complex adaptive systems and the characteristics of political organisations (as not being geared to action) are inherently broadened through the multiple dimensions of the practice turn and strategy as sub-text. The research confirmed that strategy as practice is a useful lens to understand strategy beyond the formally documented scripts and espoused pronouncements of strategy within organisational studies / Business Management / D.B.L.

Cette étude s’inscrit dans le domaine de l’intelligence artificielle, plus précisément au croisement des deux domaines que sont la planification autonome en environnement probabiliste et la vérification formelle probabiliste. Dans ce contexte, elle pose la question de la maîtrise de la complexité face à l’intégration de nouvelles technologies dans les systèmes critiques : comment garantir que l’ajout d’une intelligence à un système, sous la forme d’une autonomie, ne se fasse pas au détriment de la sécurité ? Pour répondre à cette problématique, cette étude a pour enjeu de développer un processus outillé, permettant de concevoir des systèmes auto-adaptatifs critiques, ce qui met en œuvre à la fois des méthodes de modélisation formelle des connaissances d’ingénierie, ainsi que des algorithmes de planification sûre et optimale des décisions du système. / This study takes place in the broad field of Artificial Intelligence, specifically at the intersection of two domains : Automated Planning and Formal Verification in probabilistic environment. In this context, it raises the question of the integration of new technologies in critical systems, and the complexity it entails : How to ensure that adding intelligence to a system, in the form of autonomy, is not done at the expense of safety ? To address this issue, this study aims to develop a tool-supported process for designing critical, self-adaptive systems. Throughout this document, innovations are therefore proposed in methods of formal modeling and in algorithms for safe and optimal planning.

Global projects have a high failure rate, with many project failures attributed to lack of effective leadership. A knowledge gap about leadership requirements and complexities in a global project management environment has increased the risks in global projects. The problem is evident in the increasing project failure rate and the struggling national strategies in the oil and gas industry in the Arabian Gulf Cooperation Council (GCC). The purpose of this study was to explore the role of leadership in project success and adaptation complexities in GCC. The conceptual framework consisted of complex adaptive systems and contingency theories. A qualitative approach was used to capture common understandings of project leaders' role and the opportunities and challenges in a multicultural global project environment. Personal interviews were conducted with 25 participants from the oil and gas industry in GCC who were selected using a purposive sampling method. Six themes emerged from an exploratory and comparative analysis, including: adaptable project structure with team and environment dynamics; leadership role and the impermanent multicultural environment; project success definition and the success criteria; aligned performance and governance systems; changing organizational strategy; and team building and the project complexity management. Based on study findings, a framework was created for leading 4 organizational processes in global projects, which includes the environment, team building, leadership selection, and setting of project success criteria. Higher efficiency in leading these processes may contribute to positive social change and support practitioners to promote a project environment for active knowledge integration.

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Autonomy is the most demanded yet hard-to-achieve feature of recent and future software systems. Self-driving cars, mail-delivering drones, automated guided vehicles in production sites, and housekeeping robots need to decide autonomously during most of their operation time. As soon as human intervention becomes necessary, the cost of ownership increases, and this must be avoided. Although the algorithms controlling autonomous systems become more and more intelligent, their hardest opponent is their inflexibility. The more environmental situations such a system is confronted with, the more complexity the control of the autonomous system will have to master. To cope with this challenge, engineers have approached a system design, which adopts feedback loops from nature. The resulting architectural principle, which they call self-adaptive systems, follows the idea of iteratively gathering sensor data, analyzing it, planning new adaptations of the system, and finally executing the plan. Often, adaptation means to alter the system setup, re-wire components, or even exchange control algorithms to keep meeting goals and requirements in the newly appeared situation. Although self-adaptivity helps engineers to organize the vast amount of information in a self-deciding system, it remains hard to deal with the variety of contexts, which involve both environmental influences and knowledge about the system\'s internals. This challenge not only holds for the construction phase but also for verification and validation, including software test. To assure sufficient quality of a system, it must be tested under an enormous and, thus, unmanageable, number of different contextual situations and manual test-cases. This thesis proposes a novel set of methods and model types, which help test engineers to specify precisely what they expect from a self-adaptive system under test. The formal nature of the introduced artifacts allows for automatically generating test-suites or running simulations in the loop so that a qualitative verdict on the system\'s correctness can be gained. Additional to these conceptional contributions, the thesis describes a model-based adaptivity test environment, which test engineers can use for testing actual self-adaptive systems. The implementation includes comprehensive tooling for creating the introduced types of models, generating test-cases, simulating them in the loop, automating tests, and reporting. Composing all enabling components for these tasks constitutes a reference architecture of integrated test environments for self-adaptive systems. We demonstrate the completeness and accuracy of the technical approach together with the underlying concepts by evaluating them in an experimental case study where an autonomous robot interacts with human co-workers. In summary, this thesis proposes concepts for automatically and, thus, efficiently testing self-adaptive systems. The quality, which is fostered by this novel approach, is resilience: the ability of a system to maintain its promises while facing changing environments.:1 Introduction 1 1.1 Problem Description 1 1.2 Overview of Adopted Methods 3 1.3 Hypothesis and Main Contributions 4 1.4 Organization of This Thesis 5 I Foundations 7 2 Background 9 2.1 Self-adaptive Software and Autonomic Computing 9 2.1.1 Common Principles and Components of SAS 10 2.1.2 Concrete Implementations and Applications of SAS 12 2.2 Model-based Testing 13 2.2.1 Testing for Dependability 14 2.2.2 The Basics of Testing 15 2.2.3 Automated Test Design 18 2.3 Dynamic Variability Management 22 2.3.1 Software Product Lines 23 2.3.2 Dynamic Software Product Lines 25 3 Related Work: Existing Research on Testing Self-Adaptive Systems 29 3.1 Testing Context-Aware Applications 30 3.2 The SimSOTA Project 31 3.3 Dynamic Variability in Complex Adaptive Systems (DiVA) 33 3.4 Other Early-Stage Research 34 3.5 Taxonomy of Requirements of Model-based SAS Testing 36 II Methods 39 4 Model-driven SAS Testing 41 4.1 Problem/Solution Fit 41 4.2 Example: Surveillance Drone 43 4.3 Concepts and Models for Testing Self-Adaptive Systems 44 4.3.1 Test Case Generation vs. Simulation in the Loop 44 4.3.2 Incremental Modeling Process 45 4.3.3 Basic Representation Format: Petri Nets 46 4.3.4 Context Variation 50 4.3.5 Modeling Adaptive Behavior 53 4.3.6 Dynamic Context Change 57 4.3.7 Interfacing Context from Behavioral Representation 62 4.3.8 Adaptation Mode Variation 64 4.3.9 Context-Dependent Recon guration 67 4.4 Adequacy Criteria for SAS Test Models 71 4.5 Discussion on the Viability of the Employed Models 71 4.6 Comparison to Related Work 73 4.7 Summary and Discussion 74 5 Model-based Adaptivity Test Environment 75 5.1 Technological Foundation 76 5.2 MATE Base Components 77 5.3 Metamodel Implementation 78 5.3.1 Feature-based Variability Model 79 5.3.2 Abstract and Concrete Syntax for Textual Notations 80 5.3.3 Adaptive Petri Nets 86 5.3.4 Stimulus and Recon guration Automata 87 5.3.5 Test Suite and Report Model 87 5.4 Test Generation Framework 87 5.5 Test Automation Framework 91 5.6 MATE Tooling and the SAS Test Process 93 5.6.1 Test Modeling 94 5.6.2 Test Case Generation 95 5.6.3 Test Case Execution and Test Reporting 96 5.6.4 Interactive Simulation Frontend 96 5.7 Summary and Discussion 97 III Evaluation 99 6 Experimental Study: Self-Adaptive Co-Working Robots 101 6.1 Robot Teaching and Co-Working with WEIR 103 6.1.1 WEIR Hardware Components 104 6.1.2 WEIR Software Infrastructure 105 6.1.3 KUKA LBR iiwa as WEIR Manipulator 106 6.1.4 Self-Adaptation Capabilities of WEIR 107 6.2 Cinderella as Testable Co-Working Application 109 6.2.1 Cinderella Setup and Basic Functionality 109 6.2.2 Co-Working with Cinderella 110 6.3 Testing Cinderella with MATE 112 6.3.1 Automating Test Execution 112 6.3.2 Modeling Cinderella in MATE 113 6.3.3 Testing Cinderella in the Loop 121 6.4 Evaluation Verdict and Summary 123 7 Summary and Discussion 125 7.1 Summary of Contributions 126 7.2 Open Research Questions 127 Bibliography 129 Appendices 137 Appendix Cinderella De nitions 139 1 Cinderella Adaptation Bounds 139 2 Cinderella Self-adaptive Workflow 140

<p>  </p> <p>The proper classification and disposal of waste are crucial in reducing environmental impacts and promoting sustainability. Several solid waste classification systems have been developed over the years, ranging from manual sorting to mechanical and automated sorting. Manual sorting is the oldest and most commonly used method, but it is time-consuming and labor-intensive. Mechanical sorting is a more efficient and cost-effective method, but it is not always accurate, and it requires constant maintenance. Automated sorting systems use different types of sensors and algorithms to classify waste, making them more accurate and efficient than manual and mechanical sorting systems. In this thesis, we propose the development of an intelligent solid waste detection, classification and tracking system using artificial deep learning techniques. To address the limited samples in the TrashNetV2 dataset and enhance model performance, a data augmentation process was implemented. This process aimed to prevent overfitting and mitigate data scarcity issues while improving the model's robustness. Various augmentation techniques were employed, including random rotation within a range of -20° to 20° to account for different orientations of the recycled materials. A random blur effect of up to 1.5 pixels was used to simulate slight variations in image quality that can arise during image acquisition. Horizontal and vertical flipping of images were applied randomly to accommodate potential variations in the appearance of recycled materials based on their orientation within the image. Additionally, the images were randomly scaled to 416 by 416 pixels, maintaining a consistent image size while increasing the dataset's overall size. Further variability was introduced through random cropping, with a minimum zoom level of 0% and a maximum zoom level of 25%. Lastly, hue variations within a range of -20° to 20° were randomly introduced to replicate lighting condition variations that may occur during image acquisition. These augmentation techniques collectively aimed to improve the dataset's diversity and the model's performance. In this study, YOLOv8, EfficientNet-B0 and VGG16 architectures were evaluated, and stochastic gradient descent (SGD) and Adam were used as the optimizer. Although, SGD provided better test accuracies compared to Adam. </p> <p>Among the three models, YOLOv8 showed the best performance, with the highest average precision mAP of 96.5%. YOLOv8 emerges as the top performer, with ROC values varying from 92.70% (Metal) to 98.40% (Cardboard). Therefore, the YOLOv8 model outperforms both VGG16 and EfficientNet in terms of ROC values and mAP. The findings demonstrate that our novel classifier tracker system made of YOLOv8, and supervision algorithms surpass conventional deep learning methods in terms of precision, resilience, and generalization ability. Our contribution to waste management is in the development and implementation of an intelligent solid waste detection, classification, and tracking system using computer vision and deep learning techniques. By utilizing computer vision and deep learning algorithms, our system can accurately detect, classify, and localize various types of solid waste on a moving conveyor, including cardboard, glass, metal, paper, and plastic. This can significantly improve the efficiency and accuracy of waste sorting processes.</p> <p>This research provides a promising solution for detection, classification, localization, and tracking of solid waste materials in real time system, which can be further integrated into existing waste management systems. Through comprehensive experimentation and analysis, we demonstrate the superiority of our approach over traditional methods, with higher accuracy and faster processing times. Our findings provide a compelling case for the implementation of intelligent solid waste sorting.</p>

<p>Resilience is studied as a systemic property in several disciplines such as engineering, psychology, systems biology, and ecological sciences. Yet, the system view on resilience is not pervasive in management science. This dissertation is on Enterprise Resilience, which is an emerging topic within the fields of organization and management science. Corporate enterprises are viewed as type 1 complex adaptive systems (CAS) operating within an external business environment. Thus, perturbations occurring in the environment affect enterprises, whose resilience then depends on their adaptive response to them. Therefore, the focus is on system perturbances and on investigating drivers of the enterprises’ adaptive response. As a result, enterprise resilience is more granularly defined as an enterprise’s ability to continually remain valuable to stakeholders by simultaneously managing short-term shocks and long-term stressors. This re-definition brings forth an actionable pathway to enterprise resilience- the pursuit of improved management of the enterprise’s risk and growth management functions.</p><p>Two challenging issues plaguing the risk and growth functions are the lack of a comprehensive understanding of risks (especially of unknowns) and their inter-connections, and a weak link between risk management and the enterprise’s growth strategy intended to continually and increasingly generate value. This work addresses both issues via the development of an enterprise-agnostic comprehensive risk typology, and by building a conceptual link between risk and growth strategy through the business model construct and its use in the study of repeatable patterns of innovation. Therefore, this work develops one pathway toward enterprise resilience i.e., via improved risk management and systematic growth management. Furthermore, it advances knowledge by bridging the theoretical conceptualization of an enterprise as a CAS1 into actionable methods for practice in the form of risk management tools and systematic innovation frameworks that aid the enterprise’s adaptive response.</p><p>The interdisciplinary dissertation develops hypotheses and employs appropriate qualitative and quantitative methods to test them. Overall, a theory building process is undertaken using the constructionist school of thought and using methods based in inductive logic such as the scholarship of integration, thematic analysis, and case studies. Additionally, to achieve wide and comprehensive coverage, data-driven quantitative methods using advanced computing such as data mining, machine learning, and natural language processing are employed.</p>

[pt] Nos últimos anos, foi desenvolvido um número significativo de sistemas autoadaptativos (i.e.: sistemas capazes de saber o que está acontecendo sobre si mesmo e que, consequentemente, implementam parcialmente a qualidade de consciência). A literatura tem pesquisado extensivamente o uso da engenharia de requisitos orientada a metas e o uso da arquitetura de referência MAPE (Monitor-Analyze-Plan-Execute) para o desenvolvimento de sistemas autoadaptativos. Entretanto, construir tais sistemas com base em estratégias de referência não é trivial, podendo resultar em problemas estruturais que impactam negativamente alguns atributos de qualidade do produto final (e.g.: reusabilidade, modularidade, modificabilidade e entendibilidade). Neste contexto, estratégias de reengenharia para a reorganização de tais sistemas são pouco exploradas, limitando-se a recuperar e a reestruturar a lógica da adaptação em modelos de baixo nível. Esta prática mantém a dificuldade do tratamento da qualidade de consciência como um requisito não funcional (RNF) de primeira classe, impactando diretamente na seleção da arquite-tura e implementação do sistema. Nossa pesquisa visa mitigar esse problema atra-vés de uma estratégia de reengenharia de sistemas autoadaptativos, centrada no RNF de consciência de software, com vistas a auxiliar na remoção de alguns problemas recorrentes na implementação do MAPE conforme a literatura. A estratégia de reengenharia está organizada em quatro subprocessos: (A) recuperar a intencio-nalidade do sistema com ênfase em suas metas de consciência, gerando um modelo de metas AS-IS; (B) especificar o modelo de metas TO-BE reutilizando um conjunto de SRconstructs para operacionalizar o RNF de consciência de software conforme o padrão MAPE; (C) redesenhar o sistema revisando as operacionalizações de consciência e selecionando as tecnologias para implementar o MAPE, e; (D) finalmente, reimplementar o sistema conforme nova estrutura, adicionando metainformações de código para manter a rastreabilidade para o mecanismo de autoadaptação visando facilitar novas evoluções. O escopo da nossa pesquisa são sistemas autoadaptativos orientados a objetos (OO), utilizando o framework i como linguagem para os modelos orientados a metas. Nossos resultados de avaliações em sistemas auto-adaptativos OO desenvolvidos em Java para dispositivos móveis com Android demonstram que a estratégia auxilia no realinhamento do sistema com as boas práticas recomendadas pela literatura facilitando futuras evoluções. / [en] In recent years, a significant number of self-adaptive systems (i.e.: systems capable of knowing what is happening about themselves, and consequently partially implementing the quality of awareness) have been developed. The literature has extensively researched the use of goal oriented requirements engineering and the use of the MAPE (Monitor-Analyze-Plan-Execute) reference architecture for the development of self-adaptive systems. However, building such systems based on reference strategies is not trivial, it can result in structural problems that negatively impact some quality attributes of the final product (e.g.: reusability, modularity, modifiability and understandability). In this context, reengineering strategies for the reorganization of such systems are poor explored, and they are limited to recovering and restructuring the logic of adaptation in low-level models. This approach keeps the difficulty of treating the awareness quality as a first-class non-functional re-quirement (NFR) directly affecting architecture selection and implementation of the system. Our research aims to mitigate this problem through a strategy of reengi-neering self-adaptive systems, centered on software awareness as an NFR. This strategy will assist in the removal of some recurring problems in the implementation of MAPE according to the literature. The reengineering strategy is organized into four sub-processes: (A) recover the intentionality of the system with an emphasis on its awareness goals, generating an AS-IS goal model; (B) specify the TO-BE goal model by reusing a set of SRconstructs to operationalize the software awareness NFR according to the MAPE standard; (C) redesign the system by reviewing the operationalizations of awareness and selecting the technologies to implement the MAPE, and; (D) finally, reimplement the system according to a new structure, add-ing code metadata to maintain traceability for the self-adaptation mechanism in or-der to facilitate new evolutions. The scope of our research is object-oriented (OO) self-adaptive systems using the i framework as a language for goal-oriented models. Our results of evaluations, for OO self-adaptive systems developed in Java for mobile devices with Android, show that the strategy helps in realigning the system with the best practices recommended by the, facilitating future developments.

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