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Effective orchestration is a critical driver of success in quantum computing innovation (QCI) ecosystems. Heterogeneous actor goals, roles, and power relations, however, produce tensions that confront orchestrators with paradoxical situations in which they must navigate trade-offs between competing demands. To orchestrate an ecosystem effectively, these tensions must be recognized and balanced rather than eliminated. Prior research has largely overlooked the role of paradoxes in ecosystem orchestration or has focused mainly on interfirm relationships. This study addresses this gap by examining a government led national QCI ecosystem that includes firms, research organizations, funding bodies, and governmental actors. Using an explorative case study with 15 informants from the Finnish QCI ecosystem and drawing on paradox theory as an analytical lens, we identify core paradoxical tensions and show how they challenge ecosystem orchestration. We contribute nuanced insights into the origins and dynamics of paradoxical tensions and discuss the implications for orchestrating multi-actor ecosystems.

This study analyzes the impacts of economic growth on ecosystem in Turkiye. The study uses annual data for the period 1995-2021 and the ARDL method. The study utilizes the Ecosystem Vitality Index, a sub-dimension of the Environmental Performance Index. In addition, seven models were constructed to assess in detail the impact of economic growth on different dimensions of the ecosystem. The results show that economic growth has a significant impact in all models analyzed. However, the direction of this impact differs across ecosystem components. Economic growth is found to have a positive impact on agriculture and water resources. In these models, a 1% increase in GDP increases the agriculture and water resources indices by 0.074-0.672%. In contrast, economic growth has a negative impact on biodiversity and habitat, ecosystem services, fisheries, acid rain and total ecosystem vitality. In these models, a 1% increase in GDP reduces the indices of biodiversity and habitat, ecosystem services, fisheries, acid rain and total ecosystem vitality by 0.101-2.144%. The results suggest that the environmental costs of economic growth processes need to be considered. Environmentally friendly po

United Nations have declared the current decade (2021-2030) as the "UN Decade on Ecosystem Restoration" to join R\&D forces to fight against the ongoing environmental crisis. Given the ongoing degradation of earth ecosystems and the related crucial services that they offer to the human society, ecosystem restoration has become a major society-critical issue. It is required to develop rigorously software applications managing ecosystem restoration. Reliable models of ecosystems and restoration goals are necessary. This paper proposes a rigorous approach for ecosystem requirements modeling using formal methods from a model-driven software engineering point of view. The authors describe the main concepts at stake with a metamodel in UML and introduce a formalization of this metamodel in Alloy. The formal model is executed with Alloy Analyzer, and safety and liveness properties are checked against it. This approach helps ensuring that ecosystem specifications are reliable and that the specified ecosystem meets the desired restoration goals, seen in our approach as liveness and safety properties. The concepts and activities of the approach are illustrated with CRESTO, a real-world r

The International Astronomical Union's Office of Astronomy for Development (IAU OAD) uses astronomy as a tool to address societal challenges and contribute to sustainable development. Building on more than a decade of project funding and implementation, the OAD has developed a portfolio of flagship projects that represent tested and scalable applications of Astronomy for Development across thematic areas including socio-economic development, science diplomacy, skills development, inequality reduction, and technology transfer. To support the growth and long-term sustainability of these initiatives, the OAD has established the Flagship Ecosystem, a framework built around four interconnected pillars: Resources, Training, Community, and Implementation. This paper presents an overview of the OAD Flagship projects, the structure and components of the Flagship Ecosystem, and explores how it supports the translation of astronomy-based interventions into sustainable development outcomes. The ecosystem provides open-access resources, capacity-building opportunities, communities of practice, funding mechanisms, and evidence-generation activities that enable individuals and organizations to im

Ecosystem models are often used to predict the consequences of management decisions in applied ecology, including fisheries management and threatened species conservation. These models are high-dimensional, parameter-rich, and nonlinear, yet limited data is available to calibrate them, and they are rarely tested or validated. Consequently, the accuracy of their forecasts, and their utility as decision-support tools is a matter of debate. In this paper, we calibrate ecosystem models to time-series data from 110 different experimental microcosm ecosystems, each containing between three and five interacting species. We then assess how often these calibrated models offer accurate and useful predictions about how the ecosystem will respond to a set of standard management interventions. Our results show that for each timeseries dataset, a large number of very different parameter sets offer equivalent, good fits. However, these calibrated ecosystem models have poor predictive accuracy when forecasting future dynamics and offer ambiguous predictions about how species in the ecosystem will respond to management interventions. Closer inspection reveals that the ecosystem models fail because

The last decade has witnessed a rapid advancement of generative AI technology that significantly scaled the accessibility of AI-generated non-consensual intimate images (AIG-NCII), a form of image-based sexual abuse that disproportionately harms and silences women and girls. There is a patchwork of commendable efforts across industry, policy, academia, and civil society to address AIG-NCII. However, these efforts lack a shared, consistent mental model that clearly situates the technologies they target within the context of a large, interconnected, and ever-evolving technological ecosystem. As a result, interventions remain siloed and are difficult to evaluate and compare, leading to a reactive cycle of whack-a-mole. In this paper, we contribute the first comprehensive AIG-NCII technological ecosystem that maps and taxonomizes 11 categories of technologies facilitating the creation, distribution, proliferation and discovery, infrastructural support, and monetization of AIG-NCII. First, we build and visualize the ecosystem through a synthesis of over a hundred primary sources from researchers, journalists, advocates, policymakers, and technologists. Then, we conduct two detailed walk

Regular dependency updates protect dependent software components from upstream bugs, security vulnerabilities, and poor code quality. Measures of dependency updates across software ecosystems involve two key dimensions: the time span during which a release is being newly adopted (adoption lifespan) and the extent of adoption across the ecosystem (adoption reach). We examine correlations between adoption patterns in the Maven software ecosystem and two factors: the magnitude of code modifications (extent of modifications affecting the meaning or behavior of the code, henceforth called ``semantic change") in an upstream dependency and the relative maintenance rate of upstream packages. Using the Goblin Weaver framework, we find adoption latency in the Maven ecosystem follows a log-normal distribution while adoption reach exhibits an exponential decay distribution.

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem

Currently, little is known about the structure of the Cargo ecosystem and the potential for vulnerability propagation. Many empirical studies generalize third-party dependency governance strategies from a single software ecosystem to other ecosystems but ignore the differences in the technical structures of different software ecosystems, making it difficult to directly generalize security governance strategies from other ecosystems to the Cargo ecosystem. To fill the gap in this area, this paper constructs a knowledge graph of dependency vulnerabilities for the Cargo ecosystem using techniques related to knowledge graphs to address this challenge. This paper is the first large-scale empirical study in a related research area to address vulnerability propagation in the Cargo ecosystem. This paper proposes a dependency-vulnerability knowledge graph parsing algorithm to determine the vulnerability propagation path and propagation range and empirically studies the characteristics of vulnerabilities in the Cargo ecosystem, the propagation range, and the factors that cause vulnerability propagation. Our research has found that the Cargo ecosystem's security vulnerabilities are primarily

An increase in diverse technology stacks and third-party library usage has led developers to inevitably switch technologies. To assist these developers, maintainers have started to release their libraries to multiple technologies, i.e., a cross-ecosystem library. Our goal is to explore the extent to which these cross-ecosystem libraries are intertwined between ecosystems. We perform a large-scale empirical study of 1.1 million libraries from five different software ecosystems, i.e., PyPI for Python, CRAN for R, Maven for Java, RubyGems for Ruby, and NPM for JavaScript to identify 4,146 GitHub projects that release libraries to these five ecosystems. Analyzing their contributions, we first find that a significant majority (median of 37.5%) of contributors of these cross-ecosystem libraries come from a single ecosystem, while also receiving a significant portion of contributions (median of 24.06%) from outside their target ecosystems. We also find that a cross-ecosystem library is written using multiple programming languages. Specifically, three (i.e., PyPI, CRAN, RubyGems) out of the five ecosystems has the majority of source code is written using languages not specific to that ecos

Foundation models (e.g. ChatGPT, StableDiffusion) pervasively influence society, warranting immediate social attention. While the models themselves garner much attention, to accurately characterize their impact, we must consider the broader sociotechnical ecosystem. We propose Ecosystem Graphs as a documentation framework to transparently centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical (e.g. how Bing relies on GPT-4) and social (e.g. how Microsoft relies on OpenAI) relationships. To supplement the graph structure, each asset is further enriched with fine-grained metadata (e.g. the license or training emissions). We document the ecosystem extensively at https://crfm.stanford.edu/ecosystem-graphs/. As of March 16, 2023, we annotate 262 assets (64 datasets, 128 models, 70 applications) from 63 organizations linked by 356 dependencies. We show Ecosystem Graphs functions as a powerful abstraction and interface for achieving the minimum transparency required to address myriad use cases. Therefore, we envision Ecosystem Graphs will be a community-maintained resource that

The availability of vast amounts of publicly accessible data of source code and the advances in modern language models, coupled with increasing computational resources, have led to a remarkable surge in the development of large language models for code (LLM4Code, for short). The interaction between code datasets and models gives rise to a complex ecosystem characterized by intricate dependencies that are worth studying. This paper introduces a pioneering analysis of the code model ecosystem. Utilizing Hugging Face -- the premier hub for transformer-based models -- as our primary source, we curate a list of datasets and models that are manually confirmed to be relevant to software engineering. By analyzing the ecosystem, we first identify the popular and influential datasets, models, and contributors. The popularity is quantified by various metrics, including the number of downloads, the number of likes, the number of reuses, etc. The ecosystem follows a power-law distribution, indicating that users prefer widely recognized models and datasets. Then, we manually categorize how models in the ecosystem are reused into nine categories, analyzing prevalent model reuse practices. The top

In abstract terms, ecosystem ecology is about determining when two ecosystems, superficially different, are alike in some deeper way. An external observer can choose any ecosystem property as being important. In contrast, two ecosystems are equivalent from the point of view of the organisms they contain if and only if for each species, the proportional population growth rate does not differ between the ecosystems. Comparative studies of ecosystems should therefore focus on patterns in proportional population growth rates, rather than patterns in other properties such as relative abundances. Popular activities such as measuring dissimilarity, and representing dissimilarity via ordination, can then be done from the point of view of the organisms in ecosystems. Summarizing the state of an ecosystem under this approach remains challenging. In general, the dynamics on equivalence classes of ecosystems defined in this way are structurally different from the dynamics of ecosystems as seen by an external observer. This may limit the extent to which natural selection can act on ecosystem structure.

To completely understand the effects of urban ecosystems, the effects of ecosystem disservices should be considered along with the ecosystem services and require more research attention. In this study, we tried to better understand its formation through the use of cascade flowchart and classification systems and compare their effects with ecosystem services. It is vitally important to differentiate final and intermediate ecosystem disservices for understanding the negative effects of the ecosystem on human well-being. The proposed functional classification of EDS (i.e. provisioning, regulating and cultural EDS) should also help better bridging EDS and ES studies. In addition, we used Beijing as a case study area to value the EDS caused by urban ecosystems and compare the findings with ES values. The results suggested that although EDS caused great financial loss the potential economic gain from ecosystem services still significantly outweigh the loss. Our study only sheds light on valuating the net effects of urban ecosystems. In the future, we believe that EDS valuation should be at least equally considered in ecosystem valuation studies to create more comprehensive and sustainabl

Ecosystems, which are intricate amalgams of biological communities and their surrounding environments, continually evolve under the influence of their myriad interactions. The world is currently facing intensifying environmental fluctuations. Understanding general trends in ecosystem transformations in response to environmental fluctuations and elucidating the underlying mechanisms are thus critical challenges. In this study, we used a model ecosystem approach to investigate ecosystem alterations caused by escalating environmental fluctuations. We analyzed two distinct models: a stochastic ecosystem model with a spatial structure, and a differential equation model for resource competition. We found that environmental fluctuations tend to shift multi-species coexistence toward the dominance of specific species. We also categorized biological species as specialists or generalists and discovered that which of these groups becomes the dominant species depends on the intensity and frequency of environmental fluctuations. We also determined that a qualitative change in the diversity-stability relationship depends on the period of environmental fluctuations. These results underscore the n

We investigate the thermodynamics as well as the population dynamics of ecosystems based on a stochastic approach in which the number of individuals of the several species of the ecosystem are treated as stochastic variables. The several species are connected by feeding relationships that are understood as unidirectional processes in which a certain amount of biomass is exchanged between species. We show that the equations for the averages in the numbers of individuals are that given by the deterministic approach. We determine the fluxes of mass, energy, and entropy as well as the rate of the entropy production. This last quantity, which has a central role in the present stochastic approach, is obtained by a formula appropriate for unidirectional transitions. The flux of energy across the ecosystem is shown to be proportional to the flux of entropy to the environment.

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, with measures originating from theoretical ecology, to confirm its likeness to a biological e

Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and may get removed, introducing compilation failures to dependent packages. Even worse, their impacts propagate through transitive dependencies, causing large-scale failures in the whole ecosystem. Although RUF is widely used in Rust, previous research has primarily concentrated on Rust code safety, with the usage and impacts of RUF from the Rust compiler remaining unexplored. Therefore, we aim to bridge this gap by systematically analyzing the RUF usage and impacts in the Rust ecosystem. We propose novel techniques for extracting RUF precisely, and to assess its impact on the entire ecosystem quantitatively, we accurately resolve package dependencies. We have analyzed the whole Rust ecosystem with 590K package versions and 140M transitive dependencies. Our study shows that the Rust ecosystem uses 1000 different RUF, and at most 4

A primary motivation for our research in digital ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. Here, we discuss how different computing technologies can contribute to providing the necessary self-organising features, including Multi-Agent Systems, Service-Oriented Architectures, and distributed evolutionary computing. The potential for exploiting these properties in digital ecosystems is considered, suggesting how several key features of biological ecosystems can be exploited in Digital Ecosystems, and discussing how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, considering the self-organised diversity of its evolving agent populations relative to the user request behaviour.