3,369 research outputs found

    The telecoupled sustainability impacts of global agricultural value chains:Assessing the cross-scale sustainability impacts of the cocoa sector

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    Agriculture is a major contributor to the global environmental crisis. Natural ecosystems are being replaced by agricultural land, which leads to the extinction of species and the release of tons of carbon emissions. Global agricultural value chains (GVCs) have grown due to the intensification of international trade. While GVCs have undeniably created economic opportunities for the agriculture sector, they have also led to the escalation of local environmental issues. Several initiatives have been implemented to reduce the negative impacts of agriculture, including government regulations, sustainability certification labels, and voluntary sustainability commitments. However, the effectiveness of these initiatives has been questioned due to several reasons, including the mismatches between the scale of the problem and the solution, the lack of monitoring and verification of sustainability actions, and their weak enforcement. Sustainability initiatives are informed by studies assessing the impacts of agriculture that often only focus on local impacts, while disregarding larger-scale – telecoupled– dynamics that can trigger impacts across geographic and temporal scales. This thesis aims to help bridge these knowledge gaps by examining the impacts of agricultural GVCs across scales, studying the role of GVC’s configuration in modulating these impacts and investigating the role of GVC actors in mitigating sustainability risks across scales. The global cocoa value chain is used as a case study. Chapter 2 examines various impact assessment methods and their ability to capture the effects caused by telecoupled dynamics across different scales. The study concludes that no single method is sufficient to capture all telecoupled cross-scale dynamics and that the integration of different methods is necessary to bridge gaps between methods and complement their scope. Chapter 3 implements the recommendations outlined in Chapter 2 by analyzing the impacts caused by cocoa agroforestry and cocoa full-sun production in Ghana. Impacts on carbon, biodiversity stocks, and environmental pollution were analyzed within and beyond the farm-level. This chapter reveals that findings drawn from farm-level assessments can contradict those from landscape-level assessments. Decision-makers focused should be wary of extrapolating farm-level assessment results to larger scales. Chapter 4 expands the scope to the global scale by examining the role of the cocoa GVC configuration on the capacity of the sector to address sustainability challenges across scales. The chapter identifies different types of cocoa traders, their market dominance, and sustainability commitments. The chapter highlights that to address the telecoupled impacts of the cocoa GVC, coordinated action between traders is required, along with government interventions to balance power asymmetries. Chapter 5 measured the degree to which cocoa traders, as identified in Chapter 4, are exposed to deforestation and climate change. This chapter highlights that sustainability challenges in agricultural value chains cannot be resolved in isolation as farming systems are constantly interacting with other farming systems and land competing sectors. To avoid displacing negative impacts across scales, it is necessary to have a coordinated and collaborative effort from stakeholders and sectors involved in making decisions related to land use. This thesis shows that addressing the telecoupled impacts caused by agricultural value chains needs a good understanding of the cause-effect dynamics at play. This requires the quantification of impacts caused by agriculture across scales and the characterization of the GVC network of actors modulating these impacts. Interdisciplinary methods need to be leveraged and integrated to generate actionable insights. The findings of this thesis can assist decision-makers and private actors in devising customized sustainability strategies, prioritizing action, and addressing the most vulnerable hotspots while being mindful of global teleconnections and avoiding spillovers

    Documenting Knowledge Graph Embedding and Link Prediction using Knowledge Graphs

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    In recent years, sub-symbolic learning, i.e., Knowledge Graph Embedding (KGE) incorporated with Knowledge Graphs (KGs) has gained significant attention in various downstream tasks (e.g., Link Prediction (LP)). These techniques learn a latent vector representation of KG's semantical structure to infer missing links. Nonetheless, the KGE models remain a black box, and the decision-making process behind them is not clear. Thus, the trustability and reliability of the model's outcomes have been challenged. While many state-of-the-art approaches provide data-driven frameworks to address these issues, they do not always provide a complete understanding, and the interpretations are not machine-readable. That is why, in this work, we extend a hybrid interpretable framework, InterpretME, in the field of the KGE models, especially for translation distance models, which include TransE, TransH, TransR, and TransD. The experimental evaluation on various benchmark KGs supports the validity of this approach, which we term Trace KGE. Trace KGE, in particular, contributes to increased interpretability and understanding of the perplexing KGE model's behavior

    Organizing sustainable development

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    The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility

    Distributed Ledger Technology (DLT) Applications in Payment, Clearing, and Settlement Systems:A Study of Blockchain-Based Payment Barriers and Potential Solutions, and DLT Application in Central Bank Payment System Functions

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    Payment, clearing, and settlement systems are essential components of the financial markets and exert considerable influence on the overall economy. While there have been considerable technological advancements in payment systems, the conventional systems still depend on centralized architecture, with inherent limitations and risks. The emergence of Distributed ledger technology (DLT) is being regarded as a potential solution to transform payment and settlement processes and address certain challenges posed by the centralized architecture of traditional payment systems (Bank for International Settlements, 2017). While proof-of-concept projects have demonstrated the technical feasibility of DLT, significant barriers still hinder its adoption and implementation. The overarching objective of this thesis is to contribute to the developing area of DLT application in payment, clearing and settlement systems, which is still in its initial stages of applications development and lacks a substantial body of scholarly literature and empirical research. This is achieved by identifying the socio-technical barriers to adoption and diffusion of blockchain-based payment systems and the solutions proposed to address them. Furthermore, the thesis examines and classifies various applications of DLT in central bank payment system functions, offering valuable insights into the motivations, DLT platforms used, and consensus algorithms for applicable use cases. To achieve these objectives, the methodology employed involved a systematic literature review (SLR) of academic literature on blockchain-based payment systems. Furthermore, we utilized a thematic analysis approach to examine data collected from various sources regarding the use of DLT applications in central bank payment system functions, such as central bank white papers, industry reports, and policy documents. The study's findings on blockchain-based payment systems barriers and proposed solutions; challenge the prevailing emphasis on technological and regulatory barriers in the literature and industry discourse regarding the adoption and implementation of blockchain-based payment systems. It highlights the importance of considering the broader socio-technical context and identifying barriers across all five dimensions of the social technical framework, including technological, infrastructural, user practices/market, regulatory, and cultural dimensions. Furthermore, the research identified seven DLT applications in central bank payment system functions. These are grouped into three overarching themes: central banks' operational responsibilities in payment and settlement systems, issuance of central bank digital money, and regulatory oversight/supervisory functions, along with other ancillary functions. Each of these applications has unique motivations or value proposition, which is the underlying reason for utilizing in that particular use case

    Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation

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    Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.Comment: 30 pages, 5 figures, under second revie

    Harnessing the Potential of Blockchain in DevOps: A Framework for Distributed Integration and Development

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    As the use of DevOps practices continues to grow, organizations are seeking ways to improve collaboration, speed up development cycles, and increase security, transparency, and traceability. Blockchain technology has the potential to support these goals by providing a secure, decentralized platform for distributed integration and development. In this paper, we propose a framework for distributed DevOps that utilizes the benefits of blockchain technology that can eliminate the shortcomings of DevOps. We demonstrate the feasibility and potential benefits of the proposed framework that involves developing and deploying applications in a distributed environment. We present a benchmark result demonstrating the effectiveness of our framework in a real-world scenario, highlighting its ability to improve collaboration, reduce costs, and enhance the security of the DevOps pipeline. Conclusively, our research contributes to the growing body of literature on the intersection of blockchain and DevOps, providing a practical framework for organizations looking to leverage blockchain technology to improve their development processes.Comment: pages 10, figures

    Enhancing the forensic comparison process of common trace materials through the development of practical and systematic methods

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    An ongoing advancement in forensic trace evidence has driven the development of new and objective methods for comparing various materials. While many standard guides have been published for use in trace laboratories, different areas require a more comprehensive understanding of error rates and an urgent need for harmonizing methods of examination and interpretation. Two critical areas are the forensic examination of physical fits and the comparison of spectral data, which depend highly on the examiner’s judgment. The long-term goal of this study is to advance and modernize the comparative process of physical fit examinations and spectral interpretation. This goal is fulfilled through several avenues: 1) improvement of quantitative-based methods for various trace materials, 2) scrutiny of the methods through interlaboratory exercises, and 3) addressing fundamental aspects of the discipline using large experimental datasets, computational algorithms, and statistical analysis. A substantial new body of knowledge has been established by analyzing population sets of nearly 4,000 items representative of casework evidence. First, this research identifies material-specific relevant features for duct tapes and automotive polymers. Then, this study develops reporting templates to facilitate thorough and systematic documentation of an analyst’s decision-making process and minimize risks of bias. It also establishes criteria for utilizing a quantitative edge similarity score (ESS) for tapes and automotive polymers that yield relatively high accuracy (85% to 100%) and, notably, no false positives. Finally, the practicality and performance of the ESS method for duct tape physical fits are evaluated by forensic practitioners through two interlaboratory exercises. Across these studies, accuracy using the ESS method ranges between 95-99%, and again no false positives are reported. The practitioners’ feedback demonstrates the method’s potential to assist in training and improve peer verifications. This research also develops and trains computational algorithms to support analysts making decisions on sample comparisons. The automated algorithms in this research show the potential to provide objective and probabilistic support for determining a physical fit and demonstrate comparative accuracy to the analyst. Furthermore, additional models are developed to extract feature edge information from the systematic comparison templates of tapes and textiles to provide insight into the relative importance of each comparison feature. A decision tree model is developed to assist physical fit examinations of duct tapes and textiles and demonstrates comparative performance to the trained analysts. The computational tools also evaluate the suitability of partial sample comparisons that simulate situations where portions of the item are lost or damaged. Finally, an objective approach to interpreting complex spectral data is presented. A comparison metric consisting of spectral angle contrast ratios (SCAR) is used as a model to assess more than 94 different-source and 20 same-source electrical tape backings. The SCAR metric results in a discrimination power of 96% and demonstrates the capacity to capture information on the variability between different-source samples and the variability within same-source samples. Application of the random-forest model allows for the automatic detection of primary differences between samples. The developed threshold could assist analysts with making decisions on the spectral comparison of chemically similar samples. This research provides the forensic science community with novel approaches to comparing materials commonly seen in forensic laboratories. The outcomes of this study are anticipated to offer forensic practitioners new and accessible tools for incorporation into current workflows to facilitate systematic and objective analysis and interpretation of forensic materials and support analysts’ opinions

    A Tale of Two Trees: A Comparative Study on the Effects of Scale and Biodiversity Efforts in Ghana’s Cocoa and Shea Production Networks

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    This thesis presents two case studies on Ghana’s cocoa and shea production networks, focusing on the effects of scale on biodiversity sustainability dissemination. Analysis is done through my unique holistic framework for action-based production network analysis, which provides a robust and multiscalar analysis to answer my main research question How does scale affect biodiversity sustainability throughout Ghana’s cocoa and shea production networks? The two case studies focus on Ghana’s cocoa and shea production network coordination and the action taken up by different levels of actors within scale, their considerations of, approaches to, and outcomes of biodiversity sustainability dissemination throughout their Ghanaian cocoa production networks. These case studies focus on the history and context of the cocoa and shea sectors as they function within Ghana’s agricultural industry, the influencers, and barriers to biodiversity sustainability dissemination throughout the studied production networks, and the effects of scale on this sustainability attainment. My research is support through primary data collected in Ghana and secondary data. The two case studies are then cross-analysed to draw out the commonalities in context, issues faced, and effects of scale on the studied sustainability aspects. The findings of this research show that in order to achieve biodiversity sustainability, social sustainability must be incorporated into production network coordination and that the level of actors’ scale and scalar approach to network coordination significantly impact achievement of biodiversity sustainability dissemination. The results of this thesis are novel in the fact that it combines several streams of analytical consideration into a holistic framework and presents clear and applicable results that can significantly impact the approach to sustainability dissemination throughout global production networks in an equitable manner that is fit to the context within which production takes place

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Business Functions Capabilities and Small and Medium Enterprises’ Internationalization

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    Ineffective global expansion can adversely affect small and medium enterprises (SMEs) business outcomes. Business leaders are concerned with developing effective global expansion strategies to penetrate potential international markets, thus enhancing sustainability. Grounded in the business management systems theory, the purpose of this qualitative multi-case study was to explore strategies that leaders of Sub-Saharan Africa manufacturing SMEs use for global expansion. The participants were five manufacturing value-adding SME leaders participating in export markets. Using Yin’s five steps data analysis process, six themes emerged: (a) enterprise characterization, (b) understanding the enterprise’s product, (c) intra-enterprise factor-based strategies for export participation, (d) the enterprise’s external factor-based strategies for successful export venture, (e) global expansion strategies, and (f) serendipitous findings. A key recommendation for SME leaders is to analyze the critical components of their products and prepare to adjust them to the demand dimensions of the target market. The implications for positive social change include the potential to increase the enterprise’s wealth, increase employment, reduce poverty for all value chain participants, and growth in gross domestic product
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