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Item type:Publication, Relevance of international partnerships in the implementation of the UN Sustainable Development Goals(Nature Research, 2022-12-01)To achieve Sustainable Development Goal SDG 17, which focuses on international cooperation, partnerships, will be vital. In this comment, we examine the key obstacles such as vested economic interests that will need to be overcome for the successful implementation of SDG 17. Sustainable Development Goal 17 focuses on partnerships that can enable the achievement of other SDGs. In this comment the authors examine the obstacles to the success of SDG 17 and suggest measures to overcome these. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimal plant growth through thermo mechatronic analysis(International Institute of Informatics and Cybernetics, 2022-08-01)This work is described as a proposal to apply modern control techniques and automation tools for optimal plant growth, also it was based on key agricultural strategies that were developed by ancient civilizations such as the Inca Empire. Many of them ancient techniques including the Inca engineering of andenes were forgotten or set aside through time. In this research, however, some of these key techniques are revisited to analyze and evaluate optimal plant growth using sensors and actuators that were not available in ancient civilizations. In addition, predictive and adaptive mathematical models are used for plant growth analysis of thermodynamic parameters such as temperature, humidity and potential of Hydrogen (pH). Furthermore, there were compared performances of sensors (electromechanical sensors) with designed sensors that were based in nanostructures, because of better study of the plant growth techniques. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Solving Dicke superradiance analytically: A compendium of methods(American Physical Society, 2026-01-01)We present several analytical approaches to the Dicke superradiance problem, which involves determining the time evolution of the density operator for an initially inverted ensemble of N identical two-level systems undergoing collective spontaneous emission. This serves as one of the simplest cases of open quantum system dynamics that allows for a fully analytical solution. We explore multiple methods to tackle this problem, yielding a solution valid for any time and any number of emitters. These approaches range from solving coupled rate equations and identifying exceptional points in non-Hermitian evolution to employing combinatorial and probabilistic techniques, as well as utilizing a quantum jump unraveling of the master equation. The analytical solution is expressed as a residue sum obtained from a contour integral in the complex plane, suggesting the possibility of fully analytical solutions for a broader class of open quantum system dynamics problems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scenario design: Key stages and methods for foresight application(RELX Group (Netherlands), 2025-01-01)1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Quantifying Emergent Behaviors in Agent-Based Models using Mean Information Gain(RELX Group (Netherlands), 2025-01-01)5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Extensions of a family of linear cycle sets(Taylor & Francis, 2026-01-01)This paper explores the cohomology of linear cycle sets, focusing on extensions of a specific linear cycle set H by an abelian group I. We derive explicit formulas for the second cohomology group, which classifies these extensions, and establish conditions under which the extensions are fully determined. Key results include a characterization of extensions when I lies in the socle of the extended structure and H is trivial, and the construction of explicit examples for both trivial and non-trivial cases. The paper provides a systematic approach to understanding the structure of these extensions, with applications to various families of abelian groups. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Data driven identification and current control on dual active bridge systems for low power applications(2026-07-01)This work presents a data-driven methodology for the identification and control of Dual Active Bridge (DAB) DC–DC converters, aimed at reducing the complexity associated with highly nonlinear analytical models. By exploiting an angle phase-shift representation instead of a detailed PWM-based model, the proposed approach simplifies both system excitation and identification while preserving the essential power-transfer dynamics of the converter. A frequency-domain control strategy is subsequently developed based on the identified model, enabling systematic loop-shaping and robust controller design. The resulting discrete-time controller achieves accurate reference tracking and stable regulation in simulation tests, demonstrating that the proposed pipeline provides an effective and practical alternative to conventional model-based control approaches for DAB systems Key words. System identification, non lineal systems, dual active bridge, data-driven control. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Addressing challenges and constructing a blueprint for effective generative AI integration in business operations(Taylor & Francis, 2025-01-01)This study addresses the critical challenges and proposes a comprehensive blueprint for effectively integrating Generative Artificial Intelligence (GenAI) into business operations. GenAI has emerged as a transformative force, offering significant competitive advantages to early adopters. However, a substantial gap remains in understanding the technical, organisational, and governance challenges associated with GenAI implementation. This research utilises a mixed-methods approach, incorporating a systematic literature review and expert interviews to develop a blueprint for deploying GenAI in organisations. The blueprint emphasises the alignment of GenAI initiatives with business objectives, the establishment of responsible governance framework, and the development of a technical infrastructure. It also highlights the decision-making process regarding the use of low-code/no-code platforms versus pro-code environments, as well as the impact of GenAI on both customer and employee experiences. Additionally, the study underscores the importance of organisational readiness, change management, and continuous improvement to foster a culture that embraces AI-driven innovation. By providing detailed insights into both technical and organisational aspects, this research bridges existing gaps and offers practical guidance for companies seeking to leverage GenAI to enhance their competitive edge. The findings contribute to the broader discourse on GenAI integration, supporting the strategic and operational scalability of GenAI within various industries.3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Unsupervised Model Based on Knowledge Graph and Concepts for Sentiment Analysis(Institute of Electrical and Electronics Engineers Inc., 2025)Sentiment analysis encompasses various fields such as psychology, marketing, and education, with social media serving as a key platform for gauging public opinion. Recently, graph-based methods have proven to be very useful in representing structured data. This study presents an unsupervised, graph knowledge approach to sentiment analysis that vectorizes nodes representing words and their conceptual connections. Using VADER (Valence Aware Dictionary and sentiment Reasoner) alongside conceptual words such as WordNet and ConceptNet, the method builds a graph of words based on sentiment polarity, capturing both co-occurrence and conceptual relationships. Additionally, a novel Polarity-biased Random Walk algorithm creates polarity-sensitive graph walks, which are vectorized using the Skip-Gram technique. The findings indicate that increasing walk length and the number of node walks, with a bias of 0.95 and employing ConceptNet or WordNet, enhances sentiment classification compared to models like Node2Vec, GraphSAGE, Graph Attention, and Graph Convolutional Networks. Lastly, embeddings generated from the IMDB dataset demonstrate superior accuracy in domain-specific tasks when compared to models such as Word2Vec, FastText, GloVe, and BERT.1