2025
Authors
Ceccaroni, L; Pearlman, J; Angel, D; Dreo, J; Edelist, D; Freitas, C; Ganchev, T; Ipektsidis, C; Kruniawan, F; Laudy, C; Markova, V; Mlandu, DN; Paredes, H; Oliveira, MA; Simpson, P; Venus, V; Wahyudi, F; Parkinson, S;
Publication
OCEANS 2025 BREST
Abstract
Integrating citizen science with digital twin technology represents a significant development in oceanographic research and marine management. This paper examines how the Iliad project has successfully developed a comprehensive suite of digital twins of the ocean (DTOs) that leverage citizen science contributions to enhance data coverage, improve modelling accuracy, and foster public engagement with marine ecosystems. Through innovative technological solutions, including semantic interoperability frameworks, mobile applications, knowledge graphs, and gamification approaches, the project demonstrates the reciprocal benefits between citizen scientists, scientific research and digital twin ecosystems. The developments presented in this work illustrate how engaging the public in scientific research not only broadens the data foundation for digital twins but also creates pathways for citizens to gain valuable insights from these sophisticated digital representations of ocean environments.
2025
Authors
Cambra Fierro, J; Patrício, L; Polo Redondo, Y; Trifu, A;
Publication
JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
Abstract
Purpose - Customer-provider relationships unfold through multiple touchpoints across different channels. However, some touchpoints are more important than others. Such important touchpoints are viewed as moments of truth (MOTs). This study examines the impact of a series of touchpoints on an MOT, and the role MOTs play in determining future profitability and other behavioral outcomes (e.g. customer retention and customer cross-buy) in a business-to-business (B2B) context. Design/methodology/approach - Building upon social exchange theory, a conceptual model is proposed and tested that examines the impact of human, digital, and physical touchpoints and past MOTs on customer evaluation of a current MOT and on future customer outcomes. This research employs a longitudinal methodology based on a unique panel dataset of 2,970 B2B customers. Findings - Study results show that all touchpoints significantly contribute to MOTs, while human and physical touchpoints maintain their primacy during MOTs. The impact of MOTs on future customer outcomes is also demonstrated. Practical implications - This study highlights the need for prioritizing human and physical touchpoints in managing MOTs, and for carefully managing MOTs across time. Originality/value - Given its B2B outlook and longitudinal approach, this research contributes to the multichannel and interactive marketing literature by determining relevant touchpoints for B2B customers.
2025
Authors
Chandramohan, MS; da Silva, IM; Ribeiro, RP; Jorge, A; da Silva, JE;
Publication
ENVIRONMENTS
Abstract
This study investigates spatial distribution and chemical elemental composition screening in soils in Rome (Italy) using X-ray fluorescence analysis. Fifty-nine soil samples were collected from various locations within the urban areas of the Rome municipality and were analyzed for 19 elements. Multivariate statistical techniques, including nonlinear mapping, principal component analysis, and hierarchical cluster analysis, were employed to identify clusters of similar soil samples and their spatial distribution and to try to obtain environmental quality information. The soil sample clusters result from natural geological processes and anthropogenic activities on soil contamination patterns. Spatial clustering using the k-means algorithm further identified six distinct clusters, each with specific geographical distributions and elemental characteristics. Hence, the findings underscore the importance of targeted soil assessments to ensure the sustainable use of land resources in urban areas.
2025
Authors
Amarelo, A; Amarelo, B; Ferreira, MC; Fernandes, CS;
Publication
EUROPEAN JOURNAL OF ONCOLOGY NURSING
Abstract
Purpose: To aggregate, interpret, and synthesize findings from qualitative studies on patients' experiences with chemotherapy-induced peripheral neuropathy (CIPN). Methods: A qualitative metasynthesis was conducted following the thematic synthesis approach of Thomas & Harden. A systematic literature search was performed in MEDLINE, CINAHL, Psychology and Behavioral Sciences Collection, and Scopus, including studies published up to December 2024. Two researchers independently conducted the screening and data extraction. They also independently evaluated the quality of the included studies. The data from these studies were then thematically analyzed and synthesized using Dorothea Orem's model. Results: Eighteen studies were included. Four main categories were identified: (1) Physical and Functional Impact of CIPN, (2) Emotional and Psychological Impact, (3) Coping Strategies and Self-management, and (4) Support and Barriers to Health. The findings revealed distinct self-care deficits related to functional limitations, emotional distress, and coping challenges. Utilizing Orem's Nursing Theory of Self-Care Deficit, these deficits were mapped onto different levels of nursing intervention, ranging from compensatory support to educational and self-management strategies, emphasizing an action-oriented approach in patient care. Conclusions: This metasynthesis highlights the complex and multidimensional effects of peripheral neuropathy on the lives of cancer patients. Applying Orem's model underscores the critical role of nurses in addressing healthcare system gaps, functional impairments, and long-term adaptation challenges to enhance supportive care for individuals suffering from CIPN.
2025
Authors
Metheniti, V; Parasyris, A; Fazzini, N; Outmani, S; Correia, M; Goddard, J; Alexandrakis, G; Kozyrakis, GV; Vettorello, L; Keeble, S; Oliveira, MA; Quarta, ML; Kampanis, N;
Publication
OCEANS 2025 BREST
Abstract
Developed within the Iliad Digital Twin of the Ocean (DTO) project, Coastal Crete provides advanced marine forecasting for oil spill detection and response. The system integrates satellite data, in-situ observations, and machine learning to predict oil spill trajectories and minimize environmental impacts. Using a multi-model approach, it combines WRF-DA, NEMO, and WAVEWATCH III models for high-resolution forecasts. Making use of Sentinel-1 SAR imagery, a deep learning approach was developed for near-real-time oil spill detection. The methodology is based on a U-net Neural Network, which is compared with the statistical methodology based on pythons' SNAPpy library. The operational forecasting system employs MEDSLIK-II for oil spill transport modeling and visualization via the GeoMachine platform, ensuring rapid decision-making for marine safety and environmental protection.
2025
Authors
Kasapakis, V; Morgado, L;
Publication
CoRR
Abstract
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