All Outputs (896)

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications (2022)
Journal Article
Pearcy, N., Garavaglia, M., Millat, T., Gilbert, J. P., Song, Y., Hartman, H., …Minton, N. P. (2022). A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications. PLoS Computational Biology, 18(5), Article e1010106. https://doi.org/10.1371/journal.pcbi.1010106

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly prom... Read More about A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications.

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets (2022)
Journal Article
Calderon-Ramirez, S., Oala, L., Torrentes-Barrena, J., Yang, S., Elizondo, D., Moemeni, A., …Lopez-Rubio, E. (2023). Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets. IEEE Transactions on Artificial Intelligence, 4(2), 282-291. https://doi.org/10.1109/tai.2022.3168804

Semi-supervised deep learning (SSDL) is a popular strategy to leverage unlabelled data for machine learning when labelled data is not readily available. In real-world scenarios, different unlabelled data sources are usually available, with varying de... Read More about Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets.

Demonstrating Interaction: The Case of Assistive Technology (2022)
Journal Article
Reyes-Cruz, G., Fischer, J. E., & Reeves, S. (2022). Demonstrating Interaction: The Case of Assistive Technology. ACM Transactions on Computer-Human Interaction, 29(5), 1-37. https://doi.org/10.1145/3514236

Technology "demos"have become a staple in technology design practice, especially for showcasing prototypes or systems. However, demonstrations are also commonplace and multifaceted phenomena in everyday life, and thus have found their way into empiri... Read More about Demonstrating Interaction: The Case of Assistive Technology.

RAT-RS: a reporting standard for improving the documentation of data use in agent-based modelling (2022)
Journal Article
Achter, S., Borit, M., Chattoe-Brown, E., & Siebers, P. (2022). RAT-RS: a reporting standard for improving the documentation of data use in agent-based modelling. International Journal of Social Research Methodology, 25(4), 517-540. https://doi.org/10.1080/13645579.2022.2049511

This article describes and justifies a reporting standard to improve data use documentation in Agent-Based Modelling. Following the development of reporting standards for models themselves, empirical modelling has now developed to the point where the... Read More about RAT-RS: a reporting standard for improving the documentation of data use in agent-based modelling.

Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications (2022)
Journal Article
Güleç, F., Pekaslan, D., Williams, O., & Lester, E. (2022). Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications. Fuel, 320, Article 123944. https://doi.org/10.1016/j.fuel.2022.123944

Higher heating value (HHV) is a key characteristic for the assessment and selection of biomass feedstocks as a fuel source. The HHV is usually measured using an adiabatic oxygen bomb calorimeter; however, this method can be time consuming and expensi... Read More about Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications.

Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat (2022)
Journal Article
GRIFFITHS, M., ATKINSON, J. A., Gardiner, L. J., SWARUP, R., POUND, M. P., WILSON, M. H., …WELLS, D. M. (2022). Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat. Journal of Integrative Agriculture, 21(4), 917-932. https://doi.org/10.1016/s2095-3119%2821%2963700-0

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat (Triticum aestivum L.... Read More about Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat.

Efficiency Focused Energy Management Strategy Based on Optimal Droop Gain Design for More Electric Aircraft (2022)
Journal Article
Mohamed, M. A., Yeoh, S. S., Atkin, J., Hussaini, H., & Bozhko, S. (2022). Efficiency Focused Energy Management Strategy Based on Optimal Droop Gain Design for More Electric Aircraft. IEEE Transactions on Transportation Electrification, https://doi.org/10.1109/tte.2022.3159731

Due to the substantial increase of the number of electrically-driven systems on-board More Electric Aircraft (MEA), the on-board Electric Power Systems (EPS) are becoming more and more complex. Therefore, there is a need to develop a control strategy... Read More about Efficiency Focused Energy Management Strategy Based on Optimal Droop Gain Design for More Electric Aircraft.

A European Agency for Artificial Intelligence: Protecting fundamental rights and ethical values (2022)
Journal Article
Stahl, B. C., Rodrigues, R., Santiago, N., & Macnish, K. (2022). A European Agency for Artificial Intelligence: Protecting fundamental rights and ethical values. Computer Law and Security Review, 45, Article 105661. https://doi.org/10.1016/j.clsr.2022.105661

Following years of intensive international debate of the ethical and human rights implications of artificial intelligence (AI)-related technologies, there are numerous proposals to legislate and regulate these technologies. One aspect of possible leg... Read More about A European Agency for Artificial Intelligence: Protecting fundamental rights and ethical values.

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica (2022)
Journal Article
Calderon-Ramirez, S., Murillo-Hernandez, D., Rojas-Salazar, K., Elizondo, D., Yang, S., Moemeni, A., & Molina-Cabello, M. (2022). A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica. Medical and Biological Engineering and Computing, 60(4), 1159-1175. https://doi.org/10.1007/s11517-021-02497-6

The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a... Read More about A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.

Data-inspired co-design for museum and gallery visitor experiences (2022)
Journal Article
Darzentas, D., Cameron, H., Wagner, H., Craigon, P., Bodiaj, E., Spence, J., …Benford, S. (2022). Data-inspired co-design for museum and gallery visitor experiences. AI EDAM, 36, Article e3. https://doi.org/10.1017/S0890060421000317

The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case t... Read More about Data-inspired co-design for museum and gallery visitor experiences.

Brain simulation as a cloud service: The Virtual Brain on EBRAINS (2022)
Journal Article
Schirner, M., Domide, L., Perdikis, D., Triebkorn, P., Stefanovski, L., Pai, R., …Ritter, P. (2022). Brain simulation as a cloud service: The Virtual Brain on EBRAINS. NeuroImage, 251, Article 118973. https://doi.org/10.1016/j.neuroimage.2022.118973

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance i... Read More about Brain simulation as a cloud service: The Virtual Brain on EBRAINS.

Complying with the GDPR when vulnerable people use smart devices (2022)
Journal Article
Piasecki, S., & Chen, J. (2022). Complying with the GDPR when vulnerable people use smart devices. International Data Privacy Law, 12(2), 113-131. https://doi.org/10.1093/idpl/ipac001

The number of smart home devices is increasing. They are used by vulnerable people regardless of whether they are designed specifically for them or for the general population (eg, smart door locks, smart alarms, or voice assistants). This article foc... Read More about Complying with the GDPR when vulnerable people use smart devices.

Examining the influential factors of consumer purchase intentions for blockchain traceable coffee using the theory of planned behaviour (2022)
Journal Article
Dionysis, S., Chesney, T., & McAuley, D. (2022). Examining the influential factors of consumer purchase intentions for blockchain traceable coffee using the theory of planned behaviour. British Food Journal, 124(12), 4304-4322. https://doi.org/10.1108/BFJ-05-2021-0541

Purpose: Given the increasing industry interest in blockchain technologies for supply chain management and product traceability, this paper aims to investigate consumer purchasing intentions for blockchain traceable coffee and their psychosocial ante... Read More about Examining the influential factors of consumer purchase intentions for blockchain traceable coffee using the theory of planned behaviour.

Improving simulated consumption context with virtual Reality: A focus on participant experience (2022)
Journal Article
Yang, Q., Nijman, M., Flintham, M., Tennent, P., Hidrio, C., & Ford, R. (2022). Improving simulated consumption context with virtual Reality: A focus on participant experience. Food Quality and Preference, 98, Article 104531. https://doi.org/10.1016/j.foodqual.2022.104531

Context can have a significant impact on liking, emotional response and product choice, and Virtual Reality (VR) is a promising tool to evoke realistic consumption contexts in a controlled testing environment. This study compared an innovative approa... Read More about Improving simulated consumption context with virtual Reality: A focus on participant experience.

Crossing with care: bogs, streams and assistive mobilities as family praxis in the countryside (2021)
Journal Article
Laurier, E., Dunkley, R., Smith, T. A., & Reeves, S. (2021). Crossing with care: bogs, streams and assistive mobilities as family praxis in the countryside. Gesprächsforschung, 22, 544-568

In this paper, we use ethnomethodology, membership categorisation analysis, and conversation analysis (EMCA) to investigate traversing obstacles in outdoor environments as reflexively constitutive of producing, resisting and adjusting family relation... Read More about Crossing with care: bogs, streams and assistive mobilities as family praxis in the countryside.

A self-adaptive multi-objective feature selection approach for classification problems (2021)
Journal Article
Xue, Y., Zhu, H., & Neri, F. (2022). A self-adaptive multi-objective feature selection approach for classification problems. Integrated Computer-Aided Engineering, 29(1), 3-21. https://doi.org/10.3233/ICA-210664

In classification tasks, feature selection (FS) can reduce the data dimensionality and may also improve classification accuracy, both of which are commonly treated as the two objectives in FS problems. Many meta-heuristic algorithms have been applied... Read More about A self-adaptive multi-objective feature selection approach for classification problems.

Constraint reformulations for set point optimization problems using fuzzy cognitive map models (2021)
Journal Article
Garzón Casado, A., Cano Marchal, P., Wagner, C., Gómez Ortega, J., & Gámez García, J. (2022). Constraint reformulations for set point optimization problems using fuzzy cognitive map models. Optimal Control Applications and Methods, 43(3), 711-721. https://doi.org/10.1002/oca.2846

The selection of optimal set points is an important problem in modern process control. Fuzzy cognitive maps (FCMs) allow to construct models of complex processes using expert knowledge, which is particularly useful in situations where measuring the v... Read More about Constraint reformulations for set point optimization problems using fuzzy cognitive map models.

Generalised Pattern Search with Restarting Fitness Landscape Analysis (2021)
Journal Article
Neri, F. (2022). Generalised Pattern Search with Restarting Fitness Landscape Analysis. SN Computer Science, 3(2), Article 110. https://doi.org/10.1007/s42979-021-00989-8

Fitness landscape analysis for optimisation is a technique that involves analysing black-box optimisation problems to extract pieces of information about the problem, which can beneficially inform the design of the optimiser. Thus, the design of the... Read More about Generalised Pattern Search with Restarting Fitness Landscape Analysis.

A fusion spatial attention approach for few-shot learning (2021)
Journal Article
Song, H., Deng, B., Pound, M., Özcan, E., & Triguero, I. (2022). A fusion spatial attention approach for few-shot learning. Information Fusion, 81, 187-202. https://doi.org/10.1016/j.inffus.2021.11.019

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few image... Read More about A fusion spatial attention approach for few-shot learning.

International data governance for neuroscience (2021)
Journal Article
Eke, D. O., Bernard, A., Bjaalie, J. G., Chavarriaga, R., Hanakawa, T., Hannan, A. J., …Pestilli, F. (2022). International data governance for neuroscience. Neuron, 110(4), 600-612. https://doi.org/10.1016/j.neuron.2021.11.017

As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively... Read More about International data governance for neuroscience.