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Gaussian Process Nonlinear Model Predictive Control for Online Partially Observable Systems: An Application to Froth Flotation
Get PDFSupporting Information is available online at: https://pubs.acs.org/doi/10.1021/acs.iecr.5c00660#_i71 .This paper presents a nonlinear model predictive control (NMPC) framework employing Gaussian processes (GPs) for application in froth flotation processes under online partial observability. Froth flotation, a critical process in mineral processing, involves complex, nonlinear dynamics and unmeasured variables, making traditional control methods challenging to implement. In this work, we build a data-driven control strategy by training GP-based state-space models on data generated from a physics-based model. These GP models are then integrated into an NMPC architecture where only a subset of the process states is observable online. The GP-MPC controller accounts for uncertainty and disturbances by predicting both the mean and variance of system dynamics, enabling robust optimization over a finite horizon. Results demonstrate a 20% reduction in the concentration of valuable minerals in the tailings compared to traditional control methods, while achieving consistent operation throughout the process. The framework effectively handles noise and disturbances, with the partially observable GP-MPC achieving consistent set point tracking within a 5% error margin, and reduces deviations from target values by approximately 15%. The framework also meets real-time constraints, making it well suited for complex, data-driven process control applications
Energy Management Solution for Islanding Based on a Dynamic Neuro-Fuzzy-Optical Microscope Algorithm
No full textEnsuring continuity of service is a primary objective in power systems. In grid-connected microgrids (MGs), islanding poses a significant threat to this continuity. Conventional approaches mitigate islanding by disconnecting the MG immediately after separation from the main grid to prevent overload and ensure safety, but this results in service interruption. This study proposes a dynamic islanding management strategy that maintains uninterrupted service using an optimal dynamic neuro-fuzzy–optical microscope algorithm (OMA). The method integrates a convolutional neural network (CNN), fuzzy logic (FL), and the novel OMA optimizer in a two-stage framework. In the first stage, the CNN detects islanding based on active current and voltage measurements at the point of common coupling (PCC) and their dominant harmonic components, obtained from a hybrid MG model. This model is comprising solar panels, wind turbines, a biomass generator, and a storage system. Signal and image processing techniques prepare the measurements for CNN implementation. Upon islanding detection, the second stage is activated, where FL predicts the penalty factor and OMA optimally manages economic power sharing between the grid and the MG. This integration enables safe load coverage without damaging MG components. Performance benchmarking against Quadratic Interpolation Optimization (QIO) and Hunger Games Optimizer (HGO) demonstrates that OMA achieves higher accuracy, faster convergence, and lower execution time. Validation across five scenarios under normal, islanding, and risky operating conditions confirms the method’s effectiveness, reliability, and economic benefits, achieving a 223.7% revenue improvement over the baseline with the shortest execution time. The proposed approach offers a robust and intelligent solution to the islanding problem, ensuring continuous and cost-effective microgrid operation
ZAKON I PRAKSA TRGOVINSKE ARBITRAŽE I ARBITRAŽE NA OSNOVU SPORAZUMA U POLJSKOJ: NAJNOVIJI RAZVOJ DOGAĐAJA I AKTUELNI TRENDOVI
Get PDFThis paper analyses the recent developments and current trends in the landscape of Polish arbitration. It commences with a brief overview of the legal framework governing arbitration in Poland, followed by a review of the practice of the Polish state courts in post-arbitral cases. It then describes the most relevant Polish arbitral institutions. Next, it proceeds to examine the position of treaty-based arbitration in the Polish context. Each of these sections discusses the challenges and perspectives faced by arbitration in Poland.Apstrakt:
U ovom članku analiziraćemo nedavna dešavanja i aktuelne trendove u oblasti arbitraže u Poljskoj. Prvo ćemo dati kratak pregled pravnog okvira za arbitražu u Poljskoj, a zatim i pregled prakse poljskih državnih sudova u “post-arbitražnim” predmetima. Nakon toga ćemo posvetiti pažnju najrelevantnijim poljskim arbitražnim institucijama. Na kraju ćemo razmotriti položaj arbitraže na osnovu sporazuma. U svakom od delova biće reči i o izazovima i perspektivama sa kojima se suočava arbitraža u Poljskoj
Development of an Intervention Population Ontology for specifying the characteristics of intervention participants
Get PDFPlain language summary:
Intervening to change behaviour is key to addressing many of the most serious challenges facing the world today. However, the effectiveness of interventions varies according to the characteristics of the people taking part. We need to build our knowledge of what types of interventions work best for people with particular characteristics. This study developed a unifying framework, called an “ontology”, for describing the characteristics of intervention participants.
We developed the Intervention Population Ontology using a standardised method. This included the following steps: identifying key entities to include by reviewing intervention reports and existing classification systems; coding examples of population characteristics in studies; and asking behavioural science and public health experts for feedback on the ontology. The resulting Intervention Population Ontology has 206 entries representing different human characteristics and a further 638 classes representing how these characteristics can be aggregated to describe groups of people (e.g. mean age, percent female).
The importance of the Intervention Population Ontology lies in its ability to support users in precisely describing, comparing and integrating evidence about the human participants in different studies. Going forward, users of the ontology will be able to contribute to it by providing feedback and suggestions for improvement.First Version Published: 05 Mar 2025, 10:122 (https://doi.org/10.12688/wellcomeopenres.22788.1)Data availability statement:
Underlying data:
Open Science Framework: Human Behaviour-Change Project.
https://doi.org/10.17605/OSF.IO/QRGC4 (West et al., 2020).
The BCIO is available from: https://github.com/HumanBehaviourChangeProject/ontologies
Archived version of the Intervention Population Ontology as at time of publication: https://github.com/HumanBehaviourChangeProject/ontologies/tree/master/Population
Zenodo: HumanBehaviourChangeProject/ontologies: https://doi.org/10.5281/zenodo.14882463 (Schenk et al., 2025)
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Extended data
Open Science Framework: Human Behaviour-Change Project. https://doi.org/10.17605/OSF.IO/QRGC4 (West et al., 2020).
This project contains the following extended data:
• Papers used across steps of development of the Intervention Population Ontology (https://osf.io/6xcwy)
• Version 0.1 Preliminary prototype version of Intervention Population Ontology (https://osf.io/jhymg)
• Version 0.2 Version of the Intervention Population Ontology after initial annotations (https://osf.io/m6udx)
• Expert feedback survey; Full survey provided to behavioural science and public health experts in review of the Intervention Population Ontology (https://osf.io/64mx9)
• Expert feedback on Intervention Population Ontology: Feedback received from behavioural science and public health experts together with the ontology development team’s responses (https://osf.io/6quv2)
• Version 0.3 Version of the Intervention Population Ontology after expert stakeholder feedback (https://osf.io/8uw52)
• Inter-rater reliability testing results – annotations by researchers from the ontology development team (https://osf.io/ywpgt)
• Inter-rater reliability testing results – two behaviour change experts unfamiliar with the ontology (https://osf.io/9p5zc)
• Annotation guidance manual for using the Intervention Population Ontology (https://osf.io/u9wb4)
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).Software availability:
Source code used to calculate alpha for inter-rater reliability available from:
https://github.com/HumanBehaviourChangeProject/Automation-InterRater-Reliability
Archived code at time of publication: https://doi.org/10.5281/zenodo.3833816 (Finnerty & Moore, 2020)
License: GNU General Public License v3.0Background:
The uptake, effectiveness and generalisability of interventions are influenced by the features of the populations targeted. However, populations exposed to interventions are not consistently specified in published reports.
Purpose:
To create an Intervention Population Ontology providing a clear, usable and reliable classification system to specify characteristics of populations exposed to interventions.
Methods:
The Intervention Population Ontology was developed in seven main stages 1) Defining the ontology’s scope, (2) identifying key entities by reviewing existing classification systems (top-down) and 100 intervention reports (bottom-up), 3) Refining the preliminary ontology by annotating ~150 intervention reports, 4) Stakeholder review by 29 behavioural science and public health experts, 5) Assessing inter-rater reliability of using the ontology by two coders familiar with the ontology and two coders unfamiliar with it, 6) Specifying ontological relationships between entities in the ontology and 7) making the Intervention Population Ontology machine-readable using Web Ontology Language (OWL) and publishing online.
Results:
The Intervention Population Ontology features 218 entities representing attributes of human individuals across 12 key groupings: personal attributes, geographic location, person, quality, mental capability, role, expertise, objects possessed, behaviour, personal vulnerability and personal history. It has a further 666 classes relating to how individual-level attributes are aggregated to describe groups of people. Inter-rater reliability was α=0.79 for coders familiar with the ontology and 0.85 for coders unfamiliar with the ontology.
Conclusions:
The Intervention Population Ontology can be applied to specify precisely information from diverse sources, annotate population characteristics in existing intervention evaluation reports and guide future reporting.This work was supported by Wellcome [201524]
Making sense of youth-led social action with, and for, young people from refugee backgrounds
Get PDFWithin Western resettlement countries, leisure has been positioned as a tool for achieving top-down policy goals. Yet, minimal attention has been directed towards understanding how young-forced migrants make sense of and experience leisure within the daily physically, psychologically and socially repressive regulations and processes of the UK asylum system. This novel paper examines how young people from refugee backgrounds engage in and make sense of the leisure domain, social action, whereby individuals and communities seek to generate change on issues that matter to them. The paper draws on a 3-year-long participatory action research (PAR) project in London, UK, seeking to co-develop a leisure-based, youth-led social action programme with, and for, young people from refugee backgrounds. Participant observation was used alongside photo voice methods to explore the young peoples’ entangled motives and meanings of social action. Our findings critically examine the complex ways that young people understood the social action programme in relation to (i) relational and emotional dynamics, and (ii) identity, culture, and religion. These findings offer a new way to thinking about how young forced migrants experience and negotiate leisure amid the migration-welfare nexus.This project was partially funded by the Leisure Studies Journal Maureen Harrington Fund
A digital twin framework for real-time healthcare monitoring: leveraging AI and secure systems for enhanced patient outcomes
Get PDFData availability:
Data is available on request from the authors.Acknowledgements: The authors would like to acknowledge the support of Brunel University London for providing the necessary resources and facilities for this research.Digital Twin (DT) technology in healthcare is relatively new and faces several challenges, e.g., real-time data processing, secure system integration, and robust cybersecurity. Despite the growing demand for real-time monitoring frameworks, further improvements remain possible. In this study, an architecture has been introduced that utilises cloud computing to create a DT ecosystem. A group of 20 participants has been monitored continuously using high-speed technology to track key physiological parameters, i.e., diabetes risk factors, heart rate (HR), oxygen saturation (SpO2) levels, and body temperature (BT). To strengthen the study and enhance diversity, the dataset was supplemented with 1177 anonymized medical records from the publicly available MIMIC-III Public Health Dataset. The DT model functions as a tool, storing both real-time sensor data and historical records, to effectively identify health risks and anomalies. An MLP model was combined with XGBoost, resulting in a 25% reduction in training time and a 33% reduction in testing time. The model demonstrated reliability with an accuracy of 98.9% and achieved real-time accuracy of 95.4%, alongside an F1 score of 0.984. Meticulous attention has been paid to cybersecurity measures, ensuring system integrity through end-to-end encryption and compliance with health data regulations. The incorporation of DT and AI within the healthcare sector is seen as having the potential to overcome existing limitations in monitoring systems, while workloads are relieved and data-driven diagnostics and decision-making processes are improved, e.g., through enhanced real-time patient monitoring and predictive analysis.This research was supported by Brunel University London
Engineering biology applications for environmental solutions: potential and challenges
Get PDFSupplementary information is available online at: https://www.nature.com/articles/s41467-025-58492-0#Sec9 .Engineering biology applies synthetic biology to address global environmental challenges like bioremediation, biosequestration, pollutant monitoring, and resource recovery. This perspective outlines innovations in engineering biology, its integration with other technologies (e.g., nanotechnology, IoT, AI), and commercial ventures leveraging these advancements. We also discuss commercialisation and scaling challenges, biosafety and biosecurity considerations including biocontainment strategies, social and political dimensions, and governance issues that must be addressed for successful real-world implementation. Finally, we highlight future perspectives and propose strategies to overcome existing hurdles, aiming to accelerate the adoption of engineering biology for environmental solutions.All authors acknowledge support from the UK Research and Innovation (UKRI) Biological Sciences Research Council (BBSRC) grant BB/Y008332/1. D.J.L-S acknowledges support from UKRI BBSRC grant BB/S020365. F.C. and T.G. acknowledges support from UKRI BBSRC grant BB/S009795/1. ZY thanks The Leverhulme Trust Research Leadership Awards RL-2022-041. N.K. acknowledges support from UKRI BBSRC grant BB/Y007638/1, the Department for Science, Innovation and Technology (DSIT) and the Royal Academy of Engineering for his Chair in Emerging Technologies award
Dynamics of user engagement: AI mastery goal and the paradox mindset in AI–employee collaboration
Get PDFGiven the scarcity of previous studies on employee–AI collaboration and its impact on employee behavior and user engagement, we investigated its potential to drive user engagement using a mixed-method approach. Grounded in qualitative findings from 27 participants in a healthcare setting, we propose a robust model that emphasizes the impact of AI–employee collaboration on AI mastery goal, user engagement, and a paradox mindset, as well as the moderating role of AI empathy and technological frames. Using a quantitative method, we collected data from 452 participants in a healthcare setting across two studies. Our findings showed that AI–employee collaboration can drive AI mastery goal and a paradox mindset. We also found empirical evidence that both AI mastery goal and the paradox mindset can mediate the relationship between employee–AI collaboration and user engagement. Moreover, our findings revealed interesting moderating results across two studies. In Study 1, significant effects were found for both employee–AI collaboration and AI mastery goal at low AI empathy, but not at high levels. In Study 2, while the interaction between employee–AI collaboration and AI empathy was not significant, the influence of AI mastery goal became significant at high empathy levels, and the paradox mindset showed a significant effect only at high levels of AI empathy. These findings provide managers with valuable insights into the essential operations dynamic of employee–AI collaboration, underscoring its important role in enhancing user engagement
Aging Consumer Engagement with the Mobile Food Planner Apps in Malaysia: Investigating Gender Differences
Get PDFThis study investigates the factors influencing older consumers’ word of mouth and intention to use meal planner applications, given that 57% of Malaysians aged 60 and above are active Internet users. The current study utilized an extended technology acceptance model and the stimulus organism response framework to examine the cognitive-affective dichotomy of usage influence. The respondents were initially selected through purposive sampling and further screened using filter questions to ensure alignment with the study objectives, resulting in data collected from 392 elderly individuals across various demographic regions of Malaysia. The analysis was conducted using the Statistical Package for Social Sciences (SPSS) and SmartPLS. The results indicated that cognitive factors (subjective norm, facilitating conditions, and app characteristics) positively impacted the affective components (perceived usefulness and perceived ease of use), which, in turn, enhanced consumers’ attitudes toward using these applications. Furthermore, openness to change and gender were observed to moderate the relationship between affective and conative responses (word of mouth and behavioral intention). The study provides new insights into the aging population on food-related mobile applications to governments, food service-based organizations, restaurants, and app designers. Thus, focus can be on improving app characteristics and facilitating conditions to enhance perceived usefulness and ease of use while also considering how gender and openness to change may influence consumers’ attitudes and behaviors.The authors received no financial support for the research, authorship, and/or publication of this article
Curriculum in alternative provision: Conversations with senior leaders
Get PDFData Availability Statement:
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.Over the last four to five years, I've increasingly been reflecting on the role of what were formerly referred to as offsite or pupil referral units. These are now subsumed within a more generic grouping known as alternative provision. My interest has been triggered by the recent publication of ‘Alternative provision in local areas in England: a thematic review’, which, among other things, ‘sets out good practice and highlights particular areas requiring further attention’. This was sufficient stimulus for me to continue my conversations with a small group of school leaders, working in both specialist and mainstream settings, regarding their views on what might best represent effective provision for learners at risk of disengaging from formal education or who have already been excluded from the system
Data sources:
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