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Effects of layered heterogeneity on mixed physical barrier performance to prevent seawater intrusion in coastal aquifers
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No data was used for the research described in the article.Mixed physical barriers (MPB), which combine a cutoff wall and subsurface dam, were applied in heterogeneous coastal aquifer settings, and their ability to prevent seawater intrusion (SWI) was tested. The performance of MPB was examined in a typical stratified aquifer using laboratory experiments and numerical modelling. The performance of the MPB was compared to that of a single cut-off wall by measuring the percentage of reduction of the intrusion length. SEAWAT was used for validation purposes and to further evaluate the effectiveness of MPB in five additional realistic heterogeneous configurations. Experimental results show that the MPB effectively reduced the saltwater wedge length by up to 71%. The MPB outperformed the single cut-off wall by up to 55 %. Also, numerical results show that the MPB proved to be effective, achieving a SWI length reduction of up to 69% in the scenario regardless of the aquifer layering structures. Comparable performance was observed when the high K layer was confined between two low K layers or when the aquifer presented a monotonically increasing/decreasing K pattern. The findings of this study provide insights into the applicability of the MPB in realistic heterogeneous coastal aquifers.The authors wish to thank Queen’s University Belfast for providing the facilities for the second author to conduct the experiments while he was associated with them
Stacked Hybridization to Enhance the Performance of Artificial Neural Networks (ANN) for Prediction of Water Quality Index in the Bagh River Basin, India
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The data pertaining to this study have not been deposited in a publicly accessible repository, given that all relevant data are thoroughly detailed in the article or appropriately cited in the manuscript.Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3−), Magnesium (Mg2+), Sulphate (SO42−), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.No funding was received for conducting this study
Implementing and evaluating resources to support good maternity care for parents with learning disabilities: A qualitative feasibility study in England
Get PDFSupplementary materials are available online at: https://www.sciencedirect.com/science/article/pii/S0266613824000858?via%3Dihub#sec0035 .Problem:
Parents with learning disabilities are often disadvantaged and their needs not well understood in maternity services.
Background:
Despite a global vision to improve maternity care, current evidence confirms poor pre- and post-natal care for parents with learning disabilities and their families. Midwives have expressed a need for support in the delivery of good care to this population of parents.
Aim:
To test the feasibility of implementing and evaluating two evidence-based and values-based resources – the Together Toolkit and Maternity Passport - to support good maternity care for people with learning disabilities.
Methods:
A qualitative feasibility study employing semi-structured interviews with 17 midwives and 6 parents who had used the resources in practice in four NHS Trusts in the south of England.
Findings:
Midwives and parents described how the resources positively impacted maternity care by enabling midwives, connecting networks and empowering parents. Factors affecting effective implementation of the resources were reported at an individual and setting level.
Discussion:
Staff training to raise awareness and confidence in supporting parents with learning disabilities, and improved systems for recording parent's individual needs are required to enable the delivery of personalised care.
Conclusion:
Reasonable adjustments need to be prioritised to facilitate implementation of resources to support personalised maternity care and to address inequity for parents with learning disabilities. Aspirations for equity suggested commitment from midwives to challenge and overcome barriers to implementation. Recommendations were made to improve the resources and their implementation. These resources are free and accessible for use [www.surrey.ac.uk/togetherproject].NHSE Workforce Training and Education Directorate South East Region, and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Kent, Surrey, Sussex
BPS DSEP position statement: Psychological skills training for performance enhancement, long-term development, and well-being in youth sport
Get PDFYoung athletes have become an increasingly important client group for sport psychology practitioners and a population whose physical, cognitive, emotional, and social development should be carefully considered by a practitioner when delivering their services (Visek et al., 2009). The aim of this British Psychological Society (BPS) Division of Sport and Exercise Psychology (DSEP) position statement is to summarise existing knowledge about psychological skills training (PST) interventions and discuss optimal service provision of PST in youth sport. In the first section of this position statement, we provide a brief overview of the literature exploring PST during childhood (5–11 years), early adolescence (12–15 years), and mid-to-late adolescence (16–18 years). Within each sub-section, key developmental considerations (i.e. physical, cognitive, emotional, and social) are provided followed by short summaries of research on basic single strategy (i.e. goal setting, imagery, relaxation, and self-talk) and alternative strategy interventions (e.g. mindfulness, music, perceptual training, and self-modelling) with young athletes. In the second section, optimal service provision of PST is discussed by drawing upon practitioners’ experiences of working with young athletes, concluding with 10 recommendations for youth sport organisations, training and accrediting bodies, researchers, and practitioners.British Psychological Society, Research Working Groups Scheme: Psychological Skill Use for Performance Enhancement and Refinement in YOUth (PSUPER YOU)
Towards decarbonisation of sugar refineries by calcium looping: Process integration, energy optimisation and technoeconomic assessment
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https://doi.org/10.17633/rd.brunel.25864513.v1.Supplementary material is available online at: https://www.sciencedirect.com/science/article/pii/S0196890424005387?via%3Dihub#s0075:~:text=Appendix%20A.-,Supplementary%20material,-Data%20availability .The sugar refinery process as a food industry is at the pinnacle of the energy hierarchy. Depending on the source of carbon in this industry that could be either from biomass or fossil fuel hydrocarbons, this sector has the potential to become a carbon–neutral or negative industry depending on the type of fuel for power generation. This work will deliver a new proof of concept model for simultaneous sugar refining and carbon capture, thereby transforming sugar into the carbon negative commodity which relies on the utilisation of CO2 and fresh lime in the sugar refineries carbonisation tanks and in return byproduction of calcium carbonate that can be converted in the calciner of a calcium looping process. This study begins with the design and development of the calcium looping (CaL) process for the integration to the sugar refineries using various coupled and decoupled scenarios for the boiler units based on the type of the fuel, continues with an energy optimisation and techno-economic assessment of the proposed process, and finally culminates with the parametric study on the thermodynamic and economic performance of the sugar refinery retrofitted with the calcium looping. The process simulations revealed that the integrated CaL-sugar refinery can support the electricity exporter by installation of an onsite steam cycle which is able to generate electricity from surplus carbonation heat. The cost of CO2 avoided for integration of CaL to the reference sugar refinery for natural gas boilers and calciner will be 62 £/tCO2 which drops to 25 £/tCO2 if the carbon tax is considered in the analysis and negative carbon emissions are credited. This is equivalent to 61% costs reductions associated refining sugar combined together with carbon capture and storage (CCS)
A Comprehensive Assessment of the Economic Performance of an Innovative Solar Thermal System: A Case Study
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Data used in this study are available from the corresponding author upon request.An economic assessment of an innovative solar thermal system called Application to Solar Thermal Energy to Processes (ASTEP) was conducted. It considered its three main subsystems: a novel rotary Fresnel SunDial, Thermal Energy Storage (TES) and Control System. Current Fresnel collectors are unable to provide thermal energy above 150 °C in high-latitude locations. Therefore, the key contribution of this study is the assessment of the economic performance of the ASTEP system used to provide high-temperature process heat up to 400 °C for industries located at low and high latitudes. The ASTEP system is installed at two end-users: Mandrekas (MAND), a dairy factory located in Greece at a latitude of 37.93 N and ArcelorMittal (AMTP), a manufacturer of steel tubes located in Romania at a latitude of 47.1 N. The life cycle costs (LCC), levelised cost of energy (LCOE), energy cost savings, EU carbon cost savings and benefit–cost ratio (BCR) of the ASTEP system were assessed. The results showed that AMTP’s ASTEP system had higher LCC and LCOE than MAND. This can be attributed to the use of two TES tanks and a double-axis solar tracking system for AMTP’s ASTEP system due to its high latitude location, compared to a single TES tank and single-axis solar tracking system used for MAND at low latitude. The total financial savings of the ASTEP system were EUR 249,248 for MAND and EUR 262,931 for AMTP over a period of 30 years. This study demonstrates that the ASTEP system offers financial benefits through its energy and EU carbon cost savings for industries at different latitudes while enhancing their environmental sustainability.The authors declare that this project is funded by the EU Horizon 2020 research and innovation programme, Application of Solar Energy in Industrial Processes (ASTEP), grant agreement number 884411
Segmenting Medical Images: From UNet to Res-UNet and nnUNet
Get PDFThis study provides a comparative analysis of deep learning models—UNet, Res-UNet, Attention Res-UNet, and nnUNet—evaluating their performance in brain tumour, polyp, and multi-class heart segmentation tasks. The analysis focuses on precision, accuracy, recall, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU) to assess their clinical applicability. In brain tumour segmentation, Res-UNet and nnUNet significantly outperformed UNet, with Res-UNet leading in DSC and IoU scores, indicating superior accuracy in tumour delineation. Meanwhile, nnUNet excelled in recall and accuracy, which are crucial for reliable tumour detection in clinical diagnosis and planning. In polyp detection, nnUNet was the most effective, achieving the highest metrics across all categories and proving itself as a reliable diagnostic tool in endoscopy. In the complex task of heart segmentation, Res-UNet and Attention Res-UNet were outstanding in delineating the left ventricle, with Res-UNet also leading in right ventricle segmentation. nnUNet was unmatched in myocardium segmentation, achieving top scores in precision, recall, DSC, and IoU. The conclusion notes that although ResUNet occasionally outperforms nnUNet in specific metrics, the differences are quite small. Moreover, nnUNet consistently shows superior overall performance across the experiments. Particularly noted for its high recall and accuracy, which are crucial in clinical settings to minimize misdiagnosis and ensure timely treatment, nnUNet’s robust performance in crucial metrics across all tested categories establishes it as the most effective model for these varied and complex segmentation tasks
Social media-facilitated trafficking of children and young people: a summary report
No full textProject partner: Unseen.Social media platforms can be used for exploitative activities. This poses significant challenges to the safety and wellbeing of children and young people.
Despite increased focus on the use of social media among young people, its role in facilitating exploitation has been largely absent from current research. This report presents key findings from exploratory research that aimed to better understand the use of social media in exploiting children and young people, as well as stakeholders’ preparedness to respond to it.
The report draws on evidence from practitioners across England and Wales. It provides an overview of the problem and identifies specific areas where further research is needed to inform policy and practice.The project was funded through the ESRC Vulnerability & Policing Futures Research Centre’s Early Career Researcher Development Fund. Grant reference number: ES/W002248/1
A Fuzzy Neural Network Approach to Adaptive Robust Nonsingular Sliding Mode Control for Predefined-Time Tracking of a Quadrotor
Get PDFIn this article, a novel adaptive robust predefined-time nonsingular sliding mode control (ARPTNSMC) scheme is investigated, which aims to achieve fast and accurate tracking control of a quadrotor subjected to external disturbance. Inspiration is drawn from a fuzzy neural network that is constructed by fuzzy logic and zeroing neural network (ZNN). Distinct from most sliding mode control approaches, two nonsingular sliding mode surfaces are formulated by employing general ZNN approaches and differentiable predefined-time activation functions. Furthermore, for the compensation of external disturbance, a dynamic adaptive parameter and a fuzzy adaptive parameter are designed in the attitude control law. The fuzzy adaptive parameter, generated by the Takagi–Sugeno fuzzy logic system, is incorporated to enhance the robustness while reducing the chattering phenomena resulting from the discontinuous sign function. Theoretical proofs are provided to demonstrate the predefined-time convergence and robustness of the closed-loop system. Finally, two trajectory tracking examples are offered to validate the convergence, robustness, and low-chattering characteristics of the closed-loop system under the developed ARPTNSMC scheme.10.13039/501100004761-Natural Science Foundation of Hainan Province (Grant Number: 2021JJ20005, 2022RC1103 and 2024JJ6320);
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61866013 and 62406109);
Postgraduate Scientific Research Innovation Project of Hunan Province (Grant Number: CX20230513);
Hunan Provincial Student Innovation Training Programme (Grant Number: S202410542066)
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