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Coupled effects of wind shear and bed absorption on sediment transport in wetland
No full textWetlands play a vital role in sustaining ecosystems, supporting biodiversity, and controlling environmental degradation. Sedimentation in wetlands has emerged as a critical research area due to its relevance in flood management, water purification, and geophysical applications. Despite its importance, the combined effects of wind shear and bed absorption on sediment transport remain underexplored. This study investigates sediment removal efficiency in wetland flow influenced by wind shear, absorbent beds, and particle settling. The time-dependent convection-diffusion equation is solved using a hybrid approach, combining Aris's method of integral moments with a finite difference implicit scheme. Analytical expressions for two-dimensional spatial concentration distributions are derived via Gill's series expansion. Key parameters, including wind direction (W), relative wind strength ( Er), bed absorption coefficient ( β), vegetation parameter ( α), and settling velocity ( ω), are analyzed to determine their impact on dispersion characteristics like the dispersion coefficient, concentration distribution, skewness, and kurtosis. Results show that wind shear enhances sediment dispersion when aligned with the flow ( W=+1) but induces backflow and reduces dispersion within a certain wind strength range when opposing it ( W=−1). Bed absorption accelerates stabilization by trapping particles, limiting their dispersion capacity. Settling velocity further influences dispersion by reducing the suspension of the particles, with higher ω accelerating stabilization but suppressing long-term dispersion. Dense vegetation reduces wind-induced variations, improving flow stability by dampening momentum transfer. This study provides new insights into coupled wind-absorbent bed-settling velocity interactions, offering a framework for optimizing wetland design, pollutant removal, and environmental sustainability. This proposed model can be applied to optimize wetland restoration strategies, enhance sediment management in aquatic ecosystems, and improve pollutant filtration efficiency in natural and engineered water systems.The authors wish to express their sincere thanks to the Chief Editor, Associate Editor, and anonymous reviewers for their constructive comments and suggestions for the improvement of the present research. The first author, Saugata Dutta (NTA Ref. No. 211610066362), expresses sincere gratitude to the University Grants Commission (UGC), India, for providing financial support to undertake this work. The author, Nanda Poddar, gratefully acknowledges the financial support from the Irish Research Council (now Research Ireland) (Project No. GOIPD/2024/226) for this research work.peer-reviewe
Developing a methodology with a tool for calculating and reducing the upfront carbon emissions of buildings: Ireland as a case study
No full textUpfront carbon emissions of buildings encompass all greenhouse gas emissions linked to building materials extraction, manufacturing, transportation, and installation processes. Many countries face barriers in calculating and reducing the upfront carbon emissions of buildings during the design stage due to unreliable materials embodied carbon (EC) data, inconsistent methods, and tool complexity. Therefore, the current study aims to develop a national adaptable methodology with a simple tool for calculating and reducing buildings’ upfront carbon emissions based on a reliable regional EC dataset. To validate the developed methodology and tool, a group of architecture firms in Ireland used it to calculate and reduce the upfront carbon emissions of their projects. Fourteen buildings representing six typologies were evaluated, and on average, the results were 436 and 648 kgCO2e/m2 for residential and non-residential buildings, respectively. The calculation method and tool have been validated since the upfront carbon emissions values align with the averages of buildings in Europe. The reduction methodology was developed using the materials’ substitution strategy to replace in-situ concrete, steel, insulation, and window frames with lower-EC alternatives. The substitutions reduced the upfront carbon emissions of the residential and non-residential case studies by 21% and 23.8% on average, achieving the Irish government's 2030 Climate Action Plan. The future development of the reduction methodology can go further by considering more key materials.The authors thank the Sustainable Energy Authority of Ireland (SEAI) and Laudes Foundation for funding this research work.peer-reviewe
Training requirements for Collective Intelligence (CI) and Systems Thinking (ST) facilitators
No full textIn this commentary paper, we address the issue of programme and module development for the purpose of clarifying training requirements for collective intelligence (CI) and systems thinking (ST) facilitators.peer-reviewe
Co-production of accessible digital mental health supports with marginalised young people: a scoping review
No full textBackground and aims
While some digital supports aid in improving young people’s mental health, there is a dearth of information regarding the most effective ways to involve young people in the co-production of digital technologies. User input is essential, particularly for marginalised young people who are often excluded. The aim of this scoping review was to examine recent literature on the most common co-production processes of digital mental health supports with marginalised young people.
Method
A scoping review was conducted to identify literature published since 2021, written in English, focusing on co-production processes of digital mental health supports with marginalised young people aged 16–25. Basic information, data relating to the research question and key findings were extracted. A combination of Excel and Covidence management software was used to collate the charted data and manage the screening process. Studies were included if they used innovative youth-led approaches in the design, development, implementation or evaluation of digital mental health supports ranging from mental health promotion to targeted interventions.
Results
Out of 2341 studies initially screened, 21 studies published between 2021 and 2023 with a range of study designs and evidence were included. The studies reported on engagement with marginalised young people in the design, implementation, and/or evaluation of digital mental health technologies. The review examined qualitative and mixed methods studies from eleven countries, with most co-produced digital supports relating to mental health promotion and prevention. Most common were supports targeting general mental health and mental wellbeing. More than half of the studies included representation from LGBTQ+ young people, followed by ethnic minorities and migrants, but few included other marginalised groups. Only 6 of 21 studies used innovative approaches and many typically involved young people in only one of the co-production processes with design identified as most common. Finally, passive rather than active participation of young people was found to be pronounced. This limits young people’s opportunities to shape the outcome to the data collection process only.
Conclusion
Given the extent to which marginalised groups are actually involved in co-production, the review suggests the need for young people to be more actively involved in all co-production processes of digital mental health technologies. Offering a diverse range of methods through innovative, participatory approaches can facilitate more effective engagement from young people and provide an environment that is inclusive to a range of voices and perspectives. Lastly, the lack of inclusion of marginalised groups such as young people with a disability, or youth living in rural areas is an important issue to consider for future research.The authors would like to acknowledge the funders who supported this work through the Atlantic Futures project.peer-reviewe
Integrated cloud platform for energy management of self-sustainable island communities
No full textAn integral part of contemporary initiatives striving to promote and create self-sustainable communities has been the integration and efficient management of renewable energy sources, energy storage solutions, and sustainable heating and cooling assets. This paper presents a unique combination of diverse technologies, ranging from established data management solutions like SQL and NoSQL databases to custom semantic solutions and device-specific control adapters powered by the OpenMUC gateway. Additionally, it incorporates analytical machine learning-based forecasting solutions paired with optimization algorithms, working together to enable energy self-sufficiency. The platform constituted by these solutions is subsequently utilized to provide predictive and real-time control of energy assets in various facilities in line with the selected operation strategy. Its application, particularly in terms of effective energy storage utilization and timing of asset activation scheduling, ultimately results in improvements in renewable energy integration and overall increase of energy efficiency in the considered buildings. As demonstrated in real-world use cases tested within prosumer-based energy communities in geographical islands, the application of the platform resulted in tangible modifications to the primary statistical characteristics of electrical energy consumption clearly signified, among others, by a reduction in mean consumption of between 40W and 90W. Through scrutinizing the achieved results, it can be concluded that the platform displays the capabilities to outperform stock control algorithms provided by the inverter vendors. Expanding upon the application limited to the electric assets, use cases in the thermal domain where optimization outputs are utilized for heat pump scheduling were also discussed and presented.The research presented in this paper is partly financed by the European Union (H2020 REACT project, Grant Agreement No. 824395) and the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 451-03-66/2024-03/200034).peer-reviewe
Red squirrels exhibit antipredator behavioural changes in response to a native predator, the pine marten
No full textPrey that coevolve alongside their predators develop specific antipredator responses to reduce their predation risk. Red squirrels (Sciurus vulgaris) are one such prey species who share an evolutionary history with a predator, the pine marten (Martes martes). The recent resurgence of the pine marten has caused a decline in the invasive grey squirrel (Sciurus carolinensis) in Ireland; however, it has not had the same impact on the Irish red squirrel population. We used trail cameras to record pine marten and red squirrel visits to feeders and analysed the behaviour of the red squirrel following recent pine marten presence. We found that red squirrels displayed an enhanced antipredator response involving increased vigilance, and decreased feeding following a visit from a pine marten. This effect was strongest with increasing proximity to the pine marten visit and weakened over time. These results indicate that red squirrels can detect recent pine marten presence and assess the perceived risk of predation based on the time since the predator’s visit. These behavioural adaptations and sensitivity to the recent presence of the pine marten are hypothesized to have allowed for the red squirrel population recovery, in direct contrast to the grey squirrel decline in Ireland.The primary author is funded by a University of Galway scholarship for her PhD studies.peer-reviewe
Diagonal approximations on finite regular CW complexes: Theory, implementation, and applications
No full textWe describe an algorithm for recursively constructing diagonal approximations on finite regular CW-complexes where the closure of each cell can be explicitly collapsed to a point. The algorithm is based on the standard proof of the acyclic carrier theorem and is made constructive through the use of explicit contracting homotopies. We compare the algorithm’s output with existing diagonal approximations for the families of simplices, cubes, associahedra, and permutahedra
Modular protein frameworks via supramolecular synthons
No full textControlled protein assembly and protein crystal engineering are routes to new types of biomaterials. In this review, we examine how crystal engineering concepts, including polymorph searching, molecular tectonics and supramolecular synthons, can be adapted and applied in protein-based systems. We explore ‘mix-and-match’ approaches, as established with modular frameworks that combine interchangeable components. We review the numerous current methodologies in protein assembly and crystal engineering from (de novo) designed proteins to metal-mediated or ligand-mediated strategies. Commercially available synthetic receptors such as macrocycles are useful protein assembly mediators and are advantageous in their applicability to diverse protein targets. We highlight the use of calixarenes, cucurbiturils and proteins as building blocks (tectons), showing that reproducible inter-tecton structural units (synthons) have applications in directing protein assembly and crystal engineering.We are grateful to University of Galway, SOLEIL synchrotron (Saint Aubin, France) and Research Ireland for ongoing support. Thanks to the Irish Research Council for a postgraduate scholarship to NMM (grant number GOIPG/2021/333).peer-reviewe
Enabling electrical functionality of carbon fibre composites via tow steering during additive manufacturing
No full textAdditive manufacturing of carbon fibre reinforced polymer composites enables the steering of individual fibre tows. This design flexibility has been successfully used to optimise carbon fibre composites for mechanical loading, but limited work has been carried out investigating how it could be used to enable processes that rely on the electrical properties. In this study, several carbon fibre samples are additively manufactured, and their induction heating behaviour is measured. The results indicate that a concentric layup pattern, made possible by tow steering, increases the maximum temperature reached by over 260%, compared to equivalent additively manufactured laminates with a traditional quasi-isotropic layup pattern. This demonstrates that tow steering of continuous carbon fibre can be used to alter electrical behaviour and enable new functionality. Induction welding is then used to demonstrate a practical application of this ability. Several additively manufactured samples are designed with their main structure consisting of unidirectional fibres for strength, and the joining area of the samples manufactured using a concentric pattern with improved inductance. The samples are then joined by induction welding. The results show that tow steering enables the manufacture of composite structures with one area designed for strength and another area designed to enable induction heating.This research is funded by the Department of Business, Enterprise, and Innovation and administered by Enterprise Ireland under the Disruptive Technologies Innovation Fund, Mi-Drone Project (Ref: DT 2020 0221). The authors would also like to acknowledge the financial support from Science Foundation Ireland through the MaREI Research Centre for Climate, Energy and Marine (Grant no. 12/RC/2302_2), I-Form Centre for Advanced Manufacturing (16/RC/3872 and 21/RC/10295_P2), and Enterprise Ireland through Construct Innovate (TC-2022-0033)
Optimized intelligent learning for groundwater quality prediction in diverse aquifers of arid and semi-arid regions of India
No full textEnsuring access to safe, affordable drinking water while implementing sustainable management practices is vital for achieving the United Nations Sustainable Development Goals-2030. Accurate groundwater (GW) quality assessment plays a crucial role in enhancing water management strategies. This study evaluates GW resources across the diverse aquifer systems of arid and semi-arid regions of northwest India using the recently developed Root Mean Squared-Water Quality Index (RMS-WQI) model, optimized with machine learning (ML) techniques. A total of 772 GW samples from 36 districts of state Rajasthan were analyzed for 16 water quality (WQ) indicators/parameters, including pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), major cations (Ca2+, Mg2+, Na+, K+), anions (Cl−, CO32−, HCO3−, SO42−, NO3−, F−, PO43−), Alkalinity (ALK), and Total Hardness (TH). The results indicate slightly alkaline GW (average pH 7.9), with elevated concentrations of Na+, Cl−, SO42− and NO3− exceeding Bureau of Indian Standards (BIS). This study employs the eXtreme Gradient Boosting (XGB) algorithm, demonstrating strong predictive capabilities within the RMS-WQI model across diverse aquifers of Rajasthan. This marks the first application of RMS-WQI at a state-wide scale in India. Model performance assessment indicated groundwater quality ranging from ‘fair’ to ‘marginal’, generally meeting BIS standards, with high sensitivity and low uncertainty. Statistical metrics (Root Mean Square Error-RMSE, Mean Squared Error-MSE, Mean Absolute Error-MAE, and Percentage of Absolute Bias Error-PABE) validated the model's efficiency, with minimal error and high sensitivity. Optimization using “Optuna” further enhanced model performance, confirmed by Tukey's Honest Significant Difference (HSD) test. Sensitivity analysis demonstrated robust goodness-of-fit, while uncertainty analysis indicated minimal discrepancies, with overall uncertainty below 2 %. Spatial analysis revealed varying WQ across districts, ranging from marginal to poor, while efficiency metrics demonstrated the model's effectiveness in providing accurate assessments. The configured WQI model could substantially contribute to informing aquatic managers and strategic planners for sustainable water resource management and policy development aimed at enhancing GW quality