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Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individuals) and Group M (18 individuals), wore the devices and manually logged their activities hourly and minutely, respectively. Prioritising accessibility and inclusivity, we selected three classification algorithms: k-nearest neighbours (IBk), decision trees (J48), and random forests (RF), based on: (1) proven efficacy in existing literature, (2) understandability and transparency for laypersons, (3) availability on user-friendly platforms like WEKA, and (4) efficiency on basic devices such as office laptops or smartphones. Accuracy improved with finer temporal resolution and detailed activity categories. However, when compared to other published human activity recognition research, our accuracy rates, particularly for less complex activities, were not as competitive. Misclassifications were higher for vague activities (resting, playing), while well-defined activities (smoking, cooking, running) had few errors. Including environmental sensor data increased accuracy for all activities, especially playing, smoking, and running. Future work should consider exploring other explainable algorithms available on diverse tools and platforms. Our findings underscore ML’s potential in exposure studies, emphasising its adaptability and significance for laypersons while also highlighting areas for improvement.

New mutations provide the raw material for evolution and adaptation. The distribution of fitness effects (DFE) describes the spectrum of effects of new mutations that can occur along a genome, and is, therefore, of vital interest in evolutionary biology. Recent work has uncovered striking similarities in the DFE between closely related species, prompting us to ask whether there is variation in the DFE among populations of the same species, or among species with different degrees of divergence, that is whether there is variation in the DFE at different levels of evolution. Using exome capture data from six tree species sampled across Europe we characterized the DFE for multiple species, and for each species, multiple populations, and investigated the factors potentially influencing the DFE, such as demography, population divergence, and genetic background. We find statistical support for the presence of variation in the DFE at the species level, even among relatively closely related species. However, we find very little difference at the population level, suggesting that differences in the DFE are primarily driven by deep features of species biology, and those evolutionarily recent events, such as demographic changes and local adaptation, have little impact. Nasl. z nasl. zaslona. Opis vira z dne 12. 12. 2023. Število sodelavcev v konzorciju GenTree Consortium: 128. Sodelavca pri raziskavi: M. Bajc. M. Westergen. Bibliografija: str. 15-16. Abstract.

NBS provides the technical basis for adaptation to climate change, and co-creation is the vehicle for the co-production of knowledge and innovation, both forming a strong binomial for the UE Green Infrastructures Strategy. Nonetheless, one of the main challenges for the implementation of effective co-creation strategies is the incorporation of knowledge from diverse social systems. Knowledge production has been approached through different methodological models, such as the quintuple helix innovation by Carayannis, or the diffuse/expert knowledge model by Manzini. These theoretical models are based on linear knowledge transfers, without sufficiently depicting alternative knowledge flows among (un)conventional actors. In view of these limitations, the research proposes a third strategy: the KREBS cycle of creativity defined by Oxman is a conceptual map capable of describing knowledge transfers across the four modalities of human creativity (i.e., science, engineering, design, and art). Providing sufficient “creative energy” in a co-creation process would guarantee the successful production of knowledge. Thus, the research seeks to illuminate different co-creation strategies to promote “creative energy” in the design of the Cyborg Garden (CG) in Madrid, giving a novel application to Oxman’s methodological framework based on the Carayannis’s and Manzini’s models.

Manta and devil rays (mobulids) are filter feeding elasmobranchs with extreme K-selective life histories found circumglobally from temperate to tropical waters. Their vulnerability to fisheries exploitation, bycatch, boat collisions, entanglement and unregulated tourism is exacerbated by their aggregative behavior. Studies have identified aggregation sites around the world for all nine mobulid species, with these groupings varying from a few individuals to thousands. However, the terminology used to define these aggregations and the drivers underpinning them remain unclear, hindering the development of effective management and conservation strategies. Here, we analyze aggregation behavior for mobulid species, providing consistent definitions for grouping events and summarizing the existing research on drivers and environmental factors triggering these events. We find that aggregation behaviors facilitate socializing and key life history functions in mobulids, including feeding, courtship and mating, predation avoidance, cleaning, and thermoregulation. Conservation threats and management mitigation opportunities associated with aggregations sites include fisheries, tourism, spatial protection, and climate change. Finally, we highlight knowledge gaps for future research prioritization and developments in the field for the identification of aggregation sites, the study of aggregation size and demographics and the functions and timing of aggregations.

With increasing exploitation of groundwater resources and implementation of various activities in their recharge areas, it is vital to conduct a comprehensive assessment of aquifers to ensure their conservation and sustainable management. In the present study, we used a comprehensive approach to conceptualise and identify the functioning of two connected aquifer systems in north-eastern Slovenia: the Quaternary porous aquifers Dravsko polje and Ptujsko polje. The study presents the conceptual models of both aquifers and their interconnectedness using separate mathematical-numerical models with the aim of ensuring an integrated management of these alluvial aquifer systems. It also highlights the importance of understanding connections between such systems for simulating groundwater flow and transport of different contaminants. To describe the entire aquifer system, the study defines its three essential elements: the geometry of the aquifers, their recharge by precipitation, and other boundary conditions. The geometry of the Quaternary aquifers was defined using Sequential Indicator Simulation (SIS) with the ESRI’s ArcMap software. Next, LIDAR was used for determining their surface geometry. The hydrogeologic model was designed using the Groundwater Modelling System (GMS) developed by AQUAVEO. We used the MODFLOW 2000 calculation method based on the finite difference method (FDM). The model was calibrated with the PEST module, which was used to calibrate hydraulic conductivity and hydraulic heads between the measured and modelled data. Finally, the model was validated using the Nash–Sutcliffe (NSE) efficiency coefficient. In addition, the model results estimated using the PEST tool were validated with the hydraulic conductivities determined at the pumping sites (pumping tests), each belonging to water protection zones that define the maximum travel time of the particles. This was performed using the MODPATH method. The paper also presents the possibility of modelling heterogeneous but interdependent aquifers in a groundwater body. Modelling the connection between the two aquifers, which are the most important ones in the region, is essential for a comprehensive management of the entire system of water resources. The models allow for a better understanding of groundwater flow in both aquifers. Moreover, their interconnectedness will be used for further studies in this field, as well as for integrated water management.

Green synthesis of a magnetite/graphitic carbon nitride 2D nanocomposite for efficient Hg2+ remediation in different aqueous matrices utilising a thermal assist sonication method.

In this paper, we provide conceptual and practical insights into social innovation, highlighting its role in achieving a more sustainable provision of ecosystem services from forests, as well as its role in tackling societal challenges and utilising the opportunities available in marginalised mountain areas across Europe. The findings indicate that social innovation and innovative governance mechanisms are crucial for the promotion of sustainability and multi-functionality in mountain forestry, as well as for the enhancement of a smart, green, and just transition of marginalised rural areas, so that forest-dependent communities, living and working in the vicinity of woodlands, can become healthier, happier, and more prosperous. The results show that social innovation has a power of enabling forest-dependent communities and woodland-based social enterprises to build and realise their capacities, while reducing existing inequalities, and promoting social justice and inclusion. There is also empirical evidence to suggest that by adding to improving human wellbeing, social innovation can create new responses to pressing social demands (e.g., for climate neutrality) that are not adequately addressed by markets or existing (e.g., public) institutions. We believe that the conceptualization and operationalisation of social innovation in the context of multi-functional forestry, offered by this research and its findings, can help informing forest policy and management decisions and the design of practice measures for Green Recovery and a long-term sustainability of socio-ecological systems in marginalised mountain areas. The authors are grateful to the European Union's H2020 Programme supporting the projects of SIMRA (GA 677622), SHERPA (GA 862448), FirEUrisk (GA 101003890) and RURACTIVE (GA 101084377) and to the Scottish Government for supporting, through its Strategic Research Programme (2022-2027), project of JHI-D5-1 in the Natural Capital Topic. The authors are grateful to the splendid team of the H2020 SIMRA project coordinated by the James Hutton Institute, to the SIMRA Think Tank, our colleagues and stakeholders working in case studies, living labs and innovation actions in all of the above-mentioned projects, and to numerous supporters of social innovation initiatives across Europe, and beyond. This research is linked to activities of the IUFRO Unit 04.05.05 on Social Innovation and Entrepreneurship.