Open Geospatial Consortium

Climate change impacts are increasingly manifested at local scales, where mitigation and adaptation strategies are implemented. Despite the growing wealth of available climate data and services, their effective usage in local climate impact assessment and decision-making processes for mitigation and adaptation planning remains limited due to scale mismatches, computational constraints, complexity, and usability barriers for non-domain experts. Addressing these challenges requires both advanced computational methods and improved access to climate data and analysis tools. The EU Horizon project, FOCAL, bridges the gap between data, services, and their users by implementing an open compute platform that combines intelligent workflow management with high-performance computing (HPC) resources to allow for an efficient exploration of climate data on a local scale. In addition, innovative artificial intelligence (AI) tools are developed and made available to enhance climate data analysis in terms of speed, robustness, pattern detection, and localization; thereby expanding the toolkit of climate data analysis and impact assessment methods. A main objective of FOCAL is to support science-based, actionable decision-making processes in forestry and urban planning through its provided tools. In a co-design process involving developers and potential platform users from two forest pilot regions with contrasting ecological and management contexts (Forest Pilots) as well as a pilot city (Urban Pilot), web applications for intuitive user-platform-interaction and workflows, grounded in state-of-the-art climate science, to address concrete user questions in forestry and urban planning have been specified. As a result, decision makers can efficiently use climate data for the development of climate adaptation strategies.This contribution focuses on the Urban Pilot, implemented for the pilot city Constance (Baden-Württemberg, southern Germany), located at the western end of Lake Constance. Three core workflows have been developed:1) Regional climate change workflow: provision of robust regional climate change information for the past and the future under different global warming levels for urban areas, based on regional climate model and localized climate data, serving multi-sectoral local climate impact assessments;2) Urban hot and cool spot workflow: detection and high-spatial-resolution visual exploration of hot and cool spots in urban environments, supporting exposure assessment by integrating additional data (e.g., population or infrastructure data), risk assessment, and the planning of urban heat resilience measures and cooling spaces;3) Urban blue spot workflow: identification of blue spots (rainfall accumulation hazards) and provision of blue spot data in urban landscapes using processed precipitation data and extreme precipitation scenarios, supporting applications in hydrological modeling, flood risk management, and climate adaptation.By leveraging HPC-based data processing and AI-assisted analysis, these workflows translate complex climate data into actionable, locally relevant information. While demonstrated for the pilot city Constance, the methods and workflows are transferable to other urban areas, contributing to scalable and reproducible climate services.

Abstract National policies significantly shape resource allocation and priorities in EdTech development. This paper presents insights from six countries through a systems thinking perspective. Drawing on lessons from the COVID-19 pandemic, the authors explore how national policies evolved in response to global EdTech trends. Each country offers unique approaches as case studies. China emphasizes digital transformation and the use of artificial intelligence in teacher education. Germany focuses on enhancing student media literacy through school initiatives. Japan accelerates digitalization through cross-ministerial efforts to address educational disparities. Italy emphasizes the updating of school curricula, tertiary offerings, and digital innovation projects. Norway underscores teacher autonomy and innovative methods for digital literacy, while the United States focuses on academic recovery and addressing the challenges posed by new forms of digital divide. The case studies offer insights into global EdTech trends while cautioning against viewing the field as monolithic. The authors synthesize key strategies in national priorities and practices and provide recommendations for an ethical and effective approach to EdTech development.

Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, have become a gold standard tool for estimating compound affinity changes. Here we present the results of a large-scale pre-competitive collaborative assessment of relative binding free energy (RBFE) calculations generated by 15 pharmaceutical companies. We evaluate an open-source and MIT licensed RBFE Protocol from the Open Free Energy (OpenFE) ecosystem across both public and blinded private datasets, encompassing over 1,700 ligands in total. For the public dataset, the weighted RMSE across the 58 systems was 1.73 [1.53, 1.96] kcal/mol, with 10 of the systems reaching sub-kcal/mol accuracy. For the private dataset, the weighted RMSE across the 37 systems was 2.44 [1.94, 3.06] kcal/mol, with only 2 of the systems reaching sub-kcal/mol accuracy, reflecting the increased complexity of real-world drug discovery. The protocols performance was system-dependent, with no single dominant error source, indicating that accuracy is primarily influenced by input quality and transformation type. Overall, these benchmark results are encouraging and indicate that OpenFE is ready for large-scale industrial applications, with an "out-of-the-box" accuracy that approaches that of commercial solutions. While comparison against published FEP+ results, which were obtained after manual parameter optimization, shows comparable ranking statistics, a gap remains in error statistics, for which we outline possible paths toward improvement. The protocol meets key criteria required for production use in an industrial setting: it shows robust performance, generates reproducible results, and achieves both sufficient throughput and rapid convergence.

ABSTRACTThe aim of this systematic literature review (SLR) of reviews was to update the evidence, established in a 2017 SLR, on the impact of obesity and weight loss by various interventions on health‐related quality of life (HRQoL). In total, eight SLRs and/or meta‐analyses published since 2017 were identified. The results consistently demonstrated a negative association between obesity and HRQoL, with some evidence suggesting poorer HRQoL in people with adverse metabolic profiles. In addition to substantial weight or body mass index reduction, metabolic and bariatric surgery (MBS) or endoscopic bariatric therapies resulted in significant and clinically relevant HRQoL improvements, with pronounced and persistent effects on the physical components in particular. HRQoL typically improved within 1–2 years after MBS and stabilised or deteriorated while remaining above baseline in subsequent years, likely due to weight regain. Evidence for the benefits of exercise interventions on HRQoL was inconclusive. Notably, no SLRs on anti‐obesity medications were identified, limiting conclusions on this emerging treatment area. This updated SLR expands on previous findings from the 2017 SLR, providing additional insights into underexplored areas, including the role of metabolic profiles in HRQoL and trends of HRQoL after MBS. Evidence synthesis remains challenging due to heterogeneity in HRQoL measurements used in this field.

• A novel methodology for Automatic Fault Detection and Diagnosis under limited data is developed • Dynamic and functional variables are fully integrated into the inference as random variables • The methodology is able to identify most faults efficiently, even simultaneous ones • A novel encoder-decoder based emulator is proposed for calibration of dynamic computer models Early Automatic Fault Detection and Diagnosis (AFDD) is essential in energy use and carbon emission reduction to meet the Paris Agreement goals. However, AFDD application scope is usually narrowed down to buildings equipped with costly hard-to-maintain Advanced Metering Infrastructure (AMI). The methodology developed in this paper relies on commonly metered data and numerical simulations of building energy performance to be applicable to most buildings. Besides, while other works in the field leveraged smart meters to measure dynamic solicitations about the building, e.g. building occupancy, a different way of dealing with these random variables is studied by treating them as unknown parameters in the calibration process, further decreasing the dependence to fine-grain metered data. The resulting methodology is promising, as it is able to identify most symptoms and orient an operator towards most likely faults with a reduced data set, and without specifying the faults beforehand. Operationally, this methodology could gain in robustness and efficiency if coupled with simple rule-based AFDD. Furthermore, additional metered data points may prove critical to greatly improve the AFDD efficiency in a cost-effective way.

Human activity and disturbance are thought to be perceived as a source of risk analogous to predation risk in wildlife. As such, can alter behaviour and habitat use of foraging animals. Through increased exposure to human disturbance, urban wildlife may face an increase in perceived risk during food acquisition. Urban habitats also include novel resources that could result in urban wildlife having to face distinct tradeoffs associated with foraging and patch use. To examine how a successful invasive mammal, the eastern grey squirrel, balances risk and safety under human disturbance, we measured giving‐up densities (GUD) at artificial food patches placed at sites subject to varying levels of urbanisation to investigate how features associated with human disturbance might influence feeding decisions. We found differences in GUDs between ‘safe' and risky' patches were reduced closer to roads under noisy conditions, suggesting that the risk of predation is perceived by squirrels as reduced when disturbance from human activities is highest. There was also a significant effect of the variability in noise levels on patterns of patch exploitation associated with roads, with the larger GUD differences between safe and risky patches found further from roads exacerbated under more variable noise levels, suggesting the consistency of human disturbances also moderated squirrel risk perception while foraging around roads. Our results suggest that human activities can have doubled‐edged impacts on the urban landscape of fear through offering reduced risk from predators whilst increasing foraging costs via noise disturbance.

The Zika virus (ZIKV), discovered in Africa in 1947, swiftly spread across continents, causing significant concern due to its recent association with microcephaly in newborns and Guillain-Barré syndrome in adults. Despite a decrease in prevalence, the potential for a resurgence remains, necessitating urgent therapeutic interventions. Like other flaviviruses, ZIKV presents promising drug targets within its replication machinery, notably the NS3 helicase (NS3Hel) protein, which plays critical roles in viral replication. However, a lack of structural information impedes the development of specific inhibitors targeting NS3Hel. Here we applied high-throughput crystallographic fragment screening on ZIKV NS3Hel, which yielded structures that reveal 3D binding poses of 46 fragments at multiple sites of the protein, including 11 unique fragments in the RNA-cleft site. These fragment structures provide templates for direct design of hit compounds and should thus assist the development of novel direct-acting antivirals against ZIKV and related flaviviruses, thus opening a promising avenue for combating future outbreaks.