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Deep learning-based image registration (DLIR) has been widely developed, but it remains challenging in perceiving small and large deformations. Besides, the effectiveness of the DLIR methods was also rarely validated on the downstream tasks. In the study, a multi-scale complexity-aware registration network (MSCAReg-Net) was proposed by devising a complexity-aware technique to facilitate DLIR under a single-resolution framework. Specifically, the complexity-aware technique devised a multi-scale complexity-aware module (MSCA-Module) to perceive deformations with distinct complexities, and employed a feature calibration module (FC-Module) and a feature aggregation module (FA-Module) to facilitate the MSCA-Module by generating more distinguishable deformation features. Experimental results demonstrated the superiority of the proposed MSCAReg-Net over the existing methods in terms of registration accuracy. Besides, other than the indices of Dice similarity coefficient (DSC) and percentage of voxels with non-positive Jacobian determinant (|J(phi)|=<0), a comprehensive evaluation of the registration performance was performed by applying this method on a downstream task of multi-atlas hippocampus segmentation (MAHS). Experimental results demonstrated that this method contributed to a better hippocampus segmentation over other DLIR methods, and a comparable segmentation performance with the leading SyN method. The comprehensive assessment including DSC, |J(phi)|=<0, and the downstream application on MAHS demonstrated the advances of this method.
A systemic framework of energy efficiency in schools: Experiences from six European countries
(2023)
Schools are complex physical and social institutions within national education systems. They account for significant energy consumption and like other buildings can demonstrate inefficient patterns of energy use. Poor energy performance of educational facilities is an intricate issue driven by complex causality of interconnected and dynamic factors. Addressing this issue requires a systemic approach, which is heretofore lacking. The aim of this research is to present and describe a systemic framework to facilitate energy reduction in schools across different European contexts. This transdisciplinary approach to sustainable energy use has been piloted in 13 post-primary schools located in six countries in northwest Europe. The research implements a series of planned activities and interventions, which help to unveil a systemic approach to improving energy efficiency in schools. The findings demonstrate how this approach, together with its ensuing methodologies and strategies, can contribute to reducing carbon emissions and improve knowledge and awareness around sustainable energy.
Small area estimation methods have become a widely used tool to provide accurate estimates for regional indicators such as poverty measures. Recent research has provided evidence that spatial modelling still can improve the precision of regional and local estimates. In this paper, we provide an intrinsic spatial autocorrelation model and prove the propriety of the posterior under a flat prior. Further, we show using the SAIPE poverty data that the gain in efficiency using a spatial model can be essentially important in the presence of a lack of strong auxiliary variables.
Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models.
The Saarschleife geotope (SE-Germany) represents one of the most prominent geotopes of the SaarLorLux region and is known far beyond the borders of the Greater Region. Surprisingly, there is no visual representation of the relief history and genesis of this river meander, which is unique for Central Europe - as is common at places with comparable outstanding phenomena, such as e.g. the Rocher Saint-Michel d'Aiguilhe (France) or some national parks in the U.S. (e.g. Grand Canyon). The Saarschleife geotope therefore was choosen as a pilot object for the envisaged analysis of the landscape genesis but also regarding the 3D mapping and visualization. The visualisation presents the relief history and geological evolution of the last 300 million years in selected geological epochs, which are of fundamental importance for the understanding of today's geomorphological relief conditions, and is compiled into a summarized chronology.
Terrestrial cyanobacteria grow as phototrophic biofilms and offer a wide spectrum of interesting products. For cultivation of phototrophic biofilms different reactor concepts have been developed in the last years. One of the main influencing factors is the surface material and the adhesion strength of the chosen production strain. In this work a flow chamber was developed, in which, in combination with optical coherence tomography and computational fluid dynamics simulation, an easy analysis of adhesion forces between different biofilms and varied surface materials is possible. Hereby, differences between two cyanobacteria strains and two surface materials were shown. With longer cultivation time of biofilms adhesion increased in all experiments. Additionally, the content of extracellular polymeric substances was analyzed and its role in surface adhesion was evaluated. To test the comparability of obtained results from the flow chamber with other methods, analogous experiments were conducted with a rotational rheometer, which proved to be successful. Thus, with the presented flow chamber an easy to implement method for analysis of biofilm adhesion was developed, which can be used in future research for determination of suitable combinations of microorganisms with cultivation surfaces on lab scale in advance of larger processes.
Science on ecosystems and people to support the Kunming-Montreal Global Biodiversity Framework
(2023)
In December 2022, members of the Convention on Biological Diversity adopted the new Kunming-Montreal Global Biodiversity Framework (GBF) to guide international biodiversity conservation efforts until 2030 in order to be able to live ‘in harmony with nature’ by 2050. This framework addresses the implementation gap left after the Aichi Biodiversity Targets, which were the previous global instrument for mainstreaming biodiversity conservation between 2010 and 2020.
The aim of this editorial is to draw attention to the GBF targets that are most relevant to our readership, with two objectives: First, to suggest how Ecosystems and People may be a venue for emerging research insights in support of the GBF. Second, to highlight examples of recent research in Ecosystems and People that can contribute to enrich, or even challenge, the evidence and development of the GBF Targets.
Background: Physiotherapy education and practice have country-specific peculiarities which may limit globalization in health care. This study aimed to characterize physiotherapy practice and treatment preferences, educational qualifications, and research in Nigeria, with a view of providing vital information for transnational integration and collaboration.
Methods: A cross-sectional survey of 104 Nigerian physiotherapists was conducted. The Physical Therapy Practice Questionnaire and a self-developed proforma were used as survey tools.
Results: The mean age of respondents was 33.5 ± 9.4 years. About two-fifth of all respondents (39.4%) had an MSc and mostly practice as clinicians (51.0%) in teaching hospitals (34.6%). The respondents were mostly involved in general practice (50.0%), with a caseload of 1–10 patients per day (67.3%). Soft tissue mobilization (83%), proprioceptive neuromuscular facilitation (76%), breathing exercises (77%), and transcutaneous electrical neuromuscular stimulation (83%) were commonly used. Respondents were familiar databases and evidence-based resources (81.2%) and mostly utilize PubMed (73.3%). Regular case conferences with professional colleagues (47.6%) and treatment planning of between 11 and 30 min (40.6%) were common. Educators spend 1–3 h planning educational work (91.8%). Clinical decision-making is mostly based on professional experience, while journals are the primary resource for educational information.
Conclusion: Physiotherapy practice in Nigeria is degree based and requires registration board’s licensure. Practitioners deal with a high caseload and utilize a wide range of techniques and modalities and have tendencies to utilize personal experience and research in making clinical decisions. The parity in education and practice with advanced climes inadvertently gives physiotherapy practice in Nigeria a global purview.
1. Woody riparian vegetation (WRV) benefits benthic macroinvertebrates in running waters. However, while some functions are provided by WRV irrespective of surrounding and catchment land use, others are context-specific. In recent large-scale studies, effects of WRV on macroinvertebrates were therefore small compared to catchment land use, raising the question about the relevance of WRV for restoration.
2. Model-based recursive partitioning was used to identify context-dependent effects of WRV on the macroinvertebrates' ecological status in small (catchment area 10–100 km2) lowland (n = 361) and mountain (n = 748) streams. WRV cover was quantified from orthophotos along the near (500 m) and far (5000 m) upstream river network and used to predict the site's ecological status. Agricultural, urban and woodland cover at the local and catchment scales along with hydromorphology were considered as partitioning variables.
3. In rural agricultural landscapes, the effect of WRV on the ecological status was large, indicating that establishing near-upstream WRV can improve the ecological status by as much as two of the five classes according to the EU Water Framework Directive.
4. Even in urban landscapes, effects of far-upstream WRV were large if catchments had a moderate share of agricultural land use in addition. The beneficial effects of WRV were only limited in purely urban catchments or in a multiple stressor context.
5. Synthesis and applications. While woody riparian vegetation (WRV) can even improve the ecological status in urban settings, it is especially relevant for river management in rural agricultural catchments, where developing WRV potentially are effective measures to achieve good ecological status.
The objective of the German non-profit association NFDI (German short form for ”National Research Data Infrastructure”) is to make the data stock of the entire German science system accessible to the public. To do so, it should involve all stakeholders. However, currently the Universities of Applied Sciences (UAS) are underrepresented in the NFDI, and there is a danger of neglecting their needs. Therefore, we present the project ”Research Data Management at Universities of Applied Sciences in the State of Rhineland-Palatinate” (FDM@HAW.rlp), which is funded by the German Federal Ministry of Education and Research (BMBF) and financed within the Recovery and Resilience Facility of the European Union. In the project, seven public UAS in Rhineland-Palatinate and the Catholic University of Applied Sciences (CUAS) Mainz follow a common goal: They intend to establish an institutional RDM within a period of three years by building up competencies at the UAS, setting up services for researchers and finding solutions for a common technical infrastructure.