Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
Tipping is a social norm in many countries and has important functions as a source of income, with significant social welfare effects. Tipping can also represent a form of lost tax revenue, as service workers and restaurants may not declare all cash tips. These interrelationships remain generally insufficiently understood. This paper presents the results of a comparative survey of resident tipping patterns in restaurants in Spain, France, Germany, Switzerland, Sweden, Norway, and the Netherlands. ANOVA and ANCOVA analyses confirm significant variation in tipping norms between countries, for instance with regard to the frequency of tipping and the proportion of tips in relation to bill size. The paper discusses these findings in the context of employment conditions and social welfare effects, comparing the European Union minimum wage model to gratuity-depending income approaches in the USA. Results have importance for the hospitality sector and policymakers concerned with social welfare
MULTIFILE
Background: Lipoedema is a chronic disorder of adipose tissue typically involving an abnormal build-up of fat cells in the legs, thighs and buttocks. Occurring almost exclusively in women, it often co-exists with obesity. Due to an absence of clear objective diagnostic criteria, lipoedema is frequently misdiagnosed as obesity, lymphoedema or a combination of both. The purpose of this observational study was to compare muscle strength and exercise capacity in patients with lipoedema and obesity, and to use the findings to help distinguish between lipoedema and obesity. Design: This cross-sectional, comparative pilot study performed in the Dutch Expertise Centre of Lymphovascular Medicine, Drachten, a secondary-care facility, included 44 women aged 18 years or older with lipoedema and obesity. Twenty-two women with lipoedema (diagnosed according the criteria of Wold et al, 1951) and 22 women with body mass index ≥30kg/m2 (obesity) were include in the study. No interventions were undertaken as part of the study. Results: Muscle strength of the quadriceps was measured with the MicroFET™, and functional exercise capacity was measured with the 6-minute walk test. The group with lipoedema had, for both legs, significantly lower muscle strength (left: 259.9 Newtons [N]; right: 269.7 N; p < 0.001) than the group with obesity. The group with lipoedema had a non-significant, but clinically relevant lower exercise-endurance capacity (494.1±116.0 metres) than the group with obesity (523.9±62.9 metres; p=0.296). Conclusions: Patients with lipoedema exhibit muscle weakness in the quadriceps. This finding provides a potential new criterion for differentiating lipoedema from obesity. We recommend adding measuring of muscle strength and physical endurance to create an extra diagnostic parameter when assessing for lipoedema.
LINK
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
The ongoing environmental changes in the Arctic call for a deeper understanding of how local communities experience and adapt to these transformations. This PhD examines sense of place and how this shapes future climate imaginaries within riverine communities, focusing on the Altaelva community in northern Norway. In northern Peru, the community has long experienced alternating environmental changes due to the El Niño Southern Oscillation, nowadays intensified by climate change. By examining how these communities adapt to cyclical environmental shifts, this case study provides comparative insights relevant to the Arctic, where climate change presents a more linear, continuous impact.Utilizing qualitative methods, I explore how individuals and groups form emotional and cognitive attachments to the environment while living in a changing climate. This PhD investigates locally rooted visions of climate futures that are informed by the community's sense of place, so-called “emplaced climate imaginaries”. By focusing on how the community’s attachment to the river influences their perceptions of future climate scenarios, I aim to identify the ways in which these imaginaries contribute to sustainable adaptation strategies.The study’s focus on the intersection of emotional bonds to place and anticipatory climate futures offers insights into how communities cope with and adapt to environmental change. These findings will contribute to broader discussions on climate resilience, emphasizing the importance of integrating local narratives and experiences into climate adaptation policies. The research not only provides a lens into Arctic futures but also underscores the role of local, place-based attachments in shaping responses to climate change.
Higher education offers great flexibility as students are largely free to decide where, when, and how to study. Being successful in such an environment requires well-developed self-regulated learning skills. However, every teacher in higher education knows that students experience ample difficulty to self-regulate their learning. They struggle to set and plan learning goals, and to gain sufficient depth in learning when preparing for exams. These struggles can negatively impact their learning, well-being, academic achievement, and professional life. On top of the existing flexibility in higher education, a need for more flexibility in what students learn is becoming evident. That is, students have room for flexible learningapproaches (i.e., deciding what learning goals or materials to study and how) and/or flexible learning trajectories (i.e.,choosing what combination of courses to take). This places an additional burden on students’ self-regulated learning skills. We posit that for students to thrive in flexible higher education, practice-oriented research on supporting students’self-regulated learning skills is required. Our collaborative consortium will i) unravel how students can be optimally scaffolded within flexible learning approaches and flexible learning trajectories, ii) examine how to optimize teacher and technological support, and iii) study how student autonomy and motivation can be guarded. We will set up a practice-oriented research program with both qualitative and quantitative methods, including design-based research, action research, pre-post comparative intervention studies, and large-scale correlational research. The findings will impact higher education through (technological) design guidelines and intervention programs for educational professionals, andsupport-modules for students.
