Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
Objectives To report (1) the injury incidence in recreational runners in preparation for a 8-km or 16-km running event and (2) which factors were associated withan increased injury risk. Methods Prospective cohort study in Amsterdam, the Netherlands. Participants (n=5327) received a baseline survey to determine event distance (8 km or 16 km), main sport, running experience, previous injuries, recent overuse injuries and personal characteristics. Three days after the race, they received a follow-up survey to determine duration of training period, running distance per week, training hours, injuries during preparation and use oftechnology. Univariate and multivariate regression models were applied to examine potential risk factors for injuries. Results 1304 (24.5%) participants completed both surveys. After excluding participants with current health problems, no signed informed consent, missing or incorrect data, we included 706 (13.3%) participants. In total, 142 participants (20.1%) reported an injury during preparation for the event. Univariate analyses (OR: 1.7, 95% CI 1.1 to 2.4) and multivariate analyses (OR: 1.7, 95% CI 1.1 to 2.5) showed that injury history was a significant risk factor for running injuries (Nagelkerke R-square=0.06). Conclusion An injury incidence for recreational runners in preparation for a running event was 20%. A previous injury was the only significant risk factor for runningrelated injuries.
Background: As our global population ages, malnutrition and sarcopenia are increasingly prevalent. Given the multifactorial nature of these conditions, effective management of (risk of) malnutrition and sarcopenia necessitates interprofessional collaboration (IPC). This study aimed to understand primary and social care professionals’ barriers, facilitators, preferences, and needs regarding interprofessional management of (risk of) malnutrition and sarcopenia in community-dwelling older adults. Methods: We conducted a qualitative, Straussian, grounded theory study. We collected data using online semi-structured focus group interviews. A grounded theory data analysis was performed using open, axial, and selective coding, followed by developing a conceptual model. Results: We conducted five online focus groups with 28 professionals from the primary and social care setting. We identified five selective codes: 1) Information exchange between professionals must be smooth, 2) Regular consultation on the tasks, responsibilities, and extent of IPC is needed; 3) Thorough involvement of older adults in IPC is preferred; 4) Coordination of interprofessional care around the older adult is needed; and 5) IPC must move beyond healthcare systems. Our conceptual model illustrates three interconnected dimensions in interprofessional collaboration: professionals, infrastructure, and older adults. Conclusion: Based on insights from professionals, interprofessional collaboration requires synergy between professionals, infra-structure, and older adults. Professionals need both infrastructure elements and the engagement of older adults for successful interprofessional collaboration.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
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
"Rising Tides, Shifting Imaginaries: Participatory Climate Fiction-Making with Cultural Collections," is an transdisciplinary research project that merges information design, participatory art, and climate imaginaries to address the pressing challenge of climate change, particularly the rising sea levels in the Netherlands. The doctoral research project aims to reimagine human coexistence with water-based ecosystems by exploring and reinterpreting audiovisual collections from various archives and online platforms. Through a creative and speculative approach, it seeks to visualize existing cultural representations of Dutch water-based ecosystems and, with the help of generative AI, develop alternative narratives and imaginaries for future living scenarios. The core methodology involves a transdisciplinary process of climate fiction-making, where narratives from the collections are amplified, countered, or recombined. This process is documented in a structured speculative archive, encompassing feminist data visualizations and illustrated climate fiction stories. The research contributes to the development of Dutch climate scenarios and adaptation strategies, aligning with international efforts like the CrAFt (Creating Actionable Futures) project of the New European Bauhaus program. Two primary objectives guide this research. First, it aims to make future scenarios more relatable by breaking away from traditional risk visualizations. It adopts data feminist principles, giving space to emotions and embodiment in visualization processes and avoiding the presentation of data visualization as neutral and objective. Second, the project seeks to make scenarios more inclusive by incorporating intersectional and more-than-human perspectives, thereby moving beyond techno-optimistic approaches and embracing a holistic and caring speculative approach. Combining cultural collections, digital methodologies, and artistic research, this research fosters imaginative explorations for future living.
