Background and Objectives: Various interventions aim to reduce obesity and promote healthy lifestyles among different cultural groups.Methods: We have conducted a systematic literature review, following PRISMA guidelines (registered at https://doi.org/10.17605/OSF.IO/HB9AX), to explore profiles of cultural adaptation and parenting approach of lifestyle interventions for families with young children (1-4 years).Results: Our search (in CINAHL, ERIC, PsycINFO, PubMed, Scopus, and SSCI) yielded 41 studies reporting 31 interventions. Drawing on Intervention Mapping, we applied a newly developed framework with various indicators of cultural adaptation and a parenting approach to analyze interventions. Our review shows clear differences in the level of cultural adaptation. A categorical principal component analysis revealed 6 different empirical profiles of cultural adaptation.Conclusions: Based on our profiles, we discuss how cultural adaptation can be strengthened in the design of future early interventions aimed at promoting a healthy lifestyle.
MULTIFILE
Trust between providers and consumers in the sharing economy are crucial to complete transactions successfully. From a consumer's perspective, a provider's profile is an important source of information for judging trustworthiness, because it contains multiple trust cues. However, the effect of a provider's self-description on perceived trustworthiness is still poorly understood. We examine how the linguistic features of a provider's self-description predict perceived trustworthiness. To determine the perceived trustworthiness of 259 profiles, real consumers on a Dutch sharing platform rated these profiles for trustworthiness. The results show that profiles were perceived as more trustworthy if they contained more words, more words related to cooking, and more words related to positive emotions. Also, a profile's perceived trustworthiness score correlated positively with the provider's actual sales performance. These findings indicate that a provider's self-description is a relevant signal to consumers, even though it seems easy to fake.
Systemic sclerosis (SSc) is an autoimmune disease which is characterized by vasculopathy, tissue fibrosis and activation of the innate and adaptive immune system. Clinical features of the disease consists of skin thickening and internal organ involvement. Due to the heterogeneous nature of the disease it is difficult to predict disease progression and complications. Despite the discovery of novel autoantibodies associated with SSc, there is an unmet need for biomarkers for diagnosis, disease progression and response to treatment. To date, the use of single (surrogate) biomarkers for these purposes has been unsuccessful. Combining multiple biomarkers in to predictive panels or ultimately algorithms could be more precise. Given the limited therapeutic options and poor prognosis of many SSc patients, a better understanding of the immune-pathofysiological profiles might aid to an adjusted therapeutic approach. Therefore, we set out to explore immunological fingerprints in various clinically defined forms of SSc. We used multilayer profiling to identify unique immune profiles underlying distinct autoantibody signatures. These immune profiles could fill the unmet need for prognosis and response to therapy in SSc. Here, we present 3 pathophysiological fingerprints in SSc based on the expression of circulating antibodies, vascular markers and immunomodulatory mediators.
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
Over the past decade, the trend in both the public sector and industry has been to outsource ICT to the cloud. While cost savings are often used as a rationale for outsourcing, another argument that is frequently used is that the cloud improves security. The reasoning behind this is twofold. First, cloud service providers are typically thought to have skilled staff trained in good security practices. Second, cloud providers often have a vastly distributed, highly connected network infrastructure, making them more resilient in the face of outages and denial-of-service attacks. Yet many examples of cloud outages, often due to attacks, call into question whether outsourcing to the cloud does improve security. In this project our goal therefore is to answer two questions: 1) did the cloud make use more secure?and 2) can we provide specific security guidance to support cloud outsourcing strategies? We will approach these questions in a multi-disciplinary fashion from a technical angle and from a business and management perspective. On the technical side, the project will focus on providing comprehensive insight into the attack surface at the network level of cloud providers and their users. We will use a measurement-based approach, leveraging large scale datasets about the Internet, both our own data (e.g. OpenINTEL, a large- scale dataset of active DNS measurements) and datasets from our long-term collaborators, such as CAIDA in the US (BGPStream, Network Telescope) and Saarland University in Germany (AmpPot). We will use this data to study the network infrastructure outside and within cloud environments to structurally map vulnerabilities to attacks as well as to identify security anti-patterns, where the way cloud services are managed or used introduce a weak point that attackers can target. From a business point of view, we will investigate outsourcing strategies for both the cloud providers and their customers. For guaranteeing 100% availability, cloud service providers have to maintain additional capacity at all times. They also need to forecast capacity requirements continuously for financially profitable decisions. If the forecast is lower than the capacity needed, then the cloud is not able to deliver 100% availability in case of an attack. Conversely, if the forecast is substantially higher, the cloud service provider might not be able to make desired profits. We therefore propose to assess the risk profiles of cloud providers (how likely it is a cloud provider is under attack at a given time given the nature of its customers) using available attack data to improve the provider resilience to future attacks. From the costumer perspective, we will investigate how we can support cloud outsourcing by taking into consideration business and technical constraints. Decision to choose a cloud service provider is typically based on multiple criteria depending upon the company’s needs (security and operational). We will develop decision support systems that will help in mapping companies’ needs to cloud service providers’ offers.
The application of sensors in water technology is a crucial step to provide broader, more effi-cient and more circular systems. Among the different technologies used in this filed, ultra-sound based systems are widely used in water technology, basically to generate energy peaks for cell lyse and particle separation. In this work we propose the adaptation of a (cur-rently used for medical applications) ultra sound ecosystem to monitor the vertical profile of solid particles in UASB reactors. Such information is nowadays obtained via long duration (solids) analysis and can compromises the efficiency of such reactors, especially regarding the sludge stabilization and phase separation. The project is a small part of a big effort done by different countries, e.g. Brazil, UK and The Netherlands, to bring international technology and expertise to improve the quality of waste water systems in Brazil, by supporting tech-nology and knowledge sharing. If proven feasible, the concept can generate a big business market to the involved Dutch (SME) partners as well as favor the automation of WWTP in Brazil and around the world.
