Background: In Turkey, nursing care in hospitals has gradually included more older patients, resulting in a need for knowledgeable geriatric nurses. It is unknown, however, whether the nursing workforce is ready for this increase. Therefore, the aim of this study is to validate the Knowledge about Older Patients Quiz (KOPQ) in the Turkish language and culture, to describe Turkish hospital nurses’ knowledge about older patients, and to compare levels of knowledge between Turkish and Dutch hospital nurses. Conclusions: The KOPQ-TR is promising for use in Turkey, although psychometric validation should be repeated using a better targeted sample with a larger ability variance to adequately assess the Person Separation Index and Person Reliability. Currently, education regarding care for older patients is not sufficiently represented in Turkish nursing curricula. However, the need to do so is evident, as the results demonstrate that knowledge deficits and an increase in older patients admitted to the hospital will eventually occur. International comparison and cooperation provides an opportunity to learn from other countries that currently face the challenge of an aging (hospital) population.
MULTIFILE
Background: In Turkey, nursing care in hospitals has gradually included more older patients, resulting in a need for knowledgeable geriatric nurses. It is unknown, however, whether the nursing workforce is ready for this increase. Therefore, the aim of this study is to validate the Knowledge about Older Patients Quiz (KOPQ) in the Turkish language and culture, to describe Turkish hospital nurses’ knowledge about older patients, and to compare levels of knowledge between Turkish and Dutch hospital nurses. Conclusions: The KOPQ-TR is promising for use in Turkey, although psychometric validation should be repeated using a better targeted sample with a larger ability variance to adequately assess the Person Separation Index and Person Reliability. Currently, education regarding care for older patients is not sufficiently represented in Turkish nursing curricula. However, the need to do so is evident, as the results demonstrate that knowledge deficits and an increase in older patients admitted to the hospital will eventually occur. International comparison and cooperation provides an opportunity to learn from other countries that currently face the challenge of an aging (hospital) population.
MULTIFILE
In this paper we propose a novel approach for validating a simulation model for a passengers' airport terminal. The validation approach is based on a "historical data" and "model-to-model" validation approach, and the novelty is represented by the fact that the model used as comparison uses historical data from different data sources and technologies. The proposed validation approach , which is presented as part of the IMHOTEP project, implements various data fusion and data analytics methods to generate the passenger "Activity-Travel-Diary", which is the model that is then compared with the results from the simulation model. The data used for developing the "Activity-Travel-Diary" comes from different sources and technologies such as: passengers data (personal mobile phone, apps), airport data (airport Wi-Fi, GPS, scanning facilities), and flight Information (flight schedules, gate allocation etc.). The simulation model is based on an agent-based simulation paradigm and includes all the passengers flows and operations within a terminal airport. The proposed validation approach is implemented in a real-life case study, Palma de Mallorca Airport, and preliminary results of the validation (calibration) process of the simulation model are presented.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Horticulture crops and plants use only a limited part of the solar spectrum for their growth, the photosynthetically active radiation (PAR); even within PAR, different spectral regions have different functionality for plant growth, and so different light spectra are used to influence different properties of the plant, such as leaves, fruiting, longer stems and other plant properties. Artificial lighting, typically with LEDs, has been used to provide these specified spectra per plant, defined by their light recipe. This light is called steering light. While the natural sunlight provides a much more sustainable and abundant form of energy, however, the solar spectrum is not tuned towards specific plant needs. In this project, we capitalize on recent breakthroughs in nanoscience to optimally shape the solar spectrum, and produce a spectrally selective steering light, i.e. convert the energy of the entire solar spectrum into a spectrum most useful for agriculture and plant growth to utilize the sustainable solar energy to its fullest, and save on artificial lighting and electricity. We will take advantage of the developed light recipes and create a sustainable alternative to LED steering light, using nanomaterials to optimally shape the natural sunlight spectrum, while maintaining the increased yields. As a proof of concept, we are targeting the compactness of ornamental plants and seek to steer the plants’ growth to reduce leaf extension and thus be more valuable. To realize this project the Peter Schall group at the UvA leads this effort together with the university spinout, SolarFoil, whose expertise lies in the development of spectral conversion layers for horticulture. Renolit - a plastic manufacturer and Chemtrix, expert in flow synthesis, provide expertise and technical support to scale the foil, while Ludvig-Svensson, a pioneer in greenhouse climate screens, provides the desired light specifications and tests the foil in a controlled setting.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
