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A microfluidic-chip-based system for the determination of nutrient ion concentrations in hydroponic solutions by means of ion-selective electrodes (ISEs)
No full text723728Within this paper microfluidic-chip-based measuring technology is introduced, based on miniaturized solid contact liquid membrane ion-selective electrodes, implemented on an electronic board for direct potentiometric measurement of different ion types in aqueous solution. Ion-selective electrodes are prepared on top of gold electrodes on an electronic board, forming double-layered films, consisting of a graphite ion-to-electron conversion layer and a PVC layer containing an ionophore, being specific for a certain type of ion. The electronic board is covered with a microfluidic chip, connecting the hydroponic solution to the different ion-selective electrodes. This procedure allows for minimized sample volumes, producing low waste and for a compact setup, suitable for installation on site of a hydroponic plant. Ions under investigation were ammonium, nitrate, calcium, potassium and phosphate. For all ion types suitable ionophores were found and tested. The measured potential differences against a commercial standard silver/silverchloride (Ag/AgCl) electrode show a nearly Nernstian behaviour with a slope close to the theoretical limit. NO3--ISEs were tested in mixed solutions of K3PO4/NH4NO3(aq) (constant ratio of ions), showing that the sensitivity for NO3--concentration is identical for K3PO4/NH4NO3(aq) solutions and pure KNO3(aq) solutions, but the offset is shifted by -15.4 mV
Open-Source EMT Model of Grid-Forming Converter with Industrial Grade SelfSync and SelfLim Control
No full textThe declining inertia due to fewer synchronous machines in modern power systems requires power electronic converters with grid-forming properties. Accurate dynamic models of such converters are required to study the influence of the grid-forming control on power system stability. Vendor-specific models are generally not disclosed. An open-source EMT model of an industrial grade controller called SelfSync, including a current limitation controller called SelfLim, is provided in the widely used simulation software DIgSILENT PowerFactory in this work. The controller is compared to a generic model in various study cases using the FNN grid-forming test system and the Nordic 72-bus system. It is shown that the controller is suitable for the analysis of complex contingencies
Risk stratification for the enrichment of potential high-risk groups in a cognitive normal study population
No full texte102630BACKGROUND: Dementia, particularly Alzheimer's disease, poses a significant health challenge worldwide. The PREDICTOM study aims to address this issue by establishing a comprehensive screening platform for dementia, with a specific focus on Alzheimer's disease. This innovative approach not only seeks to improve early detection but also evaluates novel biomarkers for their predictive value in assessing dementia risk.
METHOD: The study utilizes a three-tiered funneling process: Level 1 consists of at-home screening; Level 2 encompasses a risk assessment that could potentially be carried out by general practitioners; and Level 3 entails a more comprehensive examination, which is usually conducted by a medical specialist. Following the FDA's enrichment trial concept, the screening phase targets the statistical overrepresentation of potential "high-risk" patients for further assessment in levels 2 and 3. For this purpose, we develop two risk models using retrospective data. The first is a machine learning model based on the UK biobank, estimating individual risk of developing dementia within 3-5 years post-baseline, utilizing common risk factors identified by the Lancet commission. The second model, a normative model based on the existing PROTECT study cohort, analyzes age-dependent cognitive decline (via the FLAME test battery) to assess individual abnormal cognitive function. Both models will be applied to the PREDICTOM participants and subsequently can be used to rank them according to their risk of developing dementia and/or of already showing signs of cognitive decline. To combine the predictions from these models, we will apply statistical consensus ranking using the FAST algorithm. RESULT: We will designate the top n = 400 patients as the "potential high-risk" group for Level 2 inclusion, while the bottom n = 215 patients will serve as the "potential low-risk" group. Due to logistical considerations, both groups will undergo Level 2/3 assessments in batches, culminating in Level 3 evaluations for amyloid beta positivity and cognitive impairment (Figure 1).
CONCLUSION: The risk stratification ensures an enrichment of the potential high-risk group, allowing for representative analyses of the novel biomarkers and a subsequent multi-modal analysis accordingly. The results of this study could potentially lead to more effective screening and early intervention strategies for dementia.
ACKNOWLEDGMENT: This Project Is Supported By The Innovative Health Initiative Joint Undertaking (IHIJU) Under Grant Agreement No101132356. The JU Receives Support From The European Union's Horizon Europe Research And Innovation Programme. This Work Was Funded By UK Research And Innovation (UKRI) Under The UK Government's Horizon Europe Funding Guarantee[UKRI Reference Number:10083181]. In Switzerland The University Of Geneva Is Funded For PREDICTOM By The Swiss State Secretariat For Education Research And Innovation (SERI- Ref-1131 52304).21S4, Dementia Care Research and Psychosocia
How Sustainable is Machine Learning in Energy Applications? – The Sustainable Machine Learning Balance Sheet
No full textInformation Systems play a central role in the energy sector for achieving climate targets. With increasing digitization and data availability in the energy sector, data-driven machine learning (ML) approaches emerged, showing high potential. So far, research has focused on optimizing ML approaches’ prediction performance. However, this is a one-sided perspective. ML approaches require large computation times and capacities leading to high energy consumption. With the goal of sustainable energy systems, research on ML approaches should be extended to include the application’s energy consumption. ML solutions must be designed in such a way that the resulting savings in energy (and emissions) are greater than the energy consumption caused using the ML solution. To address this need, we develop the Sustainable Machine Learning Balance Sheet as a framework allowing to holistically evaluate and develop sustainable ML solutions which we validated in a case study and through expert interviews
Formal Verification of ROS Based Systems Using a Linear Logic Theorem Prover
No full text93689374In this paper, we propose a novel representation and verification technique for software components in a robotic system using a linear logic theorem prover. Linear logic includes consumable resources together with persistent resources, enabling representing and reasoning of robotic domains. We demonstrate model representation and verification of formal specifications through Robot Operating System (ROS) components. The system model can be either statically extracted by HAROS (a ROS based static analysis framework) or dynamically extracted once all system components are running. After ten years of its first release, ROS has become one of the most popular middlewares among robotic programming frameworks. Even though ROS is very popular among robotic developers, we believe that a framework for easily representing and verifying robotic systems is missing. This paper introduces a new technique for formally representing and verifying robotic systems using a linear logic theorem prover and finally presents a number of illustrations of model representation and safety property checking both statically and dynamically for the robot Kobuki
PhenoTruck®: Mobile Lab for Early Detection of Plant Pests and Pathogens in Fruit Farming and Viticulture
No full textPests that spread as a result of climate change pose an increasing threat to fruit farming and viticulture in Germany. Fraunhofer researchers are working with partners to develop methods for the early identification of infestations in grapevines and in apricot, apple and pear trees so as to enable timely countermeasures to be taken. A mobile lab, the PhenoTruck®, supports rapid and reliable identification of harmful organisms directly on site. The platform provides a highly mobile system for analyzing disease symptoms, combining machine-learning methods, drone-based multispectral sensing, hyperspectral sensing and molecular biological tests
CNN-BASED PARAMETER SELECTION FOR FAST VVC INTRA-PICTURE ENCODING
No full text21092113This paper presents two new methods for fast VVC intra-picture encoding. Both are based on an approach that uses a CNN for block-adaptive parameter estimation. The parameters restrict the multi-type-tree (MTT) partitionings tested by the encoder. The methods aim for an improvement of the approach by further constraints with additional parameters. Adding parameters increases the time required for training data generation exponentially. This raises the question which parameters to add and how. To explore further partitioning restrictions, the first method adds parameters controlling the block sizes the MTT can start from. Although this leads to four parameters, we can exploit that some of their combinations are invalid. To investigate whether testing fewer prediction and transform modes is feasible, the second method adds a single parameter that restricts their number jointly. The paper evaluates hypothetical and actual encoding time reductions for VTM-10.2. The first method outperforms our other and other existing method: The encoding time decreases by 50% with a bit rate increase of 0.7%
Inverse kinematics for full-body self representation in VR-based cognitive rehabilitation
No full text123129Being self-represented through an avatar increases embodiment and the feeling of presence in virtual reality. Nevertheless, currently users in VR are typically represented only by their hands, as not enough tracking data is available for full body self-representation. In our use case of VR-based diagnostics and cognitive rehabilitation of stroke patients, we aim for full body self-representation in order to increase therapeutic effectivity of the treatment. To solve this problem, Inverse Kinematics (IK) can be used for pose estimation, where no tracking data is available. IK allows to minimize the use of hardware and efforts of patients and clinical staff and, at the same time, provides a full-body representation based only on positions of the VR-HMD and users hands as input. In some use cases tracking data from additional, visual full body tracking sensors can be used to estimate the position of the lower body joints. In this study, we evaluate existing IK-based pose estimators; find that VRIK from Final IK is the most suitable approach for the given use case; integrate VRIK in our VR-rehabilitation system; adapt VRIK to meet the use case requirements and conduct subjective tests to validate a significantly increased notion of embodiment and presence through full-body over hands-only self-representation