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Pain and discomfort in children with gastrostomy tubes – In the context of hematopoietic stem cell transplantation
Full text linkBackground: In children with malignant and severe non-malignant disorders undergoing hematopoietic stem cell transplantation (HSCT), treatment related pain and discomfort are common. Food consumption may become troublesome, making the use of a gastrostomy tube (G-tube) necessary and resulting in complications, why the purpose was to explore pain and discomfort during the transplantation and post-transplantation time. Methods: This was a mixed methods study where data were collected along the child\u27s total health-care process between 2018 and 2021. Questions with fixed answer options were used, simultaneously, semi-structured interviews were performed. In total, sixteen families participated. Descriptive statistics and content analysis were used to describe analysed data. Findings: Intense pain was common during the post-surgery phase, especially in conjunction with G-tube care, which is why the children needed support to manage the situation. After the post-surgery phase when the skin has healed, most of the children experienced minor to no pain or bodily discomfort, why the G-tube became a well-functioning and supportive tool in daily life. Conclusions: This study describes variations in and experiences of pain and bodily discomfort in conjunction with G-tube insertion in a unique sample of children who had undergone HSCT. In conclusion, the children\u27s comfort in daily life after the post-surgery phase seemed to be only marginally affected by G-tube insertion. Children with severe non-malignant disorders seemed to experience a higher frequency and intensity of pain and bodily discomfort due to the G-tube than children with malignant disorders. Practice implications: The paediatric care team need competence in assessing G-tube related pain and awareness that experiences may differ depending on the child\u27s disorder
Microcapsule functionalization enables rate-determining release from cellulose nonwovens for long-term performance
Full text linkFunctional textiles is a rapidly growing product segment in which sustained release of actives often plays a key role. Failure to sustain the release results in costs due to premature loss of functionality and resource inefficiency. Conventional application methods such as impregnation lead to an excessive and uncontrolled release, which - for biocidal actives - results in environmental pollution. In this study, microcapsules are presented as a means of extending the release from textile materials. The hydrophobic model substance pyrene is encapsulated in poly(d,l-lactide-co-glycolide) microcapsules which subsequently are loaded into cellulose nonwovens using a solution blowing technique. The release of encapsulated pyrene is compared to that of two conventional functionalization methods: surface and bulk impregnation. The apparent diffusion coefficient is 100 times lower for encapsulated pyrene compared to impregnated pyrene. This clearly demonstrates the rate-limiting barrier properties added by the microcapsules, extending the potential functionality from hours to weeks
Influence of the design constraints on the thickness optimization of glass panes to achieve lightweight insulating glass units in cruise ships
Full text linkThe increasing complexity and size in cruise ships demands for lightweight structures and practical but accurate design methods. Conventionally, the focus has been on the steel parts of the ship, as they make most of its weight. However, the proportions of other materials are increasing. Therefore, this study attempts to provide better understanding how one could reach the lightweight designs of insulating glass units (IGUs) in ships. These are windows where at least two glass panes are separated by a hermetically sealed cavity. They are thin-walled structures that benefit not only from the geometrically nonlinear behavior, but also from the load sharing. Considering these effects, their behavior is studied using the nonlinear Finite Element Method and Particle Swarm Optimization. Different design criteria are imposed on the thickness determination of the glass panes with different shapes. Rectangular, triangular, and circular shapes are considered. The results show that the triangular shapes have the least weight for a given area when the deflection criterion is the dominating one. When maximum principal stress is the thickness defining criterion, the shapes perform almost equally well. The\ua0ratio between the pane thicknesses had the most influence on the behavior of the IGU. As it increases, i.e., one pane is significantly thicker than the other, the load sharing percentage drops, but it provides the most lightweight solution. Closer it is to 1, more equally the structural stresses are divided between the panes, i.e., redundancy is achieved. Finally, it is possible to establish a simple but effective method for the thickness determination of these IGUs using the results of this study. However, more work is required, including numerical analysis and experimental testing
Shape analysis via gradient flows on diffeomorphism groups
Full text linkWe study a Riemannian gradient flow on Sobolev diffeomorphisms for the problem of image registration. The energy functional quantifies the effect of transforming a template to a target, while also penalizing non-isometric deformations. The main result is well-posedness of the flow. We also give a geometric description of the gradient in terms of the momentum map
The Effects of Natural Sounds and Proxemic Distances on the Perception of a Noisy Domestic Flying Robot
No full textWhen flying robots are used in close-range interaction with humans, the noise they generate, also called consequential sound, is a critical parameter for user acceptance. We conjecture that there is a benefit in adding natural sounds to noisy domestic drones. To test our hypothesis experimentally, we carried out a mixed-methods research study (N=56) on reported user perception of a sonified domestic flying robot with three sound conditions at three distances. The natural sounds studied were respectively added to the robot’s inherent noises during flying; namely a birdsong and a rain sound, plus a control condition of no added sound. The distances studied were set according to proxemics; namely near, middle, and far. Our results show that adding birdsong or rain sound affects the participants’ perceptions, and the proxemic distances play a nonnegligible role. For instance, we found that participants liked the bird condition the most when the drone was at far, while they disliked the same sound the most when at near. We also found that participants’ perceptions strongly depended on their associations and interpretations deriving from previous experience. We derived six concrete design recommendations
A Room-Temperature Spin-Valve with van der Waals Ferromagnet Fe5GeTe2/Graphene Heterostructure
Full text linkThe discovery of van der Waals (vdW) magnets opened a new paradigm for condensed matter physics and spintronic technologies. However, the operations of active spintronic devices with vdW ferromagnets are limited to cryogenic temperatures, inhibiting their broader practical applications. Here, the robust room-temperature operation of lateral spin-valve devices using the vdW itinerant ferromagnet Fe5GeTe2 in heterostructures with graphene is demonstrated. The room-temperature spintronic properties of Fe5GeTe2 are measured at the interface with graphene with a negative spin polarization. Lateral spin-valve and spin-precession measurements provide unique insights by probing the Fe5GeTe2/graphene interface spintronic properties via spin-dynamics measurements, revealing multidirectional spin polarization. Density functional theory calculations in conjunction with Monte Carlo simulations reveal significantly canted Fe magnetic moments in Fe5GeTe2 along with the presence of negative spin polarization at the Fe5GeTe2/graphene interface. These findings open opportunities for vdW interface design and applications of vdW-magnet-based spintronic devices at ambient temperatures
Effects of a Synbiotic on Plasma Immune Activity Markers and Short-Chain Fatty Acids in Children and Adults with ADHD—A Randomized Controlled Trial
Full text linkSynbiotic 2000, a pre + probiotic, reduced comorbid autistic traits and emotion dysregulation in attention deficit hyperactivity disorder (ADHD) patients. Immune activity and bacteria-derived short-chain fatty acids (SCFAs) are microbiota–gut–brain axis mediators. The aim was to investigate Synbiotic 2000 effects on plasma levels of immune activity markers and SCFAs in children and adults with ADHD. ADHD patients (n = 182) completed the 9-week intervention with Synbiotic 2000 or placebo and 156 provided blood samples. Healthy adult controls (n = 57) provided baseline samples. At baseline, adults with ADHD had higher pro-inflammatory sICAM-1 and sVCAM-1 and lower SCFA levels than controls. Children with ADHD had higher baseline sICAM-1, sVCAM-1, IL-12/IL-23p40, IL-2Rα, and lower formic, acetic, and propionic acid levels than adults with ADHD. sICAM-1, sVCAM-1, and propionic acid levels were more abnormal in children on medication. Synbiotic 2000, compared to placebo, reduced IL-12/IL-23p40 and sICAM-1 and increased propionic acid levels in children on medication. SCFAs correlated negatively with sICAM-1 and sVCAM-1. Preliminary human aortic smooth-muscle-cell experiments indicated that SCFAs protected against IL-1β-induced ICAM-1 expression. These findings suggest that treatment with Synbiotic 2000 reduces IL12/IL-23p40 and sICAM-1 and increases propionic acid levels in children with ADHD. Propionic acid, together with formic and acetic acid, may contribute to the lowering of the higher-than-normal sICAM-1 levels
Prediction-Uncertainty-Aware Threat Detection for ADAS: A Case Study on Lane-Keeping Assistance
No full textAdvanced driver assistance systems typically support the driver in cases where the driver is likely to fail the driving task. The challenge, from a system perspective, is to accurately detect those cases. Recently, machine learning-based prediction models that are able to estimate the prediction uncertainty in real-time have successfully been introduced for this purpose. However, very little effort has been made on using the prediction uncertainty in the decision-making logic to improve the system\u27s robustness, especially in cases where the input data is affected by noise or anomalies that are not presented in the training data. In this work, four threat-detection methods using uncertainty estimates are proposed and evaluated using a real-world data set. The methods use different strategies for leveraging uncertainty information, where the goal is to ensure that the intervention decision is based on trustworthy predictions. The threat-detection methods\u27 performances are evaluated, using five different learning-based prediction models, in the context of a lane-keeping assistance application
Inverse molecular design and parameter optimization with H\ufcckel theory using automatic differentiation
Full text linkSemiempirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semiempirical model still in widespread use in chemistry is H\ufcckel\u27s π-electron molecular orbital theory. In this work, we implemented a H\ufcckel program using differentiable programming with the JAX framework based on limited modifications of a pre-existing NumPy version. The auto-differentiable H\ufcckel code enabled efficient gradient-based optimization of model parameters tuned for excitation energies and molecular polarizabilities, respectively, based on as few as 100 data points from density functional theory simulations. In particular, the facile computation of the polarizability, a second-order derivative, via auto-differentiation shows the potential of differentiable programming to bypass the need for numeric differentiation or derivation of analytical expressions. Finally, we employ gradient-based optimization of atom identity for inverse design of organic electronic materials with targeted orbital energy gaps and polarizabilities. Optimized structures are obtained after as little as 15 iterations using standard gradient-based optimization algorithms