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Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.
In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.
This review discusses picosecond ultrasonics experiments using ultrashort hard x-ray probe pulses to extract the transient strain response of laser-excited nanoscopic structures from Bragg-peak shifts. This method provides direct, layer-specific, and quantitative information on the picosecond strain response for structures down to few-nm thickness. We model the transient strain using the elastic wave equation and express the driving stress using Gruneisen parameters stating that the laser-induced stress is proportional to energy density changes in the microscopic subsystems of the solid, i.e., electrons, phonons and spins. The laser-driven strain response can thus serve as an ultrafast proxy for local energy-density and temperature changes, but we emphasize the importance of the nanoscale morphology for an accurate interpretation due to the Poisson effect. The presented experimental use cases encompass ultrathin and opaque metal-heterostructures, continuous and granular nanolayers as well as negative thermal expansion materials, that each pose a challenge to established all-optical techniques.
Physical fitness of primary school children differs depending on their timing of school enrollment
(2023)
Previous research has shown that children who were enrolled to school according to the legal key date (i.e., keyage children, between eight and nine years in third grade) exhibited a linear physical fitness development in the ninth year of life. In contrast, children who were enrolled with a delay (i.e., older-than-keyage children [OTK], between nine and ten years in third grade) exhibited a lower physical fitness compared to what would be expected for their age. In these studies, cross-sectional age differences within third grade and timing of school enrollment were confounded. The present study investigated the longitudinal development of keyage and OTK children from third to fifth grade. This design also afforded a comparison of the two groups at the same average chronological age, that is a dissociation of the effects of timing of school enrollment and age. We tested six physical fitness components: cardiorespiratory endurance, coordination, speed, power of lower and upper limbs, and static balance. 1502 children (i.e., 1206 keyage and 296 OTK children) from 35 schools were tested in third, fourth, and fifth grade. Except for cardiorespiratory endurance, both groups developed from third to fourth and from fourth to fifth grade and keyage children outperformed OTK children at the average ages of 9.5 or 10.5 years. For cardiorespiratory endurance, there was no significant gain from fourth to fifth grade and keyage and OTK children did not differ significantly at 10.5 years of age. One reason for a delayed school enrollment could be that a child is (or is perceived as) biologically younger than their chronological age at the school entry examination, implying a negative correlation between chronological and biological age for OTK children. Indeed, a simple reflection of chronological age brought the developmental rate of the chronologically youngest OTK children in line with the developmental rate observed for keyage children, but did not eliminate all differences. The mapping of chronological and biological age of OTK children and other possible reasons for lower physical fitness of OTK children remain a task for future research.
Efficient Removal of Tetracycline and Bisphenol A from Water with a New Hybrid Clay/TiO2 Composite
(2023)
New TiO2 hybrid composites were prepared fromkaolinclay, predried and carbonized biomass, and titanium tetraisopropoxideand explored for tetracycline (TET) and bisphenol A (BPA) removalfrom water. Overall, the removal rate is 84% for TET and 51% for BPA.The maximum adsorption capacities (q (m))are 30 and 23 mg/g for TET and BPA, respectively. These capacitiesare far greater than those obtained for unmodified TiO2. Increasing the ionic strength of the solution does not change theadsorption capacity of the adsorbent. pH changes only slightly changeBPA adsorption, while a pH > 7 significantly reduces the adsorptionof TET on the material. The Brouers-Sotolongo fractal modelbest describes the kinetic data for both TET and BPA adsorption, predictingthat the adsorption process occurs via a complex mechanism involvingvarious forces of attraction. Temkin and Freundlich isotherms, whichbest fit the equilibrium adsorption data for TET and BPA, respectively,suggest that adsorption sites are heterogeneous in nature. Overall,the composite materials are much more effective for TET removal fromaqueous solution than for BPA. This phenomenon is assigned to a differencein the TET/adsorbent interactions vs the BPA/adsorbent interactions:the decisive factor appears to be favorable electrostatic interactionsfor TET yielding a more effective TET removal.
The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware-we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.
Understanding the origin of inefficient photocurrent generation in organic solar cells with low energy offset remains key to realizing high-performance donor-acceptor systems. Here, we probe the origin of field-dependent free-charge generation and photoluminescence in wnon-fullereneacceptor (NFA)-based organic solar cells using the polymer PM6 and the NFA Y5-a non-halogenated sibling to Y6, with a smaller energetic offset to PM6. By performing time-delayed collection field (TDCF) measurements on a variety of samples with different electron transport layers and active layer thickness, we show that the fill factor and photocurrent are limited by field-dependent free charge generation in the bulk of the blend. We also introduce a new method of TDCF called m-TDCF to prove the absence of artifacts from non-geminate recombination of photogenerated and dark charge carriers near the electrodes. We then correlate free charge generation with steady-state photoluminescence intensity and find perfect anticorrelation between these two properties. Through this, we conclude that photocurrent generation in this low-offset system is entirely controlled by the field-dependent dissociation of local excitons into charge-transfer states. (c) 2023 Author(s).
Ore precipitation in porphyry copper systems is generally characterized by metal zoning (Cu-Mo to Zn-Pb-Ag), which is suggested to be variably related to solubility decreases during fluid cooling, fluid-rock interactions, partitioning during fluid phase separation and mixing with external fluids. Here, we present new advances of a numerical process model by considering published constraints on the temperature- and salinity-dependent solubility of Cu, Pb and Zn in the ore fluid. We quantitatively investigate the roles of vapor-brine separation, halite saturation, initial metal contents, fluid mixing and remobilization as first-order controls of the physical hydrology on ore formation. The results show that the magmatic vapor and brine phases ascend with different residence times but as miscible fluid mixtures, with salinity increases generating metal-undersaturated bulk fluids. The release rates of magmatic fluids affect the location of the thermohaline fronts, leading to contrasting mechanisms for ore precipitation: higher rates result in halite saturation without significant metal zoning, lower rates produce zoned ore shells due to mixing with meteoric water. Varying metal contents can affect the order of the final metal precipitation sequence. Redissolution of precipitated metals results in zoned ore shell patterns in more peripheral locations and also decouples halite saturation from ore precipitation.
We present a Reduced Order Model (ROM) which exploits recent developments in Physics Informed Neural Networks (PINNs) for solving inverse problems for the Navier-Stokes equations (NSE). In the proposed approach, the presence of simulated data for the fluid dynamics fields is assumed. A POD-Galerkin ROM is then constructed by applying POD on the snapshots matrices of the fluid fields and performing a Galerkin projection of the NSE (or the modified equations in case of turbulence modeling) onto the POD reduced basis. A POD-Galerkin PINN ROM is then derived by introducing deep neural networks which approximate the reduced outputs with the input being time and/or parameters of the model. The neural networks incorporate the physical equations (the POD-Galerkin reduced equations) into their structure as part of the loss function. Using this approach, the reduced model is able to approximate unknown parameters such as physical constants or the boundary conditions. A demonstration of the applicability of the proposed ROM is illustrated by three cases which are the steady flow around a backward step, the flow around a circular cylinder and the unsteady turbulent flow around a surface mounted cubic obstacle.
The oil palm (Elaeis guineensis Jacq.) produces a large amount of oil from the fruit. However, increasing the oil production in this fruit is still challenging. A recent study has shown that starch metabolism is essential for oil synthesis in fruit-producing species. Therefore, the transcriptomic analysis by RNA-seq was performed to observe gene expression alteration related to starch metabolism genes throughout the maturity stages of oil palm fruit with different oil yields. Gene expression profiles were examined with three different oil yields group (low, medium, and high) at six fruit development phases (4, 8, 12, 16, 20, and 22 weeks after pollination). We successfully identified and analyzed differentially expressed genes in oil palm mesocarps during development. The results showed that the transcriptome profile for each developmental phase was unique. Sucrose flux to the mesocarp tissue, rapid starch turnover, and high glycolytic activity have been identified as critical factors for oil production in oil palms. For starch metabolism and the glycolytic pathway, we identified specific gene expressions of enzyme isoforms (isozymes) that correlated with oil production, which may determine the oil content. This study provides valuable information for creating new high-oil-yielding palm varieties via breeding programs or genome editing approaches.