Numerical study on particle behavior in a Y-junction mixer for supercritical water hydrolysis.

In the continuous-type supercritical water hydrolysis process, rapid mixing of supercritical and subcritical streams is important to maximize yield and minimize degradation from over-reaction. This work investigated the particle behavior in a Y-junction mixer using large eddy simulation coupled with a discrete phase model, aiming to optimize the supercritical hydrolysis process for biomass conversion. A series of numerical simulations analyzed the influence of the mixer's orientation, flow directions, and flow rates on effective mixing and backflow prevention. The results demonstrated that the most effective mixing occurred in a vertically oriented Y-junction mixer with an upward-directed supercritical water inlet, aligning the momentum direction of natural and forced convection effectively. Consequently, over 80% of particles reached the temperatures close to the mixing temperature of supercritical and subcritical water within the Y-junction mixing zone, indicating enhanced mixing effectiveness and potential for efficient hydrolysis. This configuration also minimized backflow.

The Heritability of Human Connectomes: a Causal Modeling Analysis.

The heritability of human connectomes is crucial for understanding the influence of genetic and environmental factors on variability in connectomes, and their implications for behavior and disease. However, current methods for studying heritability assume an associational rather than a causal effect, or rely on strong distributional assumptions that may not be appropriate for complex, high-dimensional connectomes. To address these limitations, we propose two solutions: first, we formalize heritability as a problem in causal inference, and identify measured covariates to control for unmeasured confounding, allowing us to make causal claims. Second, we leverage statistical models that capture the underlying structure and dependence within connectomes, enabling us to define different notions of connectome heritability by removing common structures such as scaling of edge weights between connectomes. We then develop a non-parametric test to detect whether causal heritability exists after taking principled steps to adjust for these commonalities, and apply it to diffusion connectomes estimated from the Human Connectome Project. Our findings reveal that heritability can still be detected even after adjusting for potential confounding like neuroanatomy, age, and sex. However, once we address for rescaling between connectomes, our causal tests are no longer significant. These results suggest that previous conclusions on connectome heritability may be driven by rescaling factors. Together, our manuscript highlights the importance for future works to continue to develop data-driven heritability models which faithfully reflect potential confounders and network structure.

Eight-week supplementation of Aronia berry extract promoted the glutathione defence system against acute aerobic exercise-induced oxidative load immediately and 30 min post-exercise in healthy adults: a double-blind, randomised controlled trial.

BACKGROUND: Food antioxidants have received prompt attention for controlling oxidative stress encountered in daily life. This study aimed to examine the protective effects of Aronia berry extract (ABE) supplementation on acute aerobic exercise (AAE)-induced oxidative stress in healthy subjects. METHODS: We assessed a battery of antioxidant defence and oxidative stress parameters at pre-exercise, immediately post-exercise and 30 min post-exercise in healthy middle-aged adults with habitually low intakes of fruit and vegetables in an 8-week, double-blind, randomised, controlled clinical trial with two arms (n = 70). The AAE challenge model, characterised as a treadmill exercise for 30 min at 60% VO2 maximum, was applied to load oxidative stress at the end of the study. Pearson's correlation analysis assessed the association between the changes in antioxidant defence capacities and oxidative stress levels. RESULTS: The time-course-dependent oxidative stress was well observed in the placebo group regarding the glutathione peroxidase (GPx) activity and the reduced glutathione (GSH) availability for antioxidant defence and erythrocyte malondialdehyde, interleukin-6 and lactate levels for oxidative damage. Meanwhile, the ABE supplementation effectively strengthened the glutathione defence system by increasing GSH availability and GPx activity immediately post-exercise and 30 min post-exercise. In addition, the scatter plot and linear regression analysis revealed strong negative correlations of GSH availability with oxidised low-density lipoprotein and plasma malonaldehyde levels. CONCLUSION: These findings suggest that daily supplementation of 300 mg ABE might help boost GSH levels and an adaptive antioxidant enzyme defence system of erythrocytes in healthy adults with habitually low fruit and vegetable intakes.

Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data.

The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple factors, including climate change, affect the energy demands of local, national, and global power grids. Therefore, effective analysis of multivariable data is required for the accurate estimation of energy demand and consumption. In this context, some studies have suggested that LSTM and CNN models can be used to model electricity demand accurately. However, existing works have utilized training based on either electricity loads and weather observations or national metrics e.g., gross domestic product, imports, and exports. This binary segregation has degraded forecasting performance. To resolve this shortcoming, we propose a CNN-LSTM model based on a multivariable augmentation approach. Based on previous studies, we adopt 1D convolution and pooling to extract undiscovered features from temporal sequences. LSTM outperforms RNN on vanishing gradient problems while retaining its benefits regarding time-series variables. The proposed model exhibits near-perfect forecasting of electricity consumption, outperforming existing models. Further, state-level analysis and training are performed, demonstrating the utility of the proposed methodology in forecasting regional energy consumption. The proposed model outperforms other models in most areas.

Control of Particle Size in Flame Spray Pyrolysis of Tb-doped Y2O3 for Bio-Imaging.

Recently, the use of oxide-based nanomaterials for bio-imaging has received great attention owing to their remarkable stabilities as compared to those of conventional organic dyes. Therefore, the development of scalable methods for highly luminescent oxide materials with fine control of size has become crucial. In this study, we suggested modified flame spray pyrolysis (FSP) as a scalable method to produce a green-light emitting phosphor-Tb-doped Y2O3-in the nanometer size range. In our FSP method, an alkali salt (NaNO3) was found to be highly effective as a size-controlling agent when it is simply mixed with other metal nitrate precursors. The FSP of the mixture solution resulted in oxide composites of Y2O3:Tb3+ and NaxO. However, the sodium by-product was easily removed by washing with water. This salt-assisted FSP produced nano-sized and well-dispersed Y2O3:Tb3+ nanoparticles; their crystallinity and luminescence were higher than those of the bulk product made without the addition of the alkali salt. The nanoparticle surface was further coated with silica for biocompatibility and functionalized with amino groups for the attachment of biological molecules.

Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.

Roles of gel polymer electrolytes for high-power activated carbon supercapacitors: ion reservoir and binder-like effects.

Ion reservoir and binder-like effects of gel polymer electrolytes (GPEs) are suggested for working mechanisms to enhance rate capability and cycling stability of activated carbon (AC) supercapacitors (SCs) even at 3.4 V. Analysis on kinetics from cyclic voltammetry, electrochemical reactions through in situ Fourier-transform infrared spectroscopy, and differential information of galvanostatic curves reveals that the increased rate-capability is derived dominantly by an improved non-faradaic process by the ion reservoir effect of GPEs in the AC. Although the designed GPEs induce slightly higher bulk and diffusion resistance at the incipient stage, the GPEs play a binder-like function to suppress detachment of AC particles and aggravation of impedance parameters during cycling at 3.4 V.

Connectal coding: discovering the structures linking cognitive phenotypes to individual histories.

Cognitive phenotypes characterize our memories, beliefs, skills, and preferences, and arise from our ancestral, developmental, and experiential histories. These histories are written into our brain structure through the building and modification of various brain circuits. Connectal coding, by way of analogy with neural coding, is the art, study, and practice of identifying the network structures that link cognitive phenomena to individual histories. We propose a formal statistical framework for connectal coding and demonstrate its utility in several applications spanning experimental modalities and phylogeny.

Interpolations of groundwater table elevation in dissected uplands.

The variable elevation of the groundwater table in the St. Louis area was estimated using multiple linear regression (MLR), ordinary kriging, and cokriging as part of a regional program seeking to assess liquefaction potential. Surface water features were used to determine the minimum water table for MLR and supplement the principal variables for ordinary kriging and cokriging. By evaluating the known depth to the water and the minimum water table elevation, the MLR analysis approximates the groundwater elevation for a contiguous hydrologic system. Ordinary kriging and cokriging estimate values in unsampled areas by calculating the spatial relationships between the unsampled and sampled locations. In this study, ordinary kriging did not incorporate topographic variations as an independent variable, while cokriging included topography as a supporting covariable. Cross validation suggests that cokriging provides a more reliable estimate at known data points with less uncertainty than the other methods. Profiles extending through the dissected uplands terrain suggest that: (1) the groundwater table generated by MLR mimics the ground surface and elicits a exaggerated interpolation of groundwater elevation; (2) the groundwater table estimated by ordinary kriging tends to ignore local topography and exhibits oversmoothing of the actual undulations in the water table; and (3) cokriging appears to give the realistic water surface, which rises and falls in proportion to the overlying topography. The authors concluded that cokriging provided the most realistic estimate of the groundwater surface, which is the key variable in assessing soil liquefaction potential in unconsolidated sediments.