1,103 research outputs found

    Kinetic Modeling and Numerical Simulation as Tools to Scale Microalgae Cell Membrane Permeabilization by Means of Pulsed Electric Fields (PEF) From Lab to Pilot Plants

    Get PDF
    Pulsed Electric Fields (PEF) is a promising technology for the gentle and energy efficient disruption of microalgae cells such as Chlorella vulgaris. The technology is based on the exposure of cells to a high voltage electric field, which causes the permeabilization of the cell membrane. Due to the dependency of the effective treatment conditions on the specific design of the treatment chamber, it is difficult to compare data obtained in different chambers or at different scales, e.g., lab or pilot scale. This problem can be overcome by the help of numerical simulation since it enables the accessibility to the local treatment conditions (electric field strength, temperature, flow field) inside a treatment chamber. To date, no kinetic models for the cell membrane permeabilization of microalgae are available what makes it difficult to decide if and in what extent local treatment conditions have an impact on the permeabilization. Therefore, a kinetic model for the perforation of microalgae cells of the species Chlorella vulgaris was developed in the present work. The model describes the fraction of perforated cells as a function of the electric field strength, the temperature and the treatment time by using data which were obtained in a milliliter scale batchwise treatment chamber. Thereafter, the model was implemented in a CFD simulation of a pilot-scale continuous treatment chamber with colinear electrode arrangement. The numerical results were compared to experimental measurements of cell permeabilization in a similar continuous treatment chamber. The predicted values and the experimental data agree reasonably well what demonstrates the validity of the proposed model. Therefore, it can be applied to any possible treatment chamber geometry and can be used as a tool for scaling cell permeabilization of microalgae by means of PEF from lab to pilot scale. The present work provides the first contribution showing the applicability of kinetic modeling and numerical simulation for designing PEF processes for the purpose of biorefining microalgae biomass. This can help to develop new processes and to reduce the costs for the development of new treatment chamber designs.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

    Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms

    Full text link
    Social media is often criticized for amplifying toxic discourse and discouraging constructive conversations. But designing social media platforms to promote better conversations is inherently challenging. This paper asks whether simulating social media through a combination of Large Language Models (LLM) and Agent-Based Modeling can help researchers study how different news feed algorithms shape the quality of online conversations. We create realistic personas using data from the American National Election Study to populate simulated social media platforms. Next, we prompt the agents to read and share news articles - and like or comment upon each other's messages - within three platforms that use different news feed algorithms. In the first platform, users see the most liked and commented posts from users whom they follow. In the second, they see posts from all users - even those outside their own network. The third platform employs a novel "bridging" algorithm that highlights posts that are liked by people with opposing political views. We find this bridging algorithm promotes more constructive, non-toxic, conversation across political divides than the other two models. Though further research is needed to evaluate these findings, we argue that LLMs hold considerable potential to improve simulation research on social media and many other complex social settings

    Development of a Continuous Pulsed Electric Field (PEF) Vortex-Flow Chamber for Improved Treatment Homogeneity Based on Hydrodynamic Optimization

    Get PDF
    Pulsed electric fields (PEF) treatment is an effective process for preservation of liquid products in food and biotechnology at reduced temperatures, by causing electroporation. It may contribute to increase retention of heat-labile constituents with similar or enhanced levels of microbial inactivation, compared to thermal processes. However, especially continuous PEF treatments suffer from inhomogeneous treatment conditions. Typically, electric field intensities are highest at the inner wall of the chamber, where the flow velocity of the treated product is lowest. Therefore, inhomogeneities of the electric field within the treatment chamber and associated inhomogeneous temperature fields emerge. For this reason, a specific treatment chamber was designed to obtain more homogeneous flow properties inside the treatment chamber and to reduce local temperature peaks, therefore increasing treatment homogeneity. This was accomplished by a divided inlet into the chamber, consequently generating a swirling flow (vortex). The influence of inlet angles on treatment homogeneity was studied (final values: radial angle α = 61°; axial angle β = 98°), using computational fluid dynamics (CFD). For the final design, the vorticity, i.e., the intensity of the fluid rotation, was the lowest of the investigated values in the first treatment zone (1002.55 1/s), but could be maintained for the longest distance, therefore providing an increased mixing and most homogeneous treatment conditions. The new design was experimentally compared to a conventional co-linear setup, taking into account inactivation efficacy of Microbacterium lacticum as well as retention of heat-sensitive alkaline phosphatase (ALP). Results showed an increase in M. lacticum inactivation (maximum Δlog of 1.8 at pH 7 and 1.1 at pH 4) by the vortex configuration and more homogeneous treatment conditions, as visible by the simulated temperature fields. Therefore, the new setup can contribute to optimize PEF treatment conditions and to further extend PEF applications to currently challenging products

    iMaNGA: mock MaNGA galaxies based on IllustrisTNG and MaStar SSPs. -- III. Stellar metallicity drivers in MaNGA and TNG50

    Full text link
    The iMaNGA project uses a forward-modelling approach to compare the predictions of cosmological simulations with observations from SDSS-IV/MaNGA. We investigate the dependency of age and metallicity radial gradients on galaxy morphology, stellar mass, stellar surface mass density (Σ\Sigma_*), and environment. The key of our analysis is that observational biases affecting the interpretation of MaNGA data are emulated in the theoretical iMaNGA sample. The simulations reproduce the observed global stellar population scaling relations with positive correlations between galaxy mass and age/metallicity quite well and also produce younger stellar populations in late-type in agreement with observations. We do find interesting discrepancies, though, that can inform the physics and further development of the simulations. Ages of spiral galaxies and low-mass ellipticals are overestimated by about 2-4 Gyr. Radial metallicity gradients are steeper in iMaNGA than in MaNGA, a discrepancy most prominent in spiral and lenticular galaxies. Also, the observed steepening of metallicity gradients with increasing galaxy mass is not well matched by the simulations. We find that the theoretical radial profiles of surface mass density Σ\Sigma_* are steeper than in observations except for the most massive galaxies. In both MaNGA and iMaNGA [Z/H] correlates with Σ\Sigma_*, however, the simulations systematically predict lower [Z/H] by almost a factor of 2 at any Σ\Sigma_*. Most interestingly, for galaxies with stellar mass logM10.80M\log M_*\leq 10.80 M_\odot the MaNGA data reveal a positive correlation between galaxy radius and [Z/H] at fixed Σ\Sigma_*, which is not recovered in iMaNGA. Finally, the dependence on environmental density is negligible in both the theoretical iMaNGA and the observed MaNGA data

    Economically optimal timing for crop disease control under uncertainty: an options approach

    Get PDF
    Severe large-scale disease and pest infestations in agricultural regions can cause significant economic damage. Understanding if and when disease control measures should be taken in the presence of risk and uncertainty is a key issue. We develop a framework to examine the economically optimal timing of treatment. The decision to treat should only be undertaken when the benefits exceed the costs by a certain amount and not if they are merely equal to or greater than the costs as standard net-present-value (NPV) analysis suggests. This criterion leads to a reduction in fungicide use. We investigate the effect of the model for disease progress on the value required for immediate treatment by comparing two standard models for disease increase (exponential and logistic growth). Analyses show that the threshold value of benefits required for immediate release of treatment varies significantly with the relative duration of the agricultural season, the intrinsic rate of increase of the disease and the level of uncertainty in disease progression. In comparing the performance of the delay strategy introduced here with the conventional NPV approach, we show how the degree of uncertainty affects the benefits of delaying control

    A JWST investigation into the bar fraction at redshifts 1 < z < 3

    Full text link
    The presence of a stellar bar in a disc galaxy indicates that the galaxy hosts a dynamically settled disc and that bar-driven processes are taking place in shaping the evolution of the galaxy. Studying the cosmic evolution of the bar fraction in disc galaxies is therefore essential to understand galaxy evolution in general. Previous studies have found, using the Hubble Space Telescope (HST), that the bar fraction significantly declines from the local Universe to redshifts near one. Using the first four pointings from the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science Survey (CEERS) and the initial public observations for the Public Release Imaging for Extragalactic Research (PRIMER), we extend the studies on the bar fraction in disc galaxies to redshifts 1z31 \leq z \leq 3, i.e., for the first time beyond redshift two. We only use galaxies that are also present in the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) on the Extended Groth Strip (EGS) and Ultra Deep Survey (UDS) HST observations. An optimised sample of 768 close-to-face-on galaxies is visually classified to find the fraction of bars in disc galaxies in two redshift bins: 1z21 \leq z \leq 2 and 2<z32 < z \leq 3. The bar fraction decreases from 18.99.4+9.7\sim 18.9^{+ 9.7}_{- 9.4} per cent to 6.65.9+7.1\sim 6.6^{+ 7.1}_{- 5.9} per cent (from the lower to the higher redshift bin), but is 34\sim 3 - 4 times greater than the bar fraction found in previous studies using bluer HST filters. Our results show that bar-driven evolution commences at early cosmic times and that dynamically settled discs are already present at a lookback time of 11\sim 11 Gyrs.Comment: Submitted to MNRAS. 15 pages, 10 figures. Figure 6 and 7 summarises the main result

    Collage 2019

    Get PDF
    An exciting highlight each season, Collage is the signature production of the School of Music and a major fundraising event for supporting scholarships for music students. This special performance features over 200 student and faculty performers and includes jazz, orchestra, choir, band, percussion, and opera selections for soloists, chamber groups, and ensembles. Special lighting effects and stage design combine with the diverse and exciting program presented as rapid-fire, flowing vignettes to create a truly unique performance.https://digitalcommons.kennesaw.edu/musicprograms/2161/thumbnail.jp

    current evidence and programmatic considerations

    Get PDF
    Funding Information: We are thankful to Ann Prentice for her critical review of the section ?Concerns in populations with low calcium intake.? The convenings of the Calcium Task Force and the development of this paper and its open access were supported by funding from The Children's Investment Fund Foundation to the Nutrition Science Program of the New York Academy of Sciences. Publisher Copyright: © 2022 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of New York Academy of Sciences.Most low- and middle-income countries present suboptimal intakes of calcium during pregnancy and high rates of mortality due to maternal hypertensive disorders. Calcium supplementation during pregnancy is known to reduce the risk of these disorders and associated complications, including preeclampsia, maternal morbidity, and preterm birth, and is, therefore, a recommended intervention for pregnant women in populations with low dietary calcium intake (e.g., where ≥25% of individuals in the population have intakes less than 800 mg calcium/day). However, this intervention is not widely implemented in part due to cost and logistical issues related to the large dose and burdensome dosing schedule (three to four 500-mg doses/day). WHO recommends 1.5–2 g/day but limited evidence suggests that less than 1 g/day may be sufficient and ongoing trials with low-dose calcium supplementation (500 mg/day) may point a path toward simplifying supplementation regimens. Calcium carbonate is likely to be the most cost-effective choice, and it is not necessary to counsel women to take calcium supplements separately from iron-containing supplements. In populations at highest risk for preeclampsia, a combination of calcium supplementation and food-based approaches, such as food fortification with calcium, may be required to improve calcium intakes before pregnancy and in early gestation.publishersversionpublishe

    Structural Bootstrapping - A Novel, Generative Mechanism for Faster and More Efficient Acquisition of Action-Knowledge

    Get PDF
    eISSN: 1943-0612Humans, but also robots, learn to improve their behavior. Without existing knowledge, learning either needs to be explorative and, thus, slow or-to be more efficient-it needs to rely on supervision, which may not always be available. However, once some knowledge base exists an agent can make use of it to improve learning efficiency and speed. This happens for our children at the age of around three when they very quickly begin to assimilate new information by making guided guesses how this fits to their prior knowledge. This is a very efficient generative learning mechanism in the sense that the existing knowledge is generalized into as-yet unexplored, novel domains. So far generative learning has not been employed for robots and robot learning remains to be a slow and tedious process. The goal of the current study is to devise for the first time a general framework for a generative process that will improve learning and which can be applied at all different levels of the robot's cognitive architecture. To this end, we introduce the concept of structural bootstrapping-borrowed and modified from child language acquisition-to define a probabilistic process that uses existing knowledge together with new observations to supplement our robot's data-base with missing information about planning-, object-, as well as, action-relevant entities. In a kitchen scenario, we use the example of making batter by pouring and mixing two components and show that the agent can efficiently acquire new knowledge about planning operators, objects as well as required motor pattern for stirring by structural bootstrapping. Some benchmarks are shown, too, that demonstrate how structural bootstrapping improves performanceTaikomosios informatikos katedraVytauto Didžiojo universiteta
    corecore