Miracle-dz/UniFAR-FTUs huggingface dataset:
An experimental study on stationary history effects in high-frequency Stokes flows

We report results of a series of detailed experiments designed to unveil the dynamics of a particle of radius $a$ moving in high-frequency, low-Reynolds-number oscillatory flow. The fundamental parameters in the problem are the Strouhal ($\hbox{\it Sl}$) and the particle Reynolds numbers ($\hbox{\it Re}_p$), as well as the fluid-to-particle density ratio $\alpha$. The experiments were designed to cover a range of $\hbox{\it Sl} \hbox{\it Re}_p$ from 0.015 to 5 while keeping $\hbox{\it Re}_p < 0.5$ and $\hbox{\it Sl} > 1$. The primary objective of the experiments is to investigate stationary history effects associated with the Basset drag, which are maximized when the viscous time scale $a^2/\nu$ is of the same order of the flow time scale $9/\Omega$, where $9$ is a geometrical factor for the sphere, $\nu$ is the kinematic viscosity and $\Omega$ is the angular frequency of the background flow. The theoretically determined behaviour of stationary history effects is confirmed unequivocally by the experiments, which also validate the fractional derivative behaviour (of order $1/2$) of the history drag for the range of parameters under study.
Towards Gamified Conversational Agents for Self-Regulated Learning in Digital Education

Formal education like higher education oftentimes emphasized on strict non-digital setting. This approach can lead to issues during stressful times (e.g., Covid crisis) or when learners’ needs in general are not considered. Moreover, these times highlighted how important self-regulated learning is and how much this capability is lacking in our educational system. To address these issues, we follow an Action Design Research approach and develop a gamified conversational agent (CA) that considers the learners’ needs. We present our CA and conduct a first small-scale evaluation following a mixed-method approach. First results show that students universally liked a CA for self-regulated digital learning and many enjoyed the gamified experience which helped students to be motivated to learn. As next steps we will develop the next iteration of our CA and conduct a long-term field test at a university.
Energy dependence of signalling dynamics and robustness in bacterial two component systems

One of the best known ways bacteria cells understand and respond to the environment are through Two-Component Systems (TCS). These signalling systems are highly diverse in function and can detect a range of physical stimuli including molecular concentrations and temperature, with a range of responses including chemotaxis and anaerobic energy production. TCS exhibit a range of different molecular structures and energy costs, and multiple types co-exist in the same cell. TCSs that incur relatively high energy cost are abundant in biology, despite strong evolutionary pressure to efficiently spend energy. We are motivated to discern what benefits, if any, the more energetically expensive variants had for a cell. We seek to answer this question by modelling energy flow through two variants of TCS. This was accomplished using bond graphs, a physics-based modelling framework that accurately models energy transfer through different physical domains. Our analysis demonstrates that energy availability can affect a cell’s signal sensitivity, noise filtering effectiveness, and the stimulus level where cell response is maximal. We also found that these properties are determined not by the molecular parameters themselves, but the reaction rate parameters that govern the reaction systems as a whole. This suggests possible connections between the molecular structure and evolutionary purpose of any two-component system. This opens the door to new synthetic circuit design in systems biology, and we propose new hypotheses about this link between structure and purpose that could be experimentally verified. Author summary Two-component systems are the main way many bacteria sense and respond to their environment. They exist in such well-studied bacteria as E. coli where they have been shown to detect a range of stimuli including nutrients, temperature, acidity, and pressure. Two-component systems are ubiquitous in bacteria yet have a deceptively simple structure. Knowing how they operate and the purpose of variations in signalling structure is helpful to our understanding of cellular biology and the design of synthetic biological circuits. Critical unanswered questions remain about the energy usage and functional benefits of these systems. We sought to improve our understanding of two-component systems by applying a physics-based modelling framework. We found that tracking energy flow through the cell reveals new energy-dependent behaviour in signalling sensitivity, noise filtering, and maximal cell response. We also found that these properties are not strictly dependent on the molecular properties themselves, but from the configuration of the reaction system as a whole.
Impact of brain parcellation on prediction performance in models of cognition and demographics

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with 5 different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, Executive Function, Self-regulation, Language, Encoding and Sequence Processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme proved superior at predictive modelling across all cognitive domains and demographics. In addition, although parcellation schemes impacted the global organisation of each connectivity type, this difference could not account for the out-of-sample prediction performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by higher resolution of parcellation likely disrupts model generalisability.
Comparison of Preshaping Profilers for Reducing Residual Vibrations After Rest-to-Rest Maneuvers

Generally, spacecraft with large flexible structures have multiple flexible modes with large effective masses and various mode shapes; therefore, the presence of uncertain high-order flexible modes should be considered in high-accuracy controller design. However, most researchers have proposed input preshaping algorithms that are valid only for a few flexible modes. Consequently, when these algorithms are applied to a flexible spacecraft having uncertain high-order flexible modes, residual vibrations might not be reduced sufficiently. This paper presents a smooth feedforward control algorithm for rest-to-rest maneuvers that significantly reduces residual vibrations at the endpoint of a maneuver. The new preshaping profiler is based on a filtering technique that combines a pair of sinc functions. The new profiler is compared to several conventional preshaping profilers. In addition, residual vibrations at the endpoint of maneuvers in the presence of unknown higher modes are found to be much smaller than th.
The dominance of coinfecting parasites’ indirect effects on host traits

Indirect genetic effects (IGEs) exist when there is heritable variation in one species’ ability to alter a second species’ traits. For example, parasites can evolve disparate strategies to manipulate host immune response, whether by evading detection or suppressing immunity. A complication arises during coinfection, when two or more parasite genotypes may try to impose distinct IGEs on the same host trait: which parasite’s IGE will be dominant? Here, we apply the notion of dominance to IGEs during coinfection. Using a mathematical model we show that the dominance of IGEs can alter the evolutionary dynamics of parasites. We consider a resident parasite population receiving rare immigrants with a different immune manipulation trait. These immigrants’ relative fitness depends on resident prevalence (e.g., the probability immigrants are alone in a host, or coinfecting with a native), and the dominance of the immigrant’s IGE on host immunity. Next, we show experimentally that the cestode Schistocephalus solidus exerts an IGE on a host immune trait: parasite antigens from different populations produced different intensities of fibrosis. We then evaluated IGE dominance, finding evidence for overdominance (coinjected antigens induced an even stronger host immune response) which would be detrimental to immigrants when resident prevalence is high. This combination of experimental and modeling results shows that parasites do exhibit IGEs on host traits, and that the dominance of these IGEs during coinfection can substantially alter parasite evolution.
Maximum Entropy Density Estimation from Fractional Moments

Abstract A procedure for the estimation of probability density functions of positive random variables by its fractional moments, is presented. When all the available information is provided by population fractional moments a criterion of choosing fractional moments themselves is detected. When only a sample is known, Jaynes' maximum entropy procedure and the Akaike's estimation procedure are joined together for determining respectively, what and how many sample fractional moments have to be used in the estimation of the density. Some numerical experiments are provided.
Advances in Markov chain Monte Carlo methods

Probability distributions over many variables occur frequently in Bayesian inference, statistical physics and simulation studies. Samples from distributions give insight into their typical behavior and can allow approximation of any quantity of interest, such as expectations or normalizing constants. Markov chain Monte Carlo (MCMC), introduced by Metropolis et al. (1953), allows sampling from distributions with intractable normalization, and remains one of most important tools for approximate computation with probability distributions. While not needed by MCMC, normalizers are key quantities: in Bayesian statistics marginal likelihoods are needed for model comparison; in statistical physics many physical quantities relate to the partition function. In this thesis we propose and investigate several new Monte Carlo algorithms, both for evaluating normalizing constants and for improved sampling of distributions. Many MCMC correctness proofs rely on using reversible transition operators; often these operators lead to slow diffusive motion resembling a random walk. After reviewing existing MCMC algorithms, we develop a new framework for constructing non-reversible transition operators that allow more persistent motion. Next we explore and extend MCMC-based algorithms for computing normalizing constants. We compare annealing, multicanonical and nested sampling, giving recommendations for their use. We also develop a new MCMC operator and Nested Sampling approach for the Potts model. This demonstrates that nested sampling is sometimes better than annealing methods at computing normalizing constants and drawing posterior samples. Finally we consider “doubly-intractable” distributions with extra unknown normalizer terms that do not cancel in standard MCMC algorithms. We propose using several deterministic approximations for the unknown terms, and investigate their interaction with sampling algorithms. We then develop novel exact-sampling-based MCMC methods, the Exchange Algorithm and Latent Histories. For the first time these algorithms do not require separate approximation before sampling begins. Moreover, the Exchange Algorithm outperforms the only alternative sampling algorithm for doubly intractable distributions.
The mismatch of virtual instruction practices with young adolescents’ developmental needs

Abstract The COVID-19 pandemic caused schools to shift to virtual or hybrid instruction, thrusting young adolescents into a very different educational environment. Stage-environment fit theory outlines ways in which educational contexts may, or may not, meet the developmental needs of young adolescents. Yet little is known about how virtual environments fit the needs of middle schoolers. To assess this, we surveyed 430 middle school students in California during emergency distance learning. Open- and close-ended survey questions revealed the ways in which virtual schooling was not able to match the needs of these young adolescents. Student responses included challenges related to lack of connections with peers and teachers, difficulty working independently, and general mental health issues. Though limited, these results highlight the ways in which to consider developmental stages when altering school contexts in the face of the COVID-19 pandemic. Such findings offer insights on how to adjust online practices so that they can better support the developmental needs of middle school students.
Postnatal maturation of in vivo membrane potential dynamics during hippocampal ripples

Sharp-wave ripples (SWRs) are transient high-frequency oscillations of local field potentials (LFPs) in the hippocampus and play a critical role in memory consolidation. During SWRs, CA1 pyramidal cells exhibit rapid spike sequences that often replay the sequential activity that occurred during behavior. This temporally organized firing activity gradually emerges during two weeks after the eye opening; however, it remains unclear how the organized spikes during SWRs mature at the intracellular membrane potential (Vm) level. Here, we recorded the Vm of CA1 pyramidal cells simultaneously with hippocampal LFPs from immature mice after the developmental emergence of SWRs. On postnatal days 16–17, Vm dynamics around SWRs were premature, characterized by prolonged depolarizations without either pre- or post-SWR hyperpolarization. The biphasic hyperpolarizations, which are a typical feature of adult SWR-relevant Vm, formed by approximately postnatal day 30. This Vm maturation was associated with an increase in SWR-associated inhibitory inputs to pyramidal cells. Thus, the development of SWR-relevant inhibition restricts the temporal windows for spikes of pyramidal cells and allows CA1 pyramidal cells to organize their spike sequences during SWRs. Significance statement Sharp-wave ripples (SWRs) are prominent hippocampal oscillations and play a critical role in memory consolidation. During SWRs, hippocampal neurons synchronously emit spikes with organized temporal patterns. This temporal structure of spikes during SWRs develops during the third and fourth postnatal weeks, but the underlying mechanisms are not well-understood. Here, we recorded in vivo membrane potentials from hippocampal neurons in premature mice and suggest that the maturation of SWR-associated inhibition enables hippocampal neurons to produce precisely controlled spike times during SWRs.
A weak derivative approach to optimization of threshold parameters in a multicomponent maintenance system

We consider a multicomponent maintenance system controlled by an age replacement policy: when one of the components fails, it is immediately replaced; all components older than a threshold age θ are preventively replaced. Costs are associated with each maintenance action, such as replacement after failure or preventive replacement. We derive a weak derivative estimator for the derivative of the cost performance with respect to θ. The technique is quite general and can be applied to many other threshold optimization problems in maintenance. The estimator is easy to implement and considerably increases the efficiency of a Robbins-Monro type of stochastic approximation algorithm. The paper is self-contained in the sense that it includes a proof of the correctness of the weak derivative estimation algorithm.
A Reynolds stress model of turbulence and its application to thin shear flows

The paper provides a model of turbulence which effects closure through approximated transport equations for the Reynolds stress tensor $\overline{u_iu_j}$ and for the turbulence energy-dissipation rate ε. In its most general form the model thus entails the solution of seven transport equations for turbulence quantities but contains only six constants to be determined by experiment. It is demonstrated that the proposed approximation to the pressure-rate-of-strain correlations leads to satisfactory predictions of the component stress levels in plane homogeneous turbulence, including the non-equality of the lateral and transverse normal-stress components. For boundary-layer flows a simpler version of the model is derived wherein transport equations are solved only for the shear stress $-\overline{u_1u_2}$ the turbulence energy κ and ε. This model has been incorporated in the numerical solution procedure of Patankar & Spalding (1970) and applied to the prediction of a number of boundary-layer flows including examples of flow remote from walls, those developing along one wall and those confined within ducts. Three of the flows are strongly asymmetric with respect to the surface of zero shear stress and here the turbulent shear stress does not vanish where the mean rate of strain goes to zero. In most cases the predicted profiles and other quantities accord with the data within the probable accuracy of the measurements.
ASYMMETRIC TURBULENT VORTICAL FLOWS OVER SLENDER BODIES

Time-accurate numerical solutions have been obtained of equations modeling turbulent subsonic flows over a slender ogive-cylinder body of revolution in the high-angle-of-attack regime where a large asymmetry in the mean flow has been observed experimentally. A modified algebraic eddy-viscosity turbulence model was utilized to correctly compute the effects of the asymmetric vortices on the underlying viscous layers. In order to reproduce any one of the experimentally observed asymmetric flow-fields, it was found necessary to add a small geometrical disturbance near the body apex. By determining an appropriate size of the disturbance, it was possible to obtain excellent agreement between numerical results and experimental data for angles of attack of 30 and 40 deg, Reynolds numbers of 3.0 x 10 to the 6th and = 4.0 x 10 to the 6th, and several roll angles. When the disturbance was removed, the flow field returned to its original symmetric shape. These results are similar in behavior to solutions obtained previously for laminar flows. Just as in the laminar case, results suggest that the origin of the asymmetry is a convective-type instability of an originally symmetric flow.
Using a fully coupled flow and geomechanical simulator to model injection into heavy oil reservoirs

In this paper, the geomechanical factors that may affect injection processes in heavy oil recovery are investigated. To accurately capture the geomechanical effects, we employed a numerical formulation that allows fully coupling of nonlinear geomechanical deformation and multicomponent porous media flows. Two salient features of this new coupling formulation are the following: (1) all flow and geomechanical equations are solved implicitly in one single matrix equation, and (2) it allows reuse of matrices from both a traditional fully implicit multicomponent reservoir simulator and a nonlinear geomechanics simulator. The former feature ensures stable coupling between the reservoir flow and geomechanics, and the latter significantly reduces the programming work. Numerical examples are given to demonstrate the accuracy and convergence performance of the new formulation.
Gravity currents entering a two-layer fluid

This paper presents a study of steady gravity currents entering a two-layer system, with the current travelling either along the boundary to form a boundary current, or between the two different layers to form an intrusion. It is shown that, at the front of an intrusion, the streamlines meet at angles of 120° at a stagnation point. For an energy-conserving current the volume inflow rate to the current, the velocity of propagation and the downstream depths are determined. In contrast to the pioneering study of Benjamin (1968), it is found that the depth of the current is not always uniquely determined and it is necessary to use some principle additional to the conservation relationships to determine which solution occurs. An appropriate principle is obtained by considering dissipative currents. In general, if the volume inflow rate to a current is prescribed, the current loses energy in order to maintain a momentum balance. We thus suggest the criterion that the energy dissipation is a maximum for a fixed volume inflow rate. It is postulated that the energy which is lost will go to form a stationary wave train behind the current. A nonlinear calculation is carried out to determine the amplitude and wavelength of these waves for intrusions. Such waves have been observed on intrusions in laboratory experiments and the results of the calculation are found to agree well with the experiments. Similar waves have not been observed on boundary currents because the resulting waves have too much energy and break.
Effects of large-scale free stream turbulence on a turbulent boundary layer

Results of a wind tunnel experiment in which there are systematic variations of free stream turbulence above a flat-plate boundary layer are presented. Upstream of the plate, an active grid generates free stream turbulence varying in intensity from 0.25% to 10.5%. The momentum thickness Reynolds number of the boundary layer varies from 550 to nearly 3000. In all cases, the ratio of the free stream turbulence length scale to the boundary layer depth is greater than unity. Hotwire measurements show that, at high turbulence intensities, the effects of the free stream turbulence extend deep into the boundary layer, affecting the wall stress as well as the small-scale (derivative) statistics. Premultiplied energy spectra show a double peak. At very low free stream turbulence intensities these peaks are associated with the inner and outer scales of the turbulent boundary layer, but at high turbulence intensities the free stream energy peak dominates over the boundary layer’s outer scale. The implications of the.
The Influence of Mental Models on Employee-Driven Digital Process Innovation during Times of a Crisis

Digital technologies democratise the development of digital innovation. The resulting em-ployee-driven digital innovation has become a major driver for digital transformations and especially important during crisis times, such as the COVID 19 pandemic. To better understand cognitive factors influencing employee-driven digital process innovation (EDPI), we investigate the role of individual mental models for EDPI during times of a crisis compared to ‘normal’ times. Drawing from longitudinal data before and during the COVID 19 crisis, we find mental models having a significant influence on EDPI behaviour during ‘normal’ times. This relationship, however, loses robustne ss during the crisis, when employees with more accurate mental models show significant less EDPI behaviour before slowly recovering. We relate these findings to the mental models’ explanatory power and derive recommendations for management. Our study contributes explanatory knowledge on employee-driven digital innovation and related cognitive antecedents.