Miracle-dz/UniFAR-FTUs huggingface dataset:
Valve actuation effects on discrete monopropellant slug delivery in a micro-scale fuel injection system

A recent focus of the aerospace industry has been in the development of microthrusters for satellites in the 1.0 to 10.0 kg range, often known as “nanosats”. Nanosats are of interest for NASA and the DoD because of their ability to reduce satellite life-cycle costs while performing valuable scientific and defense missions. A survey of past and current missions involving nanosats, which can be found in Bouwmeester et al.,2 indicates that the capabilities of nanosatellites are technologically limited on several fronts including power, communication and thruster subsystems. The thrusters, which are the propulsion system tasked with positioning and orbital maintenance, are a particular challenge because of the low levels of thrust, on the order of micronewtons, required to make precise changes in position that may be required as part of a mission. Micropropulsion is an area of ongoing research with many candidate systems proposed, including electric propulsion systems and liquid and solid chemical propellant systems. Reviews of micropropulsion strategies for nanosat microthrusters can be found in Helvajian et al.3 Monopropellant-based propulsion is an attractive scheme for microthruster applications since it o↵ers a combination of relatively high energy density and simplicity of design. The latter is especially significant for the construction of miniaturized propulsion systems. The first prototype monopropellant microthruster reported in the aerospace literature was based on hydrogen peroxide and developed using microelectromechanical systems (MEMS) techniques at NASA/Goddard Space Flight Center.4 As part of that study, the operating characteristics required for the thruster subsystem were presented, and are shown in Table 1. ⇤Graduate Student, AIAA Student Member †Associate Professor, AIAA Associate Fellow 1 of 16 American Institute of Aeronautics and Astronautics 42nd AIAA Fluid Dynamics Conference and Exhibit 25 28 June 2012, New Orleans, Louisiana AIAA 2012-3160
Potentialities of Podcasting in Health Journalism — An Analysis of Three Podcasts About Covid-19 in Portugal

The pandemic caused by covid-19 was a lever for the appearance of several podcasts dedicated to the disease. In several countries, including Portugal, the media added audio content to their traditional information offering by using this digital tool. The podcast’s popularity has grown with audiences, and media companies look to this platform as a good strategy to diversify content and thus reach audiences. In times of crisis, such as a pandemic, the need for populations to access credible information they can trust and thus gain knowledge to help them make decisions increases. In this particular case, covid-19, the populations felt the need to be informed about an unknown disease, looking for information about symptoms, contagion, or means of personal protection, and podcasts represented an answer to these information needs. This article examines how three Portuguese podcasts dedicated exclusively to covid-19 treated the disease. Based on the data, we will reflect on the importance of this new tool for health journalism. The results of the research allow us to conclude that the national podcasts on covid-19 analyzed follow some patterns identified in the coverage of health topics in Portugal and that, adopting an “advisory” tone, they placed themselves in the position of supporting tools in combat the pandemic by clarifying, informing and clarifying issues related to the disease.
Machine Learning Can Predict AntieVEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVOAssociated Macular Edema

Purpose: To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related macular degeneration (nAMD), retinal vein occlusion (RVO), and diabetic macular edema (DME) treated according to a treat-and-extend regimen (TER). Design: Retrospective cohort study. Participants: Three hundred seventy-seven eyes (340 patients) with nAMD and 333 eyes (285 patients) with RVO or DME treated with antievascular endothelial growth factor agents (VEGF) according to a predefined TER from 2014 through 2018. Methods: Eyes were grouped by disease into low, moderate, and high treatment demands, defined by the average treatment interval (low, 10 weeks; high, 5 weeks; moderate, remaining eyes). Two random forest models were trained to predict the probability of the long-term treatment demand of a new patient. Both models use morphological features automatically extracted from the OCT volumes at baseline and after 2 consecutive visits, as well as patient demographic information. Evaluation of the models included a 10-fold cross-validation ensuring that no patient was present in both the training set (nAMD, approximately 339; RVO and DME, approximately 300) and test set (nAMD, approximately 38; RVO and DME, approximately 33). Main Outcome Measures: Mean area under the receiver operating characteristic curve (AUC) of both models; contribution to the prediction and statistical significance of the input features. Results: Based on the first 3 visits, it was possible to predict low and high treatment demand in nAMD eyes and in RVO and DME eyes with similar accuracy. The distribution of low, high, andmoderate demanders was 127, 42, and 208, respectively, for nAMD and 61, 50, and 222, respectively, for RVO and DME. The nAMD-trained models yielded mean AUCs of 0.79 and 0.79 over the 10-fold crossovers for low and high demand, respectively. Models for RVO and DME showed similar results, with a mean AUC of 0.76 and 0.78 for low and high demand, respectively. Even more importantly, this study revealed that it is possible to predict low demand reasonably well at the first visit, before the first injection. Conclusions: Machine learning classifiers can predict treatment demand andmay assist in establishing patientspecific treatment plans in the near future.Ophthalmology Retina 2021;5:604-624a 2021 by the American Academy of Ophthalmology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Flip bifurcation and chaos in three-state boost switching regulators

A first-order iterative map that describes the dynamics of a simple feedback boost switching regulator operating in discontinuous mode is derived. Analysis of this map shows that flip bifurcations occur at certain values of the feedback factor. Results from computer simulations and experiments reveal that the system exhibits a typical period-doubling route to chaos under the particular operating condition studied in this paper. It is found that a special kind of randomness, arising from the ability of the system to change its configuration in more than one predefined pattern, constitutes a unique feature of the chaotic dynamics of switched-mode converter circuits. >
THE USE OF BY PRODUCT-WASTE MATERIALS ON ROAD PAVEMENT CONCTRUCTION IN INDONESIA

One of efforts for sustainability in Road Pavement Materials agenda is reducing virgin material in road pavement construction and increasing the use of waste materials as alternative materials in road pavement materials without reducing quality of pavement structure. The objective of this paper is to discuss the use of product waste materials from industry for road pavement construction in Indonesia. Waste materials used in this study as additive materials on road construction are Residium Catalytic Cracking 15 (RCC-15), lignin, slag and tailing. RCC15 addition showed an increase of soil bearing capacity and reduce the level of expansion on soil on composition of 2% RCC and 2% PCC, the addition of 4% lignin by weight of dry soil can increase the unsoaked CBR, the use 100% of slag will provide a better mix characteristic than the natural aggregate mixture and the addition of 45% tailing in mix gave good effect on stability of base and sub-base layer and Generally, test laboratory results showed that road construction containing waste materials have higher performance than conventional road construction.
THE ROLE OF WHATSAPP APPLICATION IN ENCOURAGING STUDENTS’ SELF-EFFICACY OF USING BLACKBOARD SYSTEM: AN EXPLORATIVE INTERVIEW STUDY

This study aims to examine how students' social media use can play a role in encouraging their self-efficacy of using the blackboard system for learning purposes, considering their experience of using the blackboard system. This study argues that the mentioned positive role of social media in teaching and learning emerged as a govern social media platform for students. This avoids the negative effect of social media on them and provides a social media platform for assisting and guiding students. A qualitative method approach was used, using interviews conducted with 36 students. The semi-structured interviews were analyzed following the thematic content analysis method. The interviews were targeted at obtaining University of Ha'il students' opinions to confirm the challenges facing them concerning the usage of the Blackboard system. The findings show that most of the participants had positive beliefs toward the implementation of WhatsApp with Blackboard. Especially those students with little experience using the Blackboard system can improve their communication experience. We recommend future studies, finding theories to integrate the uses of social media in the education process. Next, researchers should also develop how WhatsApp can improve learning using Blackboard, especially for students with modest skills.
Evolution of ERP Systems Implementation in Taiwan — Four Surveys in Ten Years

This paper presents the results of four cross-sectional surveys of the top 5000 largest corporations in Taiwan, undertaken in 2003, 2006, 2009 and 2012. We focus on a longitudinal study of the used or intended implementation of Enterprise Resource Planning (ERP) systems of firms to examine the ”innovation diffusion” phenomenon and examine key management issues during the ”ERP life cycle” phases. Our initial empirical analysis provides us with a number of interesting results. We find that the implementation of an ERP project entails an innovation diffusion phenomenon. Our analysis further shows that some important management issues deserve more attention during the life cycle of ERP projects. Based on our findings, practitioners, academics and entrepreneurs will benefit from this knowledge to prepare an effective management plan to increase ERP implementation success.
PRBM_A_341685 2135..2146

1School of Business Administration, Zhejiang Gongshang University, Hangzhou, People’s Republic of China; 2School of Business, Wenzhou University, Wenzhou, People’s Republic of China Purpose: The uncertainties in the market have led to an increasing number of uncertainties and unexpected situations in the work environment of organizations. Based on information processing theory, we investigate how teams can dynamically integrate the expertise of their members to better respond to an uncertain market environment. We propose an important team cognition: team expertise location awareness and point out its unique impact on knowledge integration and thus on improvisation capabilities. In addition, we argue that shared leadership facilitates the use of team recognition resources by teams. Methods: This study adopts a multi-source design approach and collects data from an information technology (IT) company that provides apps of voice socialization and game for foreign markets in southern China. Our sample comprised 86 IT teams, and hierarchical regression and bootstrapping methods are also employed to test the hypotheses. Results: This study reveals that (1) team’s expertise location awareness positively influences the team’s knowledge integration ability which in turn enhances the team’s improvisational ability; (2) team’s knowledge integration mediates the effect of team’s expertise location awareness and team’s improvisational ability; (3) shared leadership moderated this above mediation effect. Conclusion: In this paper, we introduce information processing theory into team improvisation research to understand why some teams can effectively utilize and integrate their members’ knowledge and information and further contribute to effective team improvisation. These results’ theoretical and practical implications for team expertise location awareness, team knowledge integration, shared leadership, and team improvisation are discussed.
On the Reception Process of Molecular Communication-Based Drug Delivery

One of the important applications of molecular communication is the targeted drug delivery process in which the drug molecules are released toward the target (receiver) in a way that the side effects are minimized in the human body. As the total number of released molecules increases, the receiver cannot receive all of the transmitted drug molecules. Therefore, the molecules accumulate in the system which results in side effects in the body. In order to diagnose the appropriate transmission rate of the drug molecules, it is important to investigate the reception process of the receiver. In this paper, a reception model is studied using queuing theory. In the proposed model, the rejection rate of the drug molecules due to different reasons, such as random movement of the molecules, as well as the rejection rate due to active receptors are taken into account. Moreover, an interval consisting of the lower and upper bounds for the number of released molecules is presented based on the proposed model in order to determine the range of allowable dosage of the drug molecules. It is shown that queuing theory can be successfully employed in accurate modeling of the reception process of the receiver in drug delivery applications.
Well-posedness of weak solution for a nonlinear poroelasticity model

In this paper, we study the existence and uniqueness of weak solution of a nonlinear poroelasticity model widely used in many fields such as geophysics, biomechanics, civil engineering, chemical engineering, materials science, and so on. To better describe the proccess of deformation and diffusion underlying in the original model, we firstly reformulate the nonlinear poroelasticity by a multiphysics approachwhich transforms the nonlinear fluid-solid coupling problem to a fulid-fluid coupling problem. Then, we adopt the similar technique of proving the well-posedness of nonlinear Stokes equations to prove the existence and uniqueness of weak solution of a nonlinear poroelasticity model. And we strictly prove the growth, coercivity and monotonicity of the nonlinear stress-strain relation, give the energy estimates and use Schauder’s fixed point theorem to show the existence and uniqueness of weak solution of the nonlinear poroelasticity model. Besides, we prove that the weak solution of nonlinear poroelasticity model converges to the nonlinear Biot’s consolidation model as the constrained specific storage coefficient trends to zero. Finally, we draw a conclusion to summary the main results of this paper.
Fully non-linear elliptic equations on compact hyperhermitian manifolds with a flat hyperk\"ahler metric

Mainly motivated by a conjecture of Alesker and Verbitsky, we study a class of elliptic equations on compact hyperhermitian manifolds. By adapting the approach of Székelyhidi [49] to the hypercomplex setting, we prove some a priori estimates for solutions to such equations. In the estimate of the Laplacian we assume the existence of a flat hyperkähler metric. As an application of our results we prove that the quaternionic analogue of the Hessian equation can always be solved on compact flat hyperkähler manifolds.
Should Transparency Be (In-)Transparent? On Monitoring Aversion and Cooperation in Teams

Many modern organisations employ methods which involve monitoring of employees' actions in order to encourage teamwork in the workplace. While monitoring promotes a transparent working environment, the effects of making monitoring itself transparent may be ambiguous and have received surprisingly little attention in the literature. Using a novel laboratory experiment, we create a working environment in which first movers can (or cannot) observe second mover's monitoring at the end of a round. Our framework consists of a standard repeated sequential Prisoner's Dilemma, where the second mover can observe the choices made by first movers either exogenously or endogenously. We show that mutual cooperation occurs significantly more frequently when monitoring is made transparent. Additionally, our results highlight the key role of conditional cooperators (who are more likely to monitor) in promoting teamwork. Overall, the observed cooperation enhancing effects are due to monitoring actions that carry information about first movers who use it to better screen the type of their co-player and thereby reduce the risk of being exploited.
Sparse M-estimators in semi-parametric copula models

We study the large sample properties of sparse M-estimators in the presence of pseudo-observations. Our framework covers a broad class of semi-parametric copula models, for which the marginal distributions are unknown and replaced by their empirical counterparts. It is well known that the latter modification significantly alters the limiting laws compared to usual M-estimation. We establish the consistency and the asymptotic normality of our sparse penalized M-estimator and we prove the asymptotic oracle property with pseudo-observations, possibly in the case when the number of parameters is diverging. Our framework allows to manage copula-based loss functions that are potentially unbounded. Additionally, we state the weak limit of multivariate rank statistics for an arbitrary dimension and the weak convergence of empirical copula processes indexed by maps. We apply our inference method to Canonical Maximum Likelihood losses with Gaussian copulas, mixtures of copulas or conditional copulas. The theoretical results are illustrated by two numerical experiments.
Large-scale structure with superhorizon isocurvature dark energy

The standard cosmological model assumes a homogeneous and isotropic universe as the background spacetime on large scales called the cosmological principle. However, some observations suggest the possibility of an inhomogeneous and anisotropic universe at large scales. In this paper, we investigate a model of the universe with random inhomogeneities and anisotropies on very large scales, motivated by the supercurvature dark energy model in Nan et al. [Phys. Rev. D 99, 103512 (2019)]. In this model, the authors introduced a scalar field with $\mathcal{O}(1)$ inhomogeneities on a scale sufficiently larger than the current horizon scale (superhorizon scale), and the potential energy of the scalar field explains the accelerating expansion, with slight deviations from the cosmological principle. We aim at clarifying the theoretical prediction on the large-scale structure (LSS) of the matter component in this model. Based on the work on the superhorizon scale fluctuations (superhorizon mode) presented in Nan and Yamamoto [Phys. Rev. D 105, 063518 (2022)], we derive the equations that the perturbative components to the LSS obey as a generalization of the cosmological perturbations theory, which is solved to find the influence of the dark energy inhomogeneities on the formation of the LSS. Finally, we show that the model can be consistent with observations by comparing the $\sigma_8$ predicted by the numerical solution of the model with the $\sigma_8$ indicated by observations such as Planck and SDSS.
Black-Box Testing of Deep Neural Networks through Test Case Diversity

Deep Neural Networks (DNNs) have been extensively used in many areas including image processing, medical diagnostics and autonomous driving. However, DNNs can exhibit erroneous behaviours that may lead to critical errors, especially when used in safety-critical systems. Inspired by testing techniques for traditional software systems, researchers have proposed neuron coverage criteria, as an analogy to source code coverage, to guide the testing of DNNs. Despite very active research on DNN coverage, several recent studies have questioned the usefulness of such criteria in guiding DNN testing. Further, from a practical standpoint, these criteria are white-box as they require access to the internals or training data of DNNs, which is often not feasible or convenient. Measuring such coverage requires executing DNNs with candidate inputs to guide testing, which is not an option in many practical contexts. In this paper, we investigate diversity metrics as an alternative to white-box coverage criteria. For the previously mentioned reasons, we require such metrics to be black-box and not rely on the execution and outputs of DNNs under test. To this end, we first select and adapt three diversity metrics and study, in a controlled manner, their capacity to measure actual diversity in input sets. We then analyze their statistical association with fault detection using four datasets and five DNNs. We further compare diversity with state-of-the-art white-box coverage criteria. As a mechanism to enable such analysis, we also propose a novel way to estimate fault detection in DNNs. Our experiments show that relying on the diversity of image features embedded in test input sets is a more reliable indicator than coverage criteria to effectively guide DNN testing. Indeed, we found that one of our selected black-box diversity metrics far outperforms existing coverage criteria in terms of fault-revealing capability and computational time. Results also confirm the suspicions that state-of-the-art coverage criteria are not adequate to guide the construction of test input sets to detect as many faults as possible using natural inputs.
Extremal stringy black holes in equilibrium at first order in $\alpha'$

We compute the first-order \alpha'α′ corrections to well-known families of heterotic multi-center black-hole solutions in five and four dimensions. The solutions can be either supersymmetric or non-supersymmetric, depending on the relative sign between two of the black-hole charges. For both cases, we find that the equilibrium of forces persists after including the \alpha'α′ corrections, as the existence of multi-center solutions free of unphysical singularities shows. We analyze the possibility of black-hole fragmentation.
A general multi-scale description of metastable adaptive motion across fitness valleys

We consider a stochastic individual-based model of adaptive dynamics on a finite trait graph $G=(V,E)$. The evolution is driven by a linear birth rate, a density dependent logistic death rate an the possibility of mutations along the (possibly directed) edges in $E$. We study the limit of small mutation rates for a simultaneously diverging population size. Closing the gap between the works of Bovier, Coquille and Smadi (2019) and Coquille, Kraut and Smadi (2021), we give a precise description of transitions between evolutionary stable conditions (ESC), where multiple mutations are needed to cross a valley in the fitness landscape. The system shows a metastable behaviour on several divergent time scales associated to a degree of stability. We develop the framework of a meta graph that is constituted of ESCs and possible metastable transitions between those. This allows for a concise description of the multi-scale jump chain arising from concatenating several jumps. Finally, for each of the various time scale, we prove the convergence of the population process to a Markov jump process visiting only ESCs of sufficiently high stability.