Gas turbulence modulation in a two-fluid model for gas-solid flows

Recent rapid progress in the theoretical and experimental study of turbulence modulation has led to greater understanding of the physics of particle-gas turbulence interactions. A new two-fluid model incorporating these advances for relatively dilute gas-solid flows containing high-inertia particles is established. The effect of aerodynamic forces upon the particulate stresses is considered in this kinetic theory-based model, and the influence of the particles on the turbulent gas is addressed: the work associated with drag forces contributes to the gas turbulent energy, and the space occupied by particles restricts the turbulent length scale. The interparticle length scale, which is usually ignored, has been incorporated into a new model for determining the turbulent length scale. This model also considers the transport effect on the turbulent length scale. Simulation results for fully developed steady flows in vertical pipes are compared with a wide range of published experimental data and, generally, good agreement is shown. This comprehensive and validated model accounts for many of the interphase interactions that have been shown to be important

The IACOB project. VI. On the elusive detection of massive O-type stars close to the ZAMS

The apparent lack of massive O-type stars near the zero-age main sequence (at ages < 2 Myr) is a topic widely discussed. Different explanations for this elusive detection have been proposed, but no firm conclusions have been reached yet. We reassess this empirical result benefiting from the high-quality spectroscopic observations of >400 Galactic O-type stars gathered by the IACOB and OWN surveys. We used temperatures and gravities from a iacob-gbat/fastwind spectroscopic analysis to locate our sample in the Kiel and spectroscopic HR diagrams. We evaluated the completeness of our sample of stars, observational biases using information from the Galactic O star catalog (GOSC), systematics of our methodology, and compare with other recent studies using smaller samples of Galactic O-type stars. We base our discussion on the spectroscopic HR diagram to avoid the use of uncertain distances. We performed a detailed study of the young cluster Trumpler-14 as an example of how Gaia cluster distances can help to construct the associated classical HR diagram. The apparent lack of massive O-type stars near the ZAMS with masses between 30 and 70 Msol persist even when spectroscopic results from a large, non-biased sample of stars are used. We do not find correlation between the dearth of stars and observational biases, limitations of our methodology, or the use of spectroscopic HR diagram instead of the classical one. Investigating the efficiency of mass accretion during the formation process we conclude that an adjustment of the accretion rate towards lower values could reconcile the hotter boundary of detected O-type stars and the theoretical birthline. Last, we discuss that the presence of a small sample of O2-O3.5 stars found closer to the ZAMS might be explained taking into account non-standard star evolution (e.g. binary interaction, mergers, or homogeneous evolution).Comment: 20 pages, 15 figures, accepted for publication in Astronomy & Astrophysic

Understanding Solidity Event Logging Practices in the Wild

Writing logging messages is a well-established conventional programming practice, and it is of vital importance for a wide variety of software development activities. The logging mechanism in Solidity programming is enabled by the high-level event feature, but up to now there lacks study for understanding Solidity event logging practices in the wild. To fill this gap, we in this paper provide the first quantitative characteristic study of the current Solidity event logging practices using 2,915 popular Solidity projects hosted on GitHub. The study methodically explores the pervasiveness of event logging, the goodness of current event logging practices, and in particular the reasons for event logging code evolution, and delivers 8 original and important findings. The findings notably include the existence of a large percentage of independent event logging code modifications, and the underlying reasons for different categories of independent event logging code modifications are diverse (for instance, bug fixing and gas saving). We additionally give the implications of our findings, and these implications can enlighten developers, researchers, tool builders, and language designers to improve the event logging practices. To illustrate the potential benefits of our study, we develop a proof-of-concept checker on top of one of our findings and the checker effectively detects problematic event logging code that consumes extra gas in 35 popular GitHub projects and 9 project owners have already confirmed the detected issues.Comment: Accepted by 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE'23

Greedy Population Sizing for Evolutionary Algorithms

The number of parameters that need to be man ually tuned to achieve good performance of Evolutionary Algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a Greedy Population Sizing method for Evolutionary Algo rithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPS EA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually

Toward Automating EA Configuration: The Parent Selection Stage

One of the obstacles to Evolutionary Algorithms (EAs) fulfilling their promise as easy to use general-purpose problem solvers, is the difficulty of correctly configuring them for specific problems such as to obtain satisfactory performance. Having a mechanism for automatically configuring parameters and operators of every stage of the evolutionary life-cycle would give EAs a more widely spread popularity in the non-expert community. This paper investigates automatic configuration of one of the stages of the evolutionary life-cycle, the parent selection, via a new concept of semi-autonomous parent selection, where mate selection operators are encoded and evolved as in Genetic Programming. We compare the performance of the EA with semi-autonomous parent selection to that of a manually configured EA on three common test problems to determine the “price” we pay for user-friendliness

JUST CULTURE IN HEALTHCARE ERROR MANAGEMENT: NURSE-IN-TRAINING VIEW OF JUST CULTURE AND OUTCOMES OF EVENT INVOLVEMENT

This experimental study will assess the behavioral and psychosocial effects of just culture error management strategies for medical errors in a healthcare setting, and the outcomes of such strategies on work-related perceptions, attitudes, and behaviors. A total of 247 nurses-in-training were randomly assigned to one of 6 experimental conditions. In each condition, participants read a vignette that described an at-risk medical error and the error management strategy employed by a hypothetical organization. The medical error was written to implicate both the individual involved, and the larger organizational system. Vignettes differed with regard to error management strategy employed by the organization (punitive, blameless, just culture) and the degree of event severity (no harm, harm). Participants rated the organizational justice and trustworthiness of the hypothetical organization described in the vignette; then, reported their own willingness to engage in safety compliance and error reporting behaviors and their degree of organizational commitment and attraction. Results suggest that error management strategies based in just culture were associated with increase perceptions of organizational justice and trustworthiness, increased intention to engage in safety compliance, and stronger attraction and commitment to the organization. Furthermore, perceptions about the organizational justice and organizational trust mediated the relationship between error management strategy and these outcomes. Event severity did not moderate the association between error management and organizational perceptions. Furthermore, error management strategy was unrelated to error reporting intention. Control variables of familiarity with concepts of just culture, experience with medical errors (as provider or patient), and demographic variables of gender and age were not associated with organizational commitment, organizational attraction, or safety compliance. However, error reporting intention was positively associated with familiarity with concepts of just culture was positively and negatively associated with experience with medical errors as a provider

Differential Evolution and Combinatorial Search for Constrained Index Traking

Index tracking is a valuable low-cost alternative to active portfolio management. The implementation of a quantitative approach, however, is a major challenge from an optimization perspective. The optimal selection of a group of assets that can replicate the index of a much larger portfolio requires both to find the optimal subset of assets and to fine-tune their weights. The former is a combinatorial, the latter a continuous numerical problem. Both problems need to be addressed simultaneously, because whether or not a selection of assets is promising depends on the allocation weights and vice versa. Moreover, the problem is usually of high dimension. Typically, an optimal subset of 30-150 positions out of 100-600 need to be selected and their weights determined. Search heuristics can be a viable and valuable alternative to traditional methods, which often cannot deal with the problem. In this paper, we propose a new optimization method, which is partly based on Differential Evolution (DE) and on combinatorial search. The main advantage of our method is that it can tackle index tracking problem as complex as it is, generating accurate and robust results