Driving Mechanisms of Double-Nosed Low-Level Jets during MATERHORN Experiment

In the realm of boundary-layer flows in complex terrain, low-level jets (LLJs) have received considerable attention, although little literature is available for double-nosed LLJs that remain not well understood. To this end, we use the MATERHORN dataset to demonstrate that double-nosed LLJs developing within the planetary boundary layer (PBL) are common during stable nocturnal conditions and present two possible mechanisms responsible for their formation. It is observed that the onset of a double-nosed LLJ is associated with a temporary shape modification of an already-established LLJ. The characteristics of these double-nosed LLJs are described using a refined version of identification criteria proposed in the literature, and their formation is classified in terms of two driving mechanisms. The wind-driven mechanism encompasses cases where the two noses are associated with different air masses flowing one on top of the other. The wave-driven mechanism involves the vertical momentum transport by an inertial-gravity wave to generate the second nose. The wave-driven mechanism is corroborated by the analysis of nocturnal double-nosed LLJs, where inertial-gravity waves are generated close to the ground by a sudden flow perturbation.

Human Factors in Urban Transport: Control Room Resource Management Training (CRRM)

Control center operations are highly demanding in terms of the collaboration required for monitoring and decision making in teams. In a joint effort led by the DLR, several institutions investigated psychological requirements among teams in control centers. This involved holding workshops and shadowing the work of experts for operative processes in control rooms of several industry partners (Deutsche Flugsicherung GmbH, Deutsche Lufthansa AG, Flughafen Hamburg GmbH, Hamburger Hochbahn AG). An initial control room resource management (CRRM) training was developed and tested with a group of operators from the Galileo Control Center (GCC). The refined version of the 2-day CRRM training was developed for groups of 10-12 participants and was validated in seven training sessions with a total of 79 operators from the subway control center of the Hamburger Hochbahn AG. In conclusion, the CRRM training provides a successful approach to improve teamwork in control rooms.

A refined measure of conditional maximum drawdown

Risks associated to maximum drawdown have been recently formalized as the tail mean of the maximum drawdown distribution, called Conditional Expected Drawdown (CED). In fact, the special case of average maximum drawdown is widely used in the fund management industry also in association to performance management. It lacks relevant information on worst case scenarios over a fixed horizon. Formulating a refined version of CED, we are able to add this piece of information to the risk measurement of drawdown, and then get a risk measure for processes that preserves all the good properties of CED but following more prudential regulatory and management assessments, also in term of marginal risk contribution attributed to factors. As a special application, we consider the conditioning information given by the all time minimum of cumulative returns.

Solutions of the Bullough–Dodd Model of Scalar Field through Jacobi-Type Equations

A technique based on multiple auxiliary equations is used to investigate the traveling wave solutions of the Bullough–Dodd (BD) model of the scalar field. We place the model in a flat and homogeneous space, considering a symmetry reduction to a 2D-nonlinear equation. It is solved through this refined version of the auxiliary equation technique, and multiparametric solutions are found. The key idea is that the general elliptic equation, considered here as an auxiliary equation, degenerates under some special conditions into subequations involving fewer parameters. Using these subequations, we successfully construct, in a unitary way, a series of solutions for the BD equation, part of them not yet reported. The technique of multiple auxiliary equations could be employed to handle several other types of nonlinear equations, from QFT and from various other scientific areas.

Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity

We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model’s requirement that variance should be larger than the expected value. Our version of an IB model is more appropriate, as it can accommodate all potential data scenarios in which the variance is smaller, equal, or larger than the mean. This is unlike the usual IB, which accommodates only the scenario in which the variance is more than the mean. Therefore, we propose a refined version of an IB model to be able to accommodate all potential data scenarios. The application of the approach is based on a restricted infectivity rate and methodology on COVID-19 data, which exhibit two clusters of infectivity. Cluster 1 has a smaller number of primary cases and exhibits larger variance than the expected cases with a negative correlation of 28%, implying that the number of secondary cases is lesser when the number of primary cases increases and vice versa. The traditional IB model is appropriate for Cluster 1. The probability of contracting COVID-19 is estimated to be 0.13 among the primary, but is 0.75 among the secondary in Cluster 1, with a wider gap. Cluster 2, with a larger number of primary cases, exhibits smaller variance than the expected cases with a correlation of 79%, implying that the number of primary and secondary cases do increase or decrease together. Cluster 2 disqualifies the traditional IB model and requires its refined version. The probability of contracting COVID-19 is estimated to be 0.74 among the primary, but is 0.72 among the secondary in Cluster 2, with a narrower gap. The advantages of the proposed approach include the model’s ability to estimate the community’s health system memory, as future policies might reduce COVID’s spread. In our approach, the current hazard level to be infected with COVID-19 and the odds of not contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable.

A Heuristic Approach to Use Behavioral Models to Design for Change

PMD is a method to design for sustainable behavior change within organizations concerning the introduction of innovation. An earlier evaluation of PMD among users and a use case resulted in the need for refinements. In this paper, the authors describe the refined version of PMD and validate this in another case, in which a solution based on the interaction elements resulting from the PMD method was created for a company. Based on data acquired, they designed models of current and requested behavior. They selected the right interaction elements to facilitate the target audience in a change of behavior. After implementing the solution at the pre-fab concrete company, the authors observed a change in behavior among users, growing towards the behavior required to reach the set business objectives. The research allowed for the creation, evaluation, and validation of the PMD method itself in a real-life situation and showed that it is possible, at least in the use case in this research, to design for a required behavior change to increase adoption of innovations in organizations.

What is Arbitrary about Moral Arbitrariness?

John Rawls’s ‘justice as fairness’ is often cited as a central source of inspiration for luck egalitarianism, which is, correlatively, often characterized as a more refined version of justice as fairness. Rawls’s distributive hostility to morally arbitrary endowments is standardly interpreted as betraying hostility to distributions that are skewed by brute luck. This chapter argues otherwise. It has two main aims. First, it replaces the standard ‘Neutralization Interpretation’ of Rawls’s main arguments with the ‘Irrelevance Interpretation’. According to the Irrelevance Interpretation, morally arbitrary person endowments ought to play no role in the selection of principles of justice in the original position. According to the Neutralization Interpretation, by contrast, principles of justice ought to expunge the influence of any inequalities that are due to luck. The Irrelevance Interpretation is more permissive of inequalities, just as long as they serve some other purpose, such as improving the position of the worst-off. The Irrelevance Interpretation is also more congenial to Rawls’s investment in the contractarian machinery of the original position and the veil of ignorance.

New-type Hoeffding’s inequalities and application in tail bounds

It is well known that Hoeffding's inequality has a lot of applications in the signal and information processing fields. How to improve Hoeffding's inequality and find the refinements of its applications have always attracted much attentions. An improvement of Hoeffding inequality was recently given by Hertz [<a href="#1">1</a>]. Eventhough such an improvement is not so big, it still can be used to update many known results with original Hoeffding's inequality, especially for Hoeffding-Azuma inequality for martingales. However, the results in original Hoeffding's inequality and its refined version by Hertz only considered the first order moment of random variables. In this paper, we present a new type of Hoeffding's inequalities, where the high order moments of random variables are taken into account. It can get some considerable improvements in the tail bounds evaluation compared with the known results. It is expected that the developed new type Hoeffding's inequalities could get more interesting applications in some related fields that use Hoeffding's results.

Reheating in small-field inflation on the brane: the swampland criteria and observational constraints in light of the PLANCK 2018 results

We study cosmological inflation and its dynamics in the framework of the Randall–Sundrum II brane model. In particular, we analyze in detail four representative small-field inflationary potentials, namely Natural inflation, Hilltop inflation, Higgs-like inflation, and Exponential SUSY inflation, each characterized by two mass scales. We constrain the parameters for which a viable inflationary Universe emerges using the latest PLANCK results. Furthermore, we investigate whether or not those models in brane cosmology are consistent with the recently proposed Swampland Criteria, and give predictions for the duration of reheating as well as for the reheating temperature after inflation. Our results show that (i) the distance conjecture is satisfied, (ii) the de Sitter conjecture and its refined version may be avoided, and (iii) the allowed range for the five-dimensional Planck mass, $$M_5$$ M 5 , is found to be $$10^5\,\text {TeV}\lesssim M_5\lesssim 10^{12}\,\text {TeV}$$ 10 5 TeV ≲ M 5 ≲ 10 12 TeV . Our main findings indicate that non-thermal leptogenesis cannot work within the framework of RS-II brane cosmology, at least for the inflationary potentials considered here.

A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy

Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on infection cases and hospitalizations informed decision makers on the ongoing pandemic emergency, enabling the design of diversified countermeasures, from behavioral policies to full lockdowns, to curb the virus spread. In this context, mechanistic models could represent valuable tools to optimize the timing and stringency of interventions, and to reveal non-trivial properties of the pandemic dynamics that could improve the design of suitable guidelines for future epidemics. We performed a retrospective analysis of the Italian epidemic evolution up to mid-December 2020 to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign. We defined a time-varying optimization procedure to calibrate a refined version of the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model and hence accurately reconstruct the epidemic trajectory. We then derived additional features of the COVID-19 pandemic in Italy not directly retrievable from reported data, such as the estimate of the day zero of infection in late November 2019 and the estimate of the spread of undetected infection. The present analysis contributes to a better understanding of the past pandemic waves, confirming the importance of epidemiological modeling to support an informed policy design against epidemics to come.