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Few modeling approaches exist for cycloidal rotors because they are a prototypal technology. Thus, the purpose of this study was to develop new models for their analysis and validation. These models were used to analyze cycloidal rotors and a helicopter that uses them instead of a tail rotor.

Three different models were developed to study the aerodynamic response of cycloidal rotors. They are a simplified analytical model resolved algebraically; a multibody model resolved numerically; and an unsteady computational fluid dynamics (CFD) model. The models were validated using data coming from three different experimental sources, each with rotor spans and radii of roughly 1 m. The CFD model was used to investigate the influence of rotor arms. The efficiency and the stability of the rotor in different configurations were studied. An aeroelastic multibody simulation was used to verify the influence of flexibility on the rotor response.

The analyses suggested that cycloidal rotors can increase the efficiency of a helicopter at high velocities while flexibility reduces it and may lead to instabilities.

These models do not consider the effect of boundary layer friction on the trailing vortices generated by the rotor blades.

These models allow a four-step aerodynamic optimization procedure. First, a range of optimized configurations is obtained by the analytical model. Second, the multibody model refines that range. Third, the CFD model detects eventual problematic blade interactions.

The models presented should serve researchers and industrials looking for a means to measure the performance of cycloidal rotors concepts. The results presented also guide an initial cycloidal rotor design.

Using intraday prices for the S&P 500 and Nikkei Stock Average stock indexes and aggregate trading volume for the New York and Tokyo Stock Exchanges, we show how short-run comovements between national stock market returns vary over time in a way related to the trading volume and liquidity in those markets. We frame our analysis in the context of the heterogeneous-agent models of trading developed by Campbell, Grossman and Wang (1993) and Blume, Easley and O’Hara (1994) and Wang (1994) which predict that trading volume acts as a signal of the information content of a given price move. While we find that there exists significant short-run dependence in returns and volatility between Japan and the U.S., we offer new evidence that these return “spillovers” are sensitive to interactions with trading volume in those markets. The cross-market effects with volume are revealed in both close-to-open and open-to-close returns and often exhibit non-linear patterns that are not predicted by theory.

In this paper, I provide an empirical work in order to test the tax‐adjusted market valuation (residual income) model. Feltham‐Ohlson's (1995) residual income model can be extended by adding corporate tax: firm market value is a function of the bottom line after‐tax accounting data, e.g., book value and after‐tax earnings. Under this tax‐adjusted framework, certain issues are examined: the information from the firm's operating activities is not enough to measure the firm's market value; financial activities also affect firm market value. In particular, abnormal financial earnings are not equal to zero, due to the tax deduction on interest expenses. An empirical analysis, using the financial reporting data of Canadian firms for the years 1994–1999, demonstrates that the current book value of financial assets and operating assets, abnormal operating earnings, and abnormal financial earnings are all relevant to firm market value. The sensitivity tests, which define the corporate tax rates in different ways, do not change the results. The sensitivity test, which uses the financial analysts' forecasts, does not change the results, either. Furthermore, the empirical analysis shows that abnormal financial earnings enhance firm share price more when the firm has lower non‐tax costs, i.e., firm business risk (financial distress) and bankruptcy costs. It supports the previous research on capital structure to the extent that debt financing benefits a firm more when non‐tax costs are lower.

Alternative education settings (AES; i.e., self-contained alternative schools, therapeutic day treatment and residential schools, and juvenile corrections schools) serve youth with complicated and often serious academic and behavioral needs. The use of evidence-based practices (EBPs) and practices with Best Available Evidence are necessary to increase the likelihood of long-term success for these youth. In this chapter, we define three primary categories of AES and review what we know about the characteristics of youth in these schools. Next, we discuss the current emphasis on identifying and implementing EBPs with regard to both academic interventions (i.e., reading and mathematics) and interventions addressing student behavior. In particular, we consider implementation in AES, where there are often high percentages of youth requiring special education services and who have a significant need for EBPs to succeed academically, behaviorally, and in their transition to adulthood. We focus our discussion on: (a) examining approaches to identifying EBPs; (b) providing a brief review of EBPs and Best Available Evidence in the areas of mathematics, reading, and interventions addressing student behavior for youth in AES; (c) delineating key implementation challenges in AES; and (d) providing recommendations for how to facilitate the use of EBPs in AES.

Artificial intelligence (AI) raises many expectations regarding its ability to profoundly transform health care delivery. There is an abundant literature on the technical performance of AI applications in many clinical fields (e.g. radiology, ophthalmology). This article aims to bring forward the importance of studying organizational readiness to integrate AI into health care delivery.

The reflection is based on our experience in digital health technologies, diffusion of innovations and healthcare organizations and systems. It provides insights into why and how organizational readiness should be carefully considered.

As an important step to ensure successful integration of AI and avoid unnecessary investments and costly failures, better consideration should be given to: (1) Needs and added-value assessment; (2) Workplace readiness: stakeholder acceptance and engagement; (3) Technology-organization alignment assessment and (4) Business plan: financing and investments. In summary, decision-makers and technology promoters should better address the complexity of AI and understand the systemic challenges raised by its implementation in healthcare organizations and systems.

Few studies have focused on the organizational issues raised by the integration of AI into clinical routine. The current context is marked by a perplexing gap between the willingness of decision-makers and technology promoters to capitalize on AI applications to improve health care delivery and the reality on the ground, where it is difficult to initiate the changes needed to realize their full benefits while avoiding their negative impacts.

The purpose of this paper is to investigate how and why a firm’s product market power affects its dividend policy.

This paper uses three measures of market power? The degree of import competition, Herfindahl-Hirschman index, and Lerner Index? To examine how a firm’s product market power affects its dividend policy. Further, it proposes and tests a risk-based explanation for this impact.

This paper shows that market power positively affects the dividend decision, in terms of both the probability of paying a dividend and the amount of dividend payment. It also provides evidence that the route through which market power affects the dividend decision is business risk: firms with less market power are riskier and hence less likely to pay dividends than firms with more market power.

The results show that product market power may have played an important role in reshaping dividend policy of corporate America.

This study documents the relevance of market power behind dividend policy and therefore adds to the knowledge on the relationship between product markets and corporate financial policies, which is an important and understudied area of corporate finance.

This chapter examines the increased levels of cross-asset price comovement and its relationship with the recent rounds of “extraordinary intervention” from the US Federal Reserve. The results show that, even after controlling for the preceding financial crisis, asset return volatility, investor risk perceptions, and channels of monetary stimulus, historically unrelated financial asset returns experienced abnormal changes in their conditional correlations. The strength of these cross-asset correlations is directly linked to periods of Federal Reserve interventions yet disappear when the interventions were (in fact or were perceived to be) withdrawn. Despite being studied extensively in the academic literature, no traditional intervention channels can explain the changes in cross-comovement. It is proposed that the Fed’s extraordinary stimulus caused investors to use Fed announcements as a common, low-cost information source on which they used to make common portfolio-allocation decisions. The changes in comovement during the intervention period may have reduced investor welfare for those with longer-horizon allocation strategies, those not prepared for the eventual ending of the stimulus, and for underfunded liability-optimizing portfolio managers (e.g., state pension funds).