Explore the vast range of titles available.

Nonlinear dynamical index can measure the complexity for a single time scale of the series, and when combined with coarse-grained methods, multiple time scales can be obtained to extract more information. In this study, a new coarse-grained method called refined composite variable-step multiscale (RCVM) is proposed, which obtains more subseries by setting different initial points and step lengths and thus extracts more potential information; moreover, in order to get a nonlinear dynamical index value with stronger stability, this study proposes the multimapping dispersion entropy (MDE) by averaging multiple classes of effective mapping approaches on the basis of dispersion entropy; by combining MDE and RCVM processing, RCVM-MDE is proposed to be used as a new nonlinear dynamical index, which can reflect the complexity of the series at multiple scales. The results of the four classes of chaotic simulated signals show that RCVM-MDE is not only able to detect the series nonlinear dynamic changes, but also has a very high stability; the results of three classes of real-world signals demonstrate the differentiability of RCVM-MDE compared to other commonly used entropies, as well as the best classification effect.

The article aims to estimate the relationship between synergy gains and deal size of mergers & acquisitions for Indian firms by examining deals that took place between 2005 and 2015. The paper employs principal component analysis to create a composite variable representing operating and financial synergy, followed by instrument variable 2SLS regression to estimate the relationship. The results suggest that small dealsize transactions do considerably better than large deals. The magnitude of financial synergy increases with deal size . However, large companies suffer substantial operating synergistic erosion.

Despite their increasing use, composite indicators remain controversial. The undesirable dependence of countries' rankings on the preliminary normalization stage, and the disagreement among experts/stakeholders on the specific weighting scheme used to aggregate sub-indicators, are often invoked to undermine the credibility of composite indicators. Data envelopment analysis may be instrumental in overcoming these limitations. One part of its appeal in the composite indicator context stems from its invariance to measurement units, which entails that a normalization stage can be skipped. Secondly, it fills the informational gap in the 'right' set of weights by generating flexible 'benefit of the doubt'-weights for each evaluated country. The ease of interpretation is a third advantage of the specific model that is the main focus of this paper. In sum, the method may help to neutralize some recurring sources of criticism on composite indicators, allowing one to shift the focus to other, and perhaps more essential stages of their construction.

Background Missing data are common in longitudinal studies. Multiple imputation (MI) is widely used to handle missing data. However, most of the MI methods assume various missing data types as missing at random (MAR) in imputation. Two-stage MI is a flexible method that accounts for two types of missing data in a two-step process, allowing researchers to employ diverse assumptions regarding the mechanisms underlying the missing data. This method has immense potential yet limited application and extension within the field. Methods We evaluated the performance of two-stage MI in a novel context, imputing a composite variable constructed from several continuous and binary components in the longitudinal setting while handling missing data due to MAR and missing not at random (MNAR). Additionally, we compared three fully conditional specification (FCS) methods within the two-stage MI framework. Simulation studies were conducted using a longitudinal dataset that mimicked a cohort study. Sensitivity analysis was performed with various ignorability assumptions. Results In simulation studies, the imputation models within two-stage MI, assuming appropriate ignorability assumptions, exhibited the smallest bias and achieved optimal coverage probabilities for the means, slopes across different time points, and hazard ratios for mortality related to the composite variable. The FCS methods that incorporated longitudinal information yielded the best performance in most scenarios. Conclusions In the context of a longitudinal composite variable with missing values due to various missing mechanisms, the selection of imputation methods and ignorability assumptions plays an important role within the two-stage MI framework.

In the speed control system of an Interior Permanent Magnet Synchronous Motor (IPMSM) without a speed sensor, PI controllers using only a fixed set of parameters cannot achieve accurate tracking of the estimated speed in a wide speed domain and also suffer from step response overshoot. This paper proposes a Compound Variable Structure PI (CVSPI) controller to improve the system control performance. It can choose whether to include an integral term according to the size of the system deviation to speed up the response. It also introduces a Model Reference Adaptive System (MRAS) speed observer in the controller to estimate the speed and adaptively adjust the size of the anti-integration saturation gain to improve the dynamic response following performance and immunity of the system. A feed-forward link is added for a given input differential to achieve an accurate answer to time-varying inputs. As the linear compensation matrix of the conventional MRAS is a unit matrix, the speed can only be accurately observed in a specific speed range. In this paper, a new linear compensation matrix is designed, and a new speed adaptive law is derived, allowing the improved MRAS to measure speed over a wide range accurately. Simulation results validate the excellent control performance of the CVSPI and the accuracy of the enhanced MRAS over a wide speed range.

The use of structural equation modeling (SEM) is often motivated by its utility for investigating complex networks of relationships, but also because of its promise as a means of representing theoretical concepts using latent variables. In this paper, we discuss characteristics of ecological theory and some of the challenges for proper specification of theoretical ideas in structural equation models (SE models). In our presentation, we describe some of the requirements for classical latent variable models in which observed variables (indicators) are interpreted as the effects of underlying causes. We also describe alternative model specifications in which indicators are interpreted as having causal influences on the theoretical concepts. We suggest that this latter nonclassical specification (which involves another variable type—the composite) will often be appropriate for ecological studies because of the multifaceted nature of our theoretical concepts. In this paper, we employ the use of meta-models to aid the translation of theory into SE models and also to facilitate our ability to relate results back to our theories. We demonstrate our approach by showing how a synthetic theory of grassland biodiversity can be evaluated using SEM and data from a coastal grassland. In this example, the theory focuses on the responses of species richness to abiotic stress and disturbance, both directly and through intervening effects on community biomass. Models examined include both those based on classical forms (where each concept is represented using a single latent variable) and also ones in which the concepts are recognized to be multifaceted and modeled as such. To address the challenge of matching SE models with the conceptual level of our theory, two approaches are illustrated, compositing and aggregation. Both approaches are shown to have merits, with the former being preferable for cases where the multiple facets of a concept have widely differing effects in the system and the latter being preferable where facets act together consistently when influencing other parts of the system. Because ecological theory characteristically deals with concepts that are multifaceted, we expect the methods presented in this paper will be useful for ecologists wishing to use SEM.

Chattering in composite deep-hole boring can directly affect surface processing quality and efficiency and has always been a research hotspot in machining mechanics. In this study, based on Euler–Bernoulli beam theory, the fine control equations for the cutting stability of composite variable-section boring bars were established using the Hamilton principle, in which the sectional change and internal damping of the material were considered. Next, using the Galerkin method and semi-discrete method, the effects of the taper ratio, damping ratio, length-to-diameter ratio, and ply angle on the free vibration characteristics and cutting stability were analyzed in detail. The results show that at a low damping ratio, both the first-order inherent frequency and boring stability can be enhanced with the increase in the taper ratio; at a large damping ratio, increasing the taper ratio can reduce the first-order inherent frequency and boring stability. Finally, the effects of the sectional change on the inherent frequency, displacement response, and convergence were analyzed. A numerical simulation was performed for the model reliability validation. The present research results can provide a theoretical basis and technical guidance for analyzing the cutting stability and fine control of composite variable-section boring bars with large length-to-diameter ratios.

A general theoretical and numerical framework for the strength and mass optimisation of variable-stiffness composite laminates (VSCLs) is presented in this work. The optimisation is performed in the context of the first-level problem of the multi-scale two-level optimisation strategy (MS2LOS) for VSCLs. Both the failure load maximisation problem (subject to a constraint on the mass) and the mass minimisation one (with a constraint on the VSCL strength) are solved for two benchmark structures. The effect of the presence of a constraint on the maximum tow curvature is also investigated. The solutions are searched by means of a deterministic algorithm by considering different scenarios in terms of the VSCL macroscopic behaviour: the orthotropy type and shape, the direction of the main orthotropy axis and the thickness of the laminate are tailored either globally (uniform over the structure) or locally. The polar method is used to represent the point-wise elastic response of the VSCL at the macroscopic scale. The distributions of the polar parameters and of the thickness are described through B-spline entities: their properties are exploited in computing physical and geometrical response functions of the VSCL as well as their gradient. The VSCL strength at the macroscopic scale is assessed using a laminate-level failure criterion in the space of polar parameters. Numerical results show considerable improvements with respect to both quasi-homogeneous isotropic structures and an optimised VSCL solution taken from the literature obtained by using the design approach based on lamination parameters. These results confirm the effectiveness of the proposed strategy and the great potential of VSCLs.

Along with an increase in the level of societies’ wealth, factors such as the state of health, the quality of education and negative output effects including environment quality are becoming increasingly important in assessing the standard of living and well‑being of the average person. A category that has long been used to measure the economic and social well‑being of societies is GDP per capita. However, in contemporary research, other attempts, more comprehensively describing important aspects of life, are being proposed. The main aim of this article is to examine the standard of living in NUTS–4 districts in Poland in 2020 in aggregate and in its particular dimensions. Spatial differentiation of the standard of living index and sub‑indices describing its individual dimensions was also examined. The standard of living was measured on the basis of a composite variable. This variable was constructed as Hellwig’s measure of economic development on the basis of values of partial indicators describing successive dimensions. Those indicators were determined as arithmetic means of normalised diagnostic variables. The highest standard of living is observed in cities with powiat status. Among them, there are both the largest agglomerations and smaller cities constituting local centres. In the spatial distribution of the standard of living measure, attention is drawn to the large concentration of districts with the lowest values in the north‑east of Poland, in the Kujawy Region and in the south‑east. Partial indicators describing the dimensions of the standard of living, constructed for the purposes of the study, reflect the situation with regard to the degree of implementation of detailed tasks of social policy. The analysis of the situation of districts in particular dimensions of the standard of living carried out in this paper makes it possible to indicate the districts that require the greatest attention of decision‑makers and to direct the greatest resources to them.

Identifying and clearly communicating the drivers of ecosystem function is a crucially important goal for both basic and applied ecology. This has proven difficult because the putative causes (e.g., environment, species identity, biodiversity, and functional traits) are numerous and correlated. The problem is exacerbated by a lack of a formal framework for unambiguously relating theoretical language to precise, quantitative expressions of that language. Using a formal framework for the graphical expression of complex causal hypotheses, we developed a causal diagram of the concepts required to comprehensively test whether hypothesized sets of functional traits mediate the relationship between community structure and ecosystem function. We then used causal analysis, simulations, and field data to develop and test analytical strategies for understanding how community structure influences ecosystem functions via functional traits. Formal causal analysis showed that biodiversity–ecosystem function correlations are noncausal associations. Using simulations, we showed how biodiversity correlations and species identity effects can arise from misspecification or incomplete mediation by functional trait composites. We also found that different types of model misspecification result in different patterns of residuals, which may be used to diagnose gaps in functional trait hypotheses. Treating the model misspecifications eliminated associations between species identity or biodiversity and ecosystem function. Finally, we provide an example of the analysis of field data to demonstrate how to use these insights to conduct a research program that has the goal of understanding the mechanistic trait relationships that link community structure to ecosystem function.