The role of cross-listing, foreign ownership and state ownership in dividend policy in an emerging market

AbstractIn this paper, we investigate if dividend policy is influenced by ownership type. Within the dividend literature, dividends have a signaling role regarding agency costs, such that dividends may diminish insider conflicts (reduce free cash flow) or may be used to extract cash from firms (tunneling effect) – which could be predominant in emerging markets. We expect firms with foreign ownership and those that are listed in overseas markets to have different dividend policies and practices than those that are not, and firms with more state ownership and less individual ownership to be more likely to pay cash dividends and less likely to pay stock dividends. Using firms from an emerging economy (China), we examine whether these effects exist in corporate dividend policy and practice. We find that both foreign ownership and cross-listing have significant negative effects on cash dividends, consistent with the signaling effect and the notion of reduced tunneling activities for firms with the ability to raise capital from outside of China. Consistent with the tunneling effect, we find that firms with higher state ownership tend to pay higher cash dividends and lower stock dividends, while the opposite is true for public (individual) ownership. Further analysis shows that foreign ownership mediates the effect of state ownership on dividend policy. Our results have significant implications for researchers, investors, policy makers and regulators in emerging markets

Large eddy simulation of an ethylene–air turbulent premixed V-flame

AbstractLarge eddy simulation (LES) using a dynamic eddy viscosity subgrid scale stress model and a fast-chemistry combustion model without accounting for the finite-rate chemical kinetics is applied to study the ignition and propagation of a turbulent premixed V-flame. A progress variable c-equation is applied to describe the flame front propagation. The equations are solved two dimensionally by a projection-based fractional step method for low Mach number flows. The flow field with a stabilizing rod without reaction is first obtained as the initial field and ignition happens just upstream of the stabilizing rod. The shape of the flame is affected by the velocity field, and following the flame propagation, the vortices fade and move to locations along the flame front. The LES computed time-averaged velocity agrees well with data obtained from experiments

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519

Region graph partition function expansion and approximate free energy landscapes: Theory and some numerical results

Graphical models for finite-dimensional spin glasses and real-world combinatorial optimization and satisfaction problems usually have an abundant number of short loops. The cluster variation method and its extension, the region graph method, are theoretical approaches for treating the complicated short-loop-induced local correlations. For graphical models represented by non-redundant or redundant region graphs, approximate free energy landscapes are constructed in this paper through the mathematical framework of region graph partition function expansion. Several free energy functionals are obtained, each of which use a set of probability distribution functions or functionals as order parameters. These probability distribution function/functionals are required to satisfy the region graph belief-propagation equation or the region graph survey-propagation equation to ensure vanishing correction contributions of region subgraphs with dangling edges. As a simple application of the general theory, we perform region graph belief-propagation simulations on the square-lattice ferromagnetic Ising model and the Edwards-Anderson model. Considerable improvements over the conventional Bethe-Peierls approximation are achieved. Collective domains of different sizes in the disordered and frustrated square lattice are identified by the message-passing procedure. Such collective domains and the frustrations among them are responsible for the low-temperature glass-like dynamical behaviors of the system.Comment: 30 pages, 11 figures. More discussion on redundant region graphs. To be published by Journal of Statistical Physic

Sudden switch of generalized Lieb-Robinson velocity in a transverse field Ising spin chain

The Lieb-Robinson theorem states that the speed at which the correlations between two distant nodes in a spin network can be built through local interactions has an upper bound, which is called the Lieb-Robinson velocity. Our central aim is to demonstrate how to observe the Lieb-Robinson velocity in an Ising spin chain with a strong transverse field. We adopt and compare four correlation measures for characterizing different types of correlations, which include correlation function, mutual information, quantum discord, and entanglement of formation. We prove that one of correlation functions shows a special behavior depending on the parity of the spin number. All the information-theoretical correlation measures demonstrate the existence of the Lieb-Robinson velocity. In particular, we find that there is a sudden switch of the Lieb-Robinson speed with the increasing of the number of spin

Exact diagonalization of the generalized supersymmetric t-J model with boundaries

We study the generalized supersymmetric $t-J$ model with boundaries in three different gradings: FFB, BFF and FBF. Starting from the trigonometric R-matrix, and in the framework of the graded quantum inverse scattering method (QISM), we solve the eigenvalue problems for the supersymmetric $t-J$ model. A detailed calculations are presented to obtain the eigenvalues and Bethe ansatz equations of the supersymmetric $t-J$ model with boundaries in three different backgrounds.Comment: Latex file, 32 page

Spin squeezing and pairwise entanglement for symmetric multiqubit states

We show that spin squeezing implies pairwise entanglement for arbitrary symmetric multiqubit states. If the squeezing parameter is less than or equal to 1, we demonstrate a quantitative relation between the squeezing parameter and the concurrence for the even and odd states. We prove that the even states generated from the initial state with all qubits being spin down, via the one-axis twisting Hamiltonian, are spin squeezed if and only if they are pairwise entangled. For the states generated via the one-axis twisting Hamiltonian with an external transverse field for any number of qubits greater than 1 or via the two-axis counter-twisting Hamiltonian for any even number of qubits, the numerical results suggest that such states are spin squeezed if and only if they are pairwise entangled.Comment: 6 pages. Version 3: Small corrections were mad

Effects of plasmon excitation on photocatalytic activity of Ag/TiO 2 and Au/TiO2 nanocomposites

Model nanocomposite photocatalysts consisting of undoped TiO2 films with optically active Ag or Au nanoparticles (NPs) were designed, fabricated, and examined to address the role of plasmon excitations in their performance. Different composition configurations were tested in which the NPs were either facing the reaction environment or not, and in direct contact or not with TiO2. We found, as measured for the reactions of methanol and ethylene oxidation in two different photoreactors, that composites always show enhanced activity (up to x 100 for some configurations) compared to bare TiO2. We deduced from in situ localized surface plasmon resonance spectroscopy measurements that the interfacial charge transfer from TiO2 to NPs plays a major role in the activity enhancement for composite configurations where particles are in direct contact with TiO2. Plasmonic near- and far-field effects were only observed when the plasmon resonance energy overlaps with the bandgap energy of undoped TiO2. (C) 2013 Elsevier Inc. All rights reserved

Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods

In product design, various methodologies have been proposed for market segmentation, which group consumers with similar customer requirements into clusters. Central points on market segments are always used as ideal points of customer requirements for product design, which reflects particular competitive strategies to effectively reach all consumers’ interests. However, existing methodologies ignore the fuzziness on consumers’ customer requirements. In this paper, a new methodology is proposed to perform market segmentation based on consumers’ customer requirements, which exist fuzziness. The methodology is an integration of a fuzzy compression technique for multi-dimension reduction and a fuzzy clustering technique. It first compresses the fuzzy data regarding customer requirements from high dimensions into two dimensions. After the fuzzy data is clustered into marketing segments, the centre points of market segments are used as ideal points for new product development. The effectiveness of the proposed methodology in market segmentation and identification of the ideal points for new product design is demonstrated using a case study of new digital camera design