Endogenous Fiscal Policies, Environmental Quality, and Status-Seeking Behavior.

This paper analyzes endogenous fiscal policy and public decision in an endogenous growth model where agents care about social status and environmental quality. The quest for a higher status is assimilated to a preference for capital wealth. The government uses income tax to finance infrastructure and environmental protection, and maximizes individual welfare. We find that accounting for preferences for social status and environmental quality may lead to an allocation of tax revenue in favor of cleanup effort to the detriment of infrastructure. It does not necessary have a negative impact on growth. Status seeking can however harm economic growth and environmental quality when its motive is important enough. Finally, we show that economic growth is consistent with environmental preservation but is not necessarily welfare-improving as in the case of absence of status-seeking behavior.Endogenous policy; endogenous growth; environmental quality; status-seeking; public expenditure; Wagner's law.

Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces

This paper presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time.Comment: 8 pages, 8 figure

Income distribution dynamics across European regions

We use two datasets to study the convergence process across European regions. Relying on Quah (1966a,1997), we examine the dynamics of income distribution and find evidence of polarization whatever the time horizon considered. Regions whose incomes were close together at an initial period transit subsequently to widely different income levels.distribution dynamics

Demand and equilibrium with inferior and Giffen behaviors

We introduce a class of differentiable, strictly increasing, strictly concave utility functions exhibiting an explicit demand of a good which may have Giffen behavior. We provide a necessary and sufficient condition (bases on prices and consumers’ preferences and income) under which this good is normal, inferior or Giffen good. Interestingly, with this utility, the equilibrium price of a good may increase in the aggregate supply for this good

Hartman-Stampacchia theorem, Gale-Nikaido-Debreu lemma, and Brouwer and Kakutani fixed-point theorems

This paper uses the Hartman-Stampacchia theorems as primary tool to prove the Gale-Nikaido-Debreu lemma. It also establishes a full equivalence circle among the Hartman Stampacchia theorems, the Gale-Nikaido-Debreu lemmas, and Kakutani and Brouwer fixed point theorems

Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks