Real Wages and Labor-saving Technical Change: Evidence from a Panel of Manufacturing Industries in Mature and Labor-surplus Economies

This paper uses panel cointegration and error correction models to unveil the direction of long-run causality between the real product wage and labor productivity at the industry level. I use two datasets of manufacturing industries: the EU-Klems dataset covering 11 industries in 19 developed economies, and the Unido Industrial Statistics Database covering 22 industries in 30 developed and developing economies. In both datasets, I find evidence of cointegration between the two variables, as well as evidence of two-way, long-run Granger causality. These findings are consistent with theories of directed technical change, which claim that a rise in labor costs sparks the adoption of labor-saving innovations. They are also consistent with distributive theories whereby real wages keep apace of labor productivity growth, giving rise to long-run stability in functional distribution

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

Proteomic Investigation of Murine Neuronal α7-Nicotinic Acetylcholine Receptor Interacting Proteins

The α7-nicotinic acetylcholine receptor (α7-nAChR) is a ligand-gated ion channel that is expressed widely in vertebrates and is the principal high-affinity α-bungarotoxin (α-bgtx) binding protein in the mammalian CNS. α7-nAChRs associate with proteins that can modulate its properties. The α7-nAChR interactome is the summation of proteins interacting or associating with α7-nAChRs in a protein complex. To identify an α7-nAChR interactome in neural tissue, we isolated α-bgtx-affinity protein complexes from wild-type and α7-nAChR knockout (α7 KO) mouse whole brain tissue homogenates using α-bgtx-affinity beads. Affinity precipitated proteins were trypsinized and analyzed with an Orbitrap Fusion mass spectrometer. Proteins isolated with the α7-nAChR specific ligand, α-bgtx, were determined to be α7-nAChR associated proteins. The α7-nAChR subunit and 120 additional proteins were identified. Additionally, 369 proteins were identified as binding to α-bgtx in the absence of α7-nAChR expression, thereby identifying nonspecific proteins for α7-nAChR investigations using α-bgtx enrichment. These results expand on our previous investigations of α7-nAChR interacting proteins using α-bgtx-affinity bead isolation by controlling for differences between α7-nAChR and α-bgtx-specific proteins, developing an improved protein isolation methodology, and incorporating the latest technology in mass spectrometry. The α7-nAChR interactome identified in this study includes proteins associated with the expression, localization, function, or modulation of α7-nAChRs, and it provides a foundation for future studies to elucidate how these interactions contribute to human disease

On the Higgs spectra of the 3-3-1 model

The minimal scalar sector of the 3-3-1 model is composed by the SU(3)$_L$ triplet scalars $\eta$, $\rho$, $\chi$ and its potential allows the trilinear term $\frac{f}{\sqrt{2}}\chi \eta \rho$. Since $f$ is an energy scale associated to the explicit violation of Peccei-Quinn global symmetry, it is natural to consider in what energy scale such symmetry is broken and its consequences in the spectrum of scalars of the model. here, We show that $f$ determines the spectrum of scalars of the model. Hence, we develop the scalar sector considering $f$ belonging to four energy regimes, namely $f \ll \langle \eta \rangle_0$, $\langle \rho \rangle_0$; $f= \langle \eta \rangle_0$, $\langle \rho \rangle_0$; $f=\langle \chi \rangle_0$ and $f \gg \langle \chi \rangle_0$ and obtain the spectrum of scalars for each case. In the first and second cases the spectrum of scalars presents a set of new scalars belonging to the electroweak scale, while in the third case all new scalars belong to the 3-3-1 scale and the fourth case all the new scalars have masses lying at $f$ scale. All cases have a neutral CP-even scalar mimicking the standard Higgs

High-speed Fbg Interrogation System Insensitive To Fiber Link Attenuation For Magnetic Field Sensing

A high-speed FBG interrogation method for magnetic field sensing is proposed. A FBG attached to a magnetostrictive material (Terfenol-D) was used to show the output invariance when increasing the attenuation on the optical link. This was achieved by computing the ratio between the sensing and the reference signals, both generated using different DFB lasers properly tuned. The output remained invariant to attenuations up to 12 dB. Also, the system's interrogation speed was tested and compared to a commercial solution. While the commercial model was limited by its 6 kHz sampling frequency, this method provided responses up to 60 kHz.963

Fault-Tolerance in the Scope of Cloud Computing

Fault-tolerance methods are required to ensure high availability and high reliability in cloud computing environments. In this survey, we address fault-tolerance in the scope of cloud computing. Recently, cloud computing-based environments have presented new challenges to support fault-tolerance and opened new paths to develop novel strategies, architectures, and standards. We provide a detailed background of cloud computing to establish a comprehensive understanding of the subject, from basic to advanced. We then highlight fault-tolerance components and system-level metrics and identify the needs and applications of fault-tolerance in cloud computing. Furthermore, we discuss state-of-the-art proactive and reactive approaches to cloud computing fault-tolerance. We further structure and discuss current research efforts on cloud computing fault-tolerance architectures and frameworks. Finally, we conclude by enumerating future research directions specific to cloud computing fault-tolerance development.publishe

An empirical analysis of Minsky regimes in the US economy

In this paper we analyze Minskian dynamics in the US economy via an empirical application of Minsky’s financing regime classifications to a panel of nonfinancial corporations. First, we map Minsky’s definitions of hedge, speculative and Ponzi finance onto firm-level data to describe the evolution of Minskian regimes. We highlight striking growth in the share of Ponzi firms in the post-1970 US, concentrated among small corporations. This secular growth in the incidence of Ponzi firms is consistent with the possibility of a long wave of increasingly fragile finance in the US economy. Second, we explore the possibility of short-run Minskian dynamics at a business-cycle frequency. Using linear probability models relating firms’ probability of being Ponzi to the aggregate output gap, which captures short-term macroeconomic fluctuations exogenous to individual firms, we find that aggregate downturns are correlated with an al- most zero increased probability that firms are Ponzi. This result is corroborated by quantile regressions using a continuous measure of financial fragility, the interest coverage ratio, which identify almost zero effects of short-term fluctuations on financial fragility across the interest coverage distribution. Together, these results speak to an important question in the theoretical literature on financial fragility regarding the duration of Minskian cycles, and lend support, in particular, to the contention that Minskian dynamics may take the form of long waves, but do not operate at business cycle frequencies

The Empirical Analysis of Minsky Regimes in the U.S. Economy

In this paper we analyze Minskian dynamics in the US economy via an empirical application of Minsky’s financing regime classifications to a panel of nonfinancial corporations. First, we map Minsky’s definitions of hedge, speculative and Ponzi finance onto firm-level data to describe the evolution of Minskian regimes. We highlight striking growth in the share of Ponzi firms in the post-1970 US, concentrated among small corporations. This secular growth in the incidence of Ponzi firms is consistent with the possibility of a long wave of increasingly fragile finance in the US economy. Second, we explore the possibility of short-run Minskian dynamics at a business-cycle frequency. Using linear probability models relating firms’ probability of being Ponzi to the aggregate output gap, which captures short-term macroeconomic fluctuations exogenous to individual firms, we find that aggregate downturns are correlated with an almost zero increased probability that firms are Ponzi. This result is corroborated by quantile regressions using a continuous measure of financial fragility, the interest coverage ratio, which identify almost zero effects of short-term fluctuations on financial fragility across the interest coverage distribution. Together, these results speak to an important question in the theoretical literature on financial fragility regarding the duration of Minskian cycles, and lend support, in particular, to the contention that Minskian dynamics may take the form of long waves, but do not operate at business cycle frequencies

A Note on Properties in Multi-Level Modeling

Given their ubiquity in conceptual modeling languages, it is no surprise that properties have been subject to attention and specialized support in multi-level modeling approaches (including mechanisms such as deep characterization). This paper examines consequences of a typology for properties of high-order types that distinguishes them into: direct, resultant and regularity properties. We discuss several implications of the proposed classification considering a number of aspects of multi-level modeling including: specialization between high-order types, applicability to powertype variants, property change, and potency.</p