On the impact of exchange rate regimes on tourism

The main objective of this paper is to analyze the effect of the exchange rate arrangements on international tourism. The ambiguity of literature about the effect of exchange rate volatility contrasts with the magnitude of the impact of a common currency on trade. On the basis of a gravity equation we estimate a moderate effect of a currency union on tourism of almost 12%. Furthermore, we estimate a gravity equation for international trade, obtaining that the common currency effect on trade is reduced when tourism is introduced as a regressor. This suggests that tourism flows may contribute to explain the excessive magnitude of the estimated effect of a common currency on trade in this literature. Finally, we analyze the impact of several de facto exchange rate arrangements on tourism, finding that less flexible exchange rates promotes tourism flows.Tourism, Exchange Rate Regime, Common Currency

Relativistic model of hidden bottom tetraquarks

The relativistic model of the ground state and excited heavy tetraquarks with hidden bottom is formulated within the diquark-antidiquark picture. The diquark structure is taken into account by calculating the diquark-gluon vertex in terms of the diquark wave functions. Predictions for the masses of bottom counterparts to the charm tetraquark candidates are given.Comment: 6 page

Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks

This research was suported by the ``Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación'' of Spain and the European Regional Development Fund [grant number PID2021-124486NB-I00].Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.Ministerio de Ciencia e Innovación MICINNEuropean Regional Development Fund PID2021-124486NB-I00 ERDFAgencia Estatal de Investigación AE

Nosocomial Infections Caused by Drug-Resistant Bacteria in a Referral University Hospital, Tehran, Iran

Background: The emergence of antimicrobial-resistant pathogens associated with hospital-acquired infections (HAIs) is a major public health problem worldwide. Although being drug resistance is common in some countries and rare in others, the extent of this condition is not precisely known in most parts of Iran.Materials and Methods: Clinical specimens from patients who had been in the hospital for at least 48 hours were included in this study. The pattern of antibiotic resistance was determined by disk diffusion method as recommended by the Clinical Laboratory and Standards Institute (CLSI).Results: Of 11164 patients that were investigated, 369 (3.3%) had nosocomial infections. The most frequently isolated organisms from all sites of infections were Acinetobacter species (14.2%), Escherichia coli (13.7%) and Pseudomonas aeruginosa (9.9%). Among the Gram-negative bacilli, Acinetobacter spp was mostly resistant to ciprofloxacin, ceftriaxon, co-trimoxazole and centamicin, while P. aeruginosa was frequently resistant to ampicillin/sulbactam (87%). Imipenem and piperacillin/tazobactam were the most active antimicrobials against gram-negative microorganisms whereas vancomycin was the antimicrobial agent most consistently active against the Gram-positive cocci.Conclusions: This study highlights the importance of antimicrobial-resistant pathogens associated with nosocomial infection in Tehran, Iran. Using proper diagnostic criteria as well as administering more effective treatment may limit the frequency of drug-resistant bacteria associated with HAIs.

Pathways to Greener Pastures: Research Opportunities to Integrate Life Cycle Assessment and Sustainable Business Process Management Based on a Systematic Tertiary Literature Review

Sustainable Business Process Management (BPM) is a research field that aims to improve the sustainability performance of organizations’ operations. With its focus on business processes, it has the potential to bring sustainability considerations from external reporting to the core of organizations. We present a systematic tertiary literature study to provide a catalog of existing literature reviews and primary work and to give a consolidated overview of the state and research needs of the field. We find that Sustainable BPM research has focused on modeling approaches and most of the work so far is largely conceptual, with a limited sustainability perspective. Based on these findings, we propose an integration of BPM and Life Cycle Assessment (LCA), an established and rigorous method for sustainability analysis. We present research opportunities to show how both disciplines can synergize and leverage methods and techniques for business process automation and innovation to effectively improve the sustainability performance of organizations

Study of space environment effects on thermal control coatings - Dependence of thermal control coating degradation upon electron energy Final report

Hemispherical spectral reflectance in vacuum for thermal coating degradation using electron energ

Distributed Monte Carlo Simulation

Monte Carlo simulation is an effective way to analyze models of sophisticated problems, but often suffers from high computational complexity. Distributed computing is an effective technology that can be used for compute-intensive applications, such as Monte Carlo simulation. The goal of this thesis is to combine the concepts of Monte Carlo simulation and distributed computing in an effort to develop an efficient system capable of rapidly executing computationally-demanding simulations.;When distributed computing is used to support the simulations of multiple users, a scheduling algorithm is required to allocate resources among the users\u27 jobs. In this thesis, a scheduling algorithm is developed that is suitable for Monte Carlo simulation and utilizes the available distributed-computing resources. The unified framework for scheduling is capable of accommodating classic scheduling algorithms such as equal job share, first-in first-out (FIFO), and proportional fair scheduling. The behavior of the scheduler can be controlled by just three parameters. By choosing appropriate parameter values, individual users and their jobs can be assigned different priorities. By introducing an appropriate analytical model, the role of these parameters on system behavior is thoroughly investigated. Using insights obtained by studying the analytical model, a complete distributed Monte Carlo system is designed and presented as a case study