Empirical Evidence On The Correlation Between The Exchange Rate And Romanian Exports

Few subjects of international economics are so much exposed to heated debates as the exchange rate problem. From monetary crises and balance-of-payments adjustments to monetary zones, dealing with currency swings seems to embody any economist's worries about the rightfulness of economic models and the relevance of empirical analyses he or she has to choose. Is appreciation or depreciation good for a country's welfare? Would that answer still be valid in the long run? The unsettled character of the problem largely resides in the manifest contradiction between the firm theoretical predictions and their unconvincing empirical testing. One of the least uncontroversial tenets refers to the positive correlation between currency depreciation or devaluation (although of different origins, their effects are generally the same) and a country's current account. This paper attempts to test this prediction on the case of Romanian economy and to conclude on possible explanations of the theoretical-empirical conflict.exports, exchange rate, elasticity

The Historical Evolution of Research in Accounting. National and European Context

Driven by the desire to create added value in a very generous but very difficult field of the history of accounting in Romania, and wanting to generate useful knowledge both for the academic and for the practical fields, this article aims to demonstrate the intrinsic link between economic and social development and the development of accounting through historical approach.  In this context, history is not regarded as a sequence of facts, resulting in inevitable cause-effect relationships. On the contrary, it is something we have sought to avoid at all times in order to emphasize a procedural approach in which events can be analyzed in an interpretative manner. Given that history means a chain of cause and effect facts, we must draw attention to the fact that this is an ideal situation, from which we start in order to simplify reasoning. In fact, to the extent that the causes could have been objectively known, history would not have been rewritten so many times. For this reason, we prefer to approach history as an essentially discontinuous phenomenon, following major trends, speculations being what ensures the continuity of the historical process

Leveraging Transfer Learning for Robust Multimodal Positioning Systems using Smartphone Multi-sensor Data

Indoor positioning has been widely researched in recent years due to its high demand for developing localization services and its complexity in GPS-denied environments. However, the diversity of indoor spaces and temporal variation of local conditions impose the need for building specific and periodic calibrations at high cost for deployment and maintenance of these localization systems. A robust positioning solution that overcomes these challenges is yet to be available. Previous systems achieve good performance when specializing their solution to the unique characteristics of the deployment site. The drive is now to automatically model these localization solutions on the sensor data from each site with the least amount of effort. We propose to accelerate the model adaptation to new deployment sites by using transfer learning of a multimodal deep neural network architecture. We demonstrate that the required training data is drastically reduced compared to training the model from scratch, while also boosting its accuracy, due to the additional knowledge from pretraining on other sites. The resulting model is also fault-tolerant, showing good performance in missing modalities experiment. Our research opens the way toward scalable and cost efficient localization systems

Electrochemical investigations of the Membrane-Bound [NiFe] Hydrogenase of Ralstonia eutropha

Hydrogenases are structurally complex enzymes that catalyse the production and oxidation of H2 in a wide variety of microorganisms. The catalytic properties of these enzymes are related to the interaction between their redox-active metal cofactors. O2-tolerant [NiFe] hydrogenases catalyse H2 oxidation in the presence of O2, which normally completely inhibits other hydrogenases. Their (in)activation mechanism is of fundamental importance for H2-based energy technologies. The present study has characterized the catalytic properties of the full heterotrimeric membrane bound hydrogenase (MBH) in native-like conditions by using a novel approach for immobilizing the enzyme onto the electrode. With the use of the tethered bilayer lipid membrane (tBLM) approach, the study obtained mechanistic insights relevant to the in vivo functioning of the enzyme. The MBH, inserted into the tethered lipid membrane, in equilibrium with the quinone pool, was probed in cyclic voltammetry and chronoamperometry experiments. The catalytic properties displayed at oxidizing potentials revealed that the heterotrimeric MBH undergoes anaerobic oxidative inactivation to a much smaller extent compared to the heterodimeric sub-complex, which was probed in previous protein film electrochemistry studies. In addition, the enzyme recovers after aerobic inactivation under oxidizing conditions without the application of reducing potentials. The reactivation kinetics of MBHwt and that of an MBH variant with the metal cofactor configuration of an O2-sensitive [NiFe] hydrogenase were probed under oxidative substrate-limiting conditions. The results show that the O2 sensitive mutant reactivates faster than MBHwt. This indicates that protection against oxidative damage is achieved by tuning electron transfer to the active site with the scope of preventing the formation of reactive species that would lead to irreversible inactivation

Multimodal Sensing for Robust and Energy-Efficient Context Detection with Smart Mobile Devices

Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly growing. There are already over 2 billion smartphone users worldwide [1] and the percentage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. This thesis presents how multimodal sensor data can be integrated to facilitate ro- bust and energy efficient mobile context detection, considering three important and challenging detection tasks: indoor localization, indoor-outdoor detection and human activity recognition. This thesis presents three methods for multimodal sensor inte- gration, each applied for a different type of context detection task considered in this thesis. These are gradually decreasing in design complexity, starting with a solution based on an engineering approach decomposing context detection to simpler tasks and integrating these with a particle filter for indoor localization. This is followed by man- ual extraction of features from different sensors and using an adaptive machine learn- ing technique called semi-supervised learning for indoor-outdoor detection. Finally, a method using deep neural networks capable of extracting non-intuitive features di- rectly from raw sensor data is used for human activity recognition; this method also provides higher degree of generalization to other context detection tasks. Energy efficiency is an important consideration in general for battery powered mo- bile devices and context detection is no exception. In the various context detection tasks and solutions presented in this thesis, particular attention is paid to this issue by relying largely on sensors that consume low energy and on lightweight computations. Overall, the solutions presented improve on the state of the art in terms of accuracy and robustness while keeping the energy consumption low, making them practical for use on mobile devices

Bibliometric Analysis of Fuzzy Logic Research in International Scientific Databases

The purpose of this study is to explore the Web of Science Database (WOS) and review the significant contributions to the research of Fuzzy Logic or Fuzzy Sets theory from the beginning to the present. This study analyzes the most eminent authors, institutions, countries, and journals in Fuzzy Logic research by applying science mapping methods and bibliometric measures. Also, we paid attention to link strength and h-index to represent the visibility, influence, and link between the representative authors. Moreover, we added descriptive statistics to highlight strong linearity and a connection between fuzzy publications and Fuzzy Logic research. Also, we applied regression analyses and prevision functions to predict the evolution of the Fuzzy Logic topic. The results showed a significant increase in the number of papers published annually in a portfolio of internationally representative journals. This leads us to the idea that Fuzzy Logic research is now a transdisciplinary topic that continually develops. Therefore, it can be found in more and more related areas such as artificial intelligence, IoT, medicine, economics, or the environment. Most of the results are consistent with other bibliometric studies. Still, some results are different, results related to the current cited works that show a polarization in the Asia area and the top journals that is continuously changing depending on the number of papers and the quotations of scientific personalities that publish. We used the VOS Viewer software to map the main trends in the field. The results indicate that the use of concepts has long exceeded traditional boundaries