Abstract: The article proposes to consider the possibility of connecting the problems of engineering synthesis of frequency control systems for asynchronous electric drives with the basic provisions of the theory of identification of asynchronous electric motors based on the equations of a generalized AC electric machine.The article presents the results of experimental studies of load parrying processes in asynchronous electric drives with vector and scalar controls. These results indicate the absence of fundamental advantages as processes in a drive with vector control. This advantage should have manifested itself in a more efficient formation of the moment and fast transients. It is suggested that the reason for this is in too significant errors, assumptions and simplifications of the equations adopted in the derivation of vector control and which are its theoretical basis. A method is proposed for describing asynchronous electric motors by nonlinear transfer functions, which made it possible to formulate the principle of correction of electric drives and a method for assessing their efficiency.The article shows. that the correction based on the proposed nonlinear transfer functions of the induction motor is much more efficient than the traditional vector control, which was confirmed by detailed experiments and modeling. The results of the most important of which are given in the article. An assumption was made. that the advantage in efficiency is due to a more accurate identification of the dynamics of an asynchronous electric motor with a gear function instead of vector equations.

Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; this way, motor diagnostics is an issue that assumes great importance. To prevent their failures and timely face the considered service outages, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on Artificial Neural Network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminary validated on a set of 28 electric motors, and its performance is compared with common state-of-art machine learning techniques. The obtained results show that the proposed methodology is able to reach accuracy levels greater than 98\% in identifying anomalous conditions of three-phase asynchronous motors.

The stringency of GMO regulation affects trade of agricultural products among countries. On that account, our investigation attempts to shed the light on the complexity of the impact of genetically modified organisms (GMO) regulations among countries on bilateral trade with a focus on GMO approvals. We develop a framework extending Xiong and Beghin (2014) and their decomposition of export supply and imports demand effects. Our approach encompasses the supplemental effect of GMO regulation laxity in production on the exporter’s productivity. It decomposes three effects that impact bilateral trade flows between trade partners: productivity in the source country, sorting cost from bilateral dissimilarity in regulations, and stringency impact on import demand. We estimate the model using a panel dataset of corn trade and two econometric approaches (PPML, Heckman sample-selection). We find that GMO laxity in production of exporters has the most prominent and robust effect of enhancing bilateral trade of corn. The effect of GMO laxity in demand appears to be smaller than the export booster effect of GMO adoption. Finally, bilateral dissimilarity in regulations does not appear to matter, once we account for the impact of GMO in production of the exporters and laxity in demand differentiated for importer and exporters. Hence, GMO approval regulations have dominating multilateral effects rather than bilateral ones.

Multiaxial fatigue experiments under asynchronous loadings with four different loading frequency ratios were carried out on 30CrMnSiA steel. The experimental results show that the fatigue life decreases when the axial or torsion frequency increases from 1 to 2, while there is no significant change when the axial or torsion frequency increases from 2 to 4. The surface crack paths are observed and show that cracks initiate on the maximum shear stress amplitude planes, propagate approximately tens of microns, and then turn to propagate along the maximum normal stress planes. The number of secondary cracks increases when the axial or torsion frequency increases. Subsequently, the Bannantine-Socie and Wang-Brown cycle counting methods along with various multiaxial fatigue criteria and Palmgren-Miner’s cumulative damage rule were used for fatigue life prediction. The experimental results are consistent with the fatigue life predicted by the Bannantine-Socie method with the section critical plane criterion for 30CrMnSiA steel under asynchronous loading paths.

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption — a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified the need to react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ''events''. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches - developed in different research projects - to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.