Rescuing Quartic and Natural Inflation in the Palatini Formalism

When considered in the Palatini formalism, the Starobinsky model does not provide us with a mechanism for inflation due to the absence of a propagating scalar degree of freedom. By (non)--minimally coupling scalar fields to the Starobinsky model in the Palatini formalism we can in principle describe the inflationary epoch. In this article, we focus on the minimally coupled quartic and natural inflation models. Both theories are excluded in their simplest realization since they predict values for the inflationary observables that are outside the limits set by the Planck data. However, with the addition of the $R^2$ term and the use of the Palatini formalism, we show that these models can be rendered viable.Comment: JCAP accepted version, 16 pages, 7 figure

A proposed framework of an interactive semi-virtual environment for enhanced education of children with autism spectrum disorders

Education of people with special needs has recently been considered as a key element in the field of medical education. Recent development in the area of information and communication technologies may enable development of collaborative interactive environments which facilitate early stage education and provide specialists with robust tools indicating the person's autism spectrum disorder level. Towards the goal of establishing an enhanced learning environment for children with autism this paper attempts to provide a framework of a semi-controlled real-world environment used for the daily education of an autistic person according to the scenarios selected by the specialists. The proposed framework employs both real-world objects and virtual environments equipped with humanoids able to provide emotional feedback and to demonstrate empathy. Potential examples and usage scenarios for such environments are also described

Use of mine planning software for the evaluation of resources and reserves of a sedimentary nickel deposit

Η χρήση λογισμικού μεταλλευτικού σχεδιασμού για την αξιολόγηση κοιτασμάτων και την εκτίμηση ορυκτών πόρων και αποθεμάτων είναι διαδεδομένη και θεωρείται προϋπόθεση για την αναφορά αποθεμάτων με βάση διεθνή πρότυπα και κώδικες αναφοράς. Οι βασικές αρχές λειτουργίας και εφαρμογής αυτών των κωδικών είναι η διαφάνεια του αναφερόμενου υλικού, η σχετικότητα των αναφερόμενων πληροφοριών (ουσιαστικότητα) και η επάρκεια των ατόμων που εμπλέκονται στην παραγωγή των αναφορών. Η εφαρμογή ειδικού λογισμικού μεταλλευτικού σχεδιασμού που περιγράφεται στην εργασία αυτή λαμβάνει χώρα στα λειτουργικά μεταλλεία ιζηματογενών κοιτασμάτων νικελίου στην Κεντρική Εύβοια. Περιλαμβάνει όλα τα στάδια της επεξεργασίας ερευνητικών δεδομένων συμπεριλαμβανομένης της εισαγωγής, ανάλυσης, επεξεργασίας και μοντελοποίησης των δεδομένων, της γεωστατιστικής εκτίμησης των ποσοτήτων και ποιοτήτων των ορυκτών πόρων και αποθεμάτων, και το σχεδιασμό, βελτιστοποίηση και προγραμματισμό των μεταλλευτικών δραστηριοτήτων.The use of mine planning software in the evaluation of deposits and estimation of mineral resources and reserves has become widespread and is considered a prerequisite for the reporting of reserves according to international reporting standards and codes. The main principles of operation and application of these codes are transparency of reported material, the relevance of reported information (materiality) and the competence of people involved in producing the reports. The application of specialised mine planning software described in this paper currently takes place at operational mines of sedimentary nickel deposits in Central Evia, Greece. It involves all stages of processing of exploration data including entry, analysis, processing and modelling of data, geostatistical estimation of quantities and qualities of mineral resources and mineral reserves, and the design, optimisation and scheduling of mining operations

Optimal Excitation Controller Design for Wind Turbine Generator

An optimal excitation controller design based on multirate-output controllers (MROCs) having a multirate sampling mechanismwith different sampling period in each measured output of the system is presented. The proposed H∞ -control techniqueis applied to the discrete linear open-loop system model which represents a wind turbine generator supplying an infinite busthrough a transmission line

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 fault prognosis experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the prognosis phase once they got exposed to real-world data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.Comment: 25 Pages, 13 Figures, 5 Table