Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy and environment in the transition economies

* List of Illustrations * Preface * List of Acronyms * 1. Energy and Change * At Stake * After the Fall * Reform and Energy Intensity * Conclusions * 2. End-Use Efficiency * Buildings * Industry * Transportation * Technology Cost Analysis * Poland * Czech Republic * Russia * Ukraine * Other Countries * Frontier Financing * Conclusions * 3. Nuclear Safety and Security * Soviet Reactor Technology * Aiding Nuclear Power * Civilian Nuclear Security and the Military * Conclusions * 4. Petroleum Geoeconomics * Shifting Caspian Fortunes * The Caspian * Russian Gas * Pipeline Geopolitics * Reform * Environment * Conclusions * 5. Coal and Reform * Coal's Decline * Mining Technology * Coal Pollution * Reform * Conclusions * 6. Utility Monopoly and Competition * Highly Charged Reform * District Heating * Advanced Power Technology and Generation * Conclusions * 7. Environmental and Climatic Change * Elements of Climate Change * The Euro-Siberian Climate * Energy Projections, Climate Forecasts * Beyond Kyoto * Conclusions * 8. The Second Generation of Reform * End of the Beginning * Reforming Aid to Reform * The Next Generation * Appendix * Index
Large-Scale Inverse Reinforcement Learning via Function Approximation

This paper introduces a new method for inverse reinforcement learning in large-scale state space. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function to maximize the likelihood of the observed motion. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle large state space efficiently. We test the proposed method in a simulated environment, and show that it is more accurate than existing methods and significantly better in scalability. We also apply the method to evaluating the effect of rehabilitative stimulations on patients with spinal cord injuries based on the observed patient motions.
De-scripting a Museum’s Presence and Atmosphere: An Exhibition Experiment

The aim of this article is to study ways of highlighting presence and atmosphere in a concrete museum setting by means of H.U. Gumbrecht’s presence theory, Gernot Böhme’s definition of atmosphere and Madeleine Akrich’s and Bruno Latour’s script theory. The article is based on an exhibition experiment conducted at Faaborg Museum in Denmark where an artwork was exhibited in a temporary room with a door inviting visitors to enter one by one. The experiment was an attempt to highlight presence and atmosphere by de-scripting the museum’s script. Twenty-one qualitative visitor interviews show that the enclosure was successful in turning the visitors’ attention to the atmosphere of the museum space and the artwork itself. A significant number of visitors described in their own words their experience as a state of presence and these experiences were closely linked with the physical and social space of the museum.
Proximal type algorithms involving linesearch and inertial technique for split variational inclusion problem in hilbert spaces with applications

ABSTRACT In convex optimization, numerous problems in applied sciences can be modelled as the split variational inclusion problem (SVIP). In this connection, we aim to design new and efficient proximal type algorithms which are based on the inertial technique and the linesearches terminology. We then discuss its convergence under some suitable conditions without the assumption on the operator norm. We also apply our main result to the split minimization problem, the split feasibility problem, the relaxed split feasibility problem and the linear inverse problem. Finally, we provide some numerical experiments and comparisons to these problems. The obtained result mainly improves the recent results investigated by Chuang.
Experiments on the Frascati Tokamak Upgrade with a liquid tin limiter

A capillary porous liquid tin limiter (TLL) was exposed as a plasma-facing component (PFC) in the Frascati Tokamak Upgrade. The TLL was progressively inserted deeper into the scrape-off-layer, very close to the last closed magnetic surface (<0.5 cm). Spectroscopic measurements, a fast IR camera, and Langmuir probes monitored the evolution of tin emission, tin surface temperature, and heat loads on the limiter. The surface temperature rose up to 1700 °C in the hottest limiter region, a value for which tin evaporation is very high. Heat loads in excess of 18 MW m−2 were withstood by the TLL. Numerical simulations performed with the ANSYS code were in agreement with the experimental data, taking into account the heat load deposition profile on the TLL. As long as the surface temperature of the tin limiter is below 1300 °C, the main tin production mechanism is sputtering and the presence of such impurity in the discharge is negligible. When evaporation becomes dominant beyond 1300 °C, tin is the main impurity present in the plasma. Nevertheless, the concentration of tin is on the order of 5 × 10−4 of the electronic density, and no degradation in plasma performance has been observed. These results are an important experimental confirmation of the possibility to use liquid metals as a PFC solution to the power exhaust for a fusion power plant.
Conversor CA-CC trifásico de único estágio, bidirecional, isolado em alta frequência, com correção de fator de potência

Este trabalho propoe o estudo e implementacao de um conversor CA-CC bidirecional trifasico de unico estagio, isolado em alta frequencia com correcao de fator de potencia. Baseado no conversor dual active bridge (DAB) e associado com a celula de comutacao de tres estados (CC3E), pode-se afirmar que esta topologia e adequada para aplicacoes como fontes de telecomunicacao operando em 380 V - 400 V. Outra aplicacao interessante para a topologia proposta reside nas novas concepcoes de geracao distribuida e smart grids, pois esse conversor pode conectar a rede eletrica trifasica a outras fontes/cargas e controlar o fluxo de potencia entre as mesmas. Uma analise matematica baseada no modelo fundamental e apresentada, na qual se verifica que a tensao efetiva aplicada na ponte H se aproxima daquela obtida com a tecnica AM-DSB (Amplitude Modulation with Double Side-Band – Modulacao em Amplitude com Banda Lateral Dupla). Dessa forma, desenvolve-se a analise de perdas e sao delimitadas as faixas de comutacao nao dissipativa para os semicondutores. Um exemplo de projeto e desenvolvido, sendo descrito todo o hardware executado e o software implementado em um microcontrolador de 32 bits fabricado por Texas Instruments. Resultados de simulacao e experimentais sao adequadamente discutidos, validando o funcionamento do conversor proposto tanto em regime permanente quanto no que tange a resposta transitoria diante de degraus de carga.
Renormalization Group Evolution Analysis of the Gauged Froggatt-Nielsen Mechanism in 2-Higgs-doublet Models

In this thesis, we consider a 2-Higgs-doublet model with a gauged Froggatt-Nielsen mechanism as an extension of the standard model. We assume that (i) the theory at the electroweak scale is the low energy limit of a theory defined at a scale $\Lambda_{FN}$ (ii) the Yukawa couplings at $\Lambda_{FN}$ are generated by the Froggatt-Nielsen mechanism. The first assumption is enforced by requiring that models be well behaved under Renormalization Group evolution up to $\Lambda_{FN}$ in two scenarios: $\Lambda_{FN} = 10^5$ GeV and $\Lambda_{FN}= 10^{16}$ GeV. We then proceed to find flavon charge assignments that reproduce the correct order of magnitude for fermion masses and quark mixing. The flavon charges are further constrained by anomaly cancellation conditions. Lastly, having identified the parameter space of models consistent with our assumptions and experimental constraints, we evolve these regions up to $\Lambda_{FN}$ using the RG equations and discuss possible physics at that scale.
Dissecting the Yield Curve: The International Evidence

Using a stochastic volatility affine term structure model, we explicitly consider the interrelation between yield curves and macro and volatility factors. We provide estimates of short rate expectations, term premium and convexity of nominal yields and for their real and inflation components for four different currency areas: US, Euro Area, UK, and Japan. We find that in all areas there are non-negligible convexity effects in correspondence with high volatility periods, and that term premium and convexity explain a significant proportion of the dynamics at the long end of the yield curve. Using panel regressions, we show that, overall, short rate expectations are procyclical while term premia exhibit a countercyclical behaviour and tend to increase with yield volatility. We also detect strong cross-country co-movements both in short rate expectations and term premia, with the degree of connectedness exhibiting significant time variation.
Performance Analysis of Statistical Approaches and NMF Approaches for Speech Enhancement

Super-Gaussian Based Bayesian Estimators plays significant role in noise reduction. However, the traditional Bayesian Estimators process only DFT spectral amplitude of noisy speech and the phase is left unprocessed. While deriving Bayesian estimators, consideration of phase information provides improved results. The main objective of this paper is twofold. Firstly, the Super-Gaussian based Complex speech coefficients given Uncertain Phase (CUP) based Bayesian estimators are compared under different noise conditions like White noise, Babble noise, Pink noise, Modulated Pink noise, Factory noise, Car noise, Street noise, F16 noise and M109 noise. Secondly, a novel speech enhancement method is proposed by combining CUP estimators with different NMF approaches and online bases updation. The statistical estimators show less effective results under completely non-stationary assumptions. Non-negative Matrix Factorization (NMF) based algorithms show better performance for non stationary noises. The drawback of NMF is, it requires training and/or requires clean speech and noise signals. This drawback can be overcome by taking the advantages of both statistical approaches and NMF approaches. Such approaches like Posteriori Regularized NMF (PR-NMF), Weibull Rayleigh NMF (WR-NMF), Nakagami Rayleigh (NR-NMF), CUP estimator with Gamma and Generalized Gamma distributions + NMF + Online bases Update (CUP-GG + NMF + OU) and CUP-GG + WR-NMF / NR-NMF + OU are considered for comparison. The objective of this paper is to analyze the performance of speech enhancement methods using Bayesian estimators, NMF approaches, Combination of statistical and NMF approaches. The objective performance measures Perceptual Evaluation of Speech Quality (PESQ), ShortTime Objective Intelligibility (STOI), Signal to Noise Ratio (SNR), Signal to Distortion Ratio (SDR), Segmental SNR (Seg SNR) are considered for comparison.
5G IoT Industry Verticals and Network Requirements

The effective provisioning of industry verticals over the next-to-come 5G systems opens novel business opportunities for telco operators especially when considering the integration of Internet of Things (IoT) devices as enablers of business cases based on remote sensing and control. This chapter highlights the main features of IoT verticals with particular attention on healthcare, smart cities, industry automation and entertainment business cases. The aim of this Chapter is to derive the requirements such IoT verticals pose in terms of design features to be considered in the standardization of 5G systems. This chapter presents the state of the art on the contribution from the research community and standardization bodies to address the 5G design characteristics with particular attention to the features enabling a proper management of IoT-oriented business cases.
Weighted Greatest Common Divisors

In this thesis, the notion of weighted greatest common divisor (wgcd) will be discussed and redefined for integral domains. We will use the concept of squarefree factorizations to prove existence and uniqueness (up to unit multiplication) for unique factorization domains. We will use Yun's squarefree factorization algorithm to write algorithms for computing wgcd's of polynomials over some fields, of which some will be implemented in the computer algebra system PARI/GP. We will also extend the idea of factoring the usual gcd to compute wgcd's of polynomials over any field and compare the corresponding algorithm to those based on Yun's algorithm.
A Survey on Drug-Target Interaction Prediction Methods Analysis of Prediction Mechanisms for Drug Target Discovery

In Modern drug discovery, identifying drug target interactions is having high significance in terms of suggesting new drug candidates and repositioning old drugs. Prediction of drug-target interactions can be done by experimentally and it is very expensive and time consuming. Therefore, there is a continuous demand for effective and low-cost computational techniques for drug target interaction prediction. Many algorithms have been formulated to infer novel drug – target interactions. In this paper, we make a survey on the recent progress being made on computational methodologies that have been developed to predict drug targets based on different kinds of drug and protein data.
Convolutional Neural Networks Generalization Utilizing the Data Graph Structure

Convolutional Neural Networks have proved to be very efficient in image and audio processing. Their success is mostly attributed to the convolutions which utilize the geometric properties of a low - dimensional grid structure. This paper suggests a generalization of CNNs to graph-structured data with varying graph structure, that can be applied to standard regression or classification problems by learning the graph structure of the data. We propose a novel convolution framework approach on graphs which utilizes a random walk to select relevant nodes. The convolution shares weights on all features, providing the desired parameter efficiency. Furthermore, the additional computations in the training process are only executed once in the pre-processing step. We empirically demonstrate the performance of the proposed CNN on MNIST data set, and challenge the state-of-the-art on Merck molecular activity data set.
Electronic structure study of 
LaCoIn5
 and its comparison with 
CeCoIn5

A central key to understanding heavy-fermion systems involves revealing how itinerant low-energy excitations emerge from local $f$ moments. An effective way to understand the $f$-electron behavior is to compare the electronic structure of $f$-electron compounds with a reference isostructural compound with no $f$ electrons. Here we present a systematic electronic structure study of ${\mathrm{LaCoIn}}_{5}$, which is a non-$f$ reference compound of the heavy-fermion superconductor ${\mathrm{CeCoIn}}_{5}$. Our angle-resolved photoemission spectroscopy study of the three-dimensional Fermi surface and band structure of ${\mathrm{LaCoIn}}_{5}$ highlights the rather three-dimensional electronic character of this compound. The conduction bands of ${\mathrm{LaCoIn}}_{5}$ are almost identical to that of ${\mathrm{CeCoIn}}_{5}$, though obvious differences can be found in the low-energy electronic structure. Finally, we give a quantitative analysis of the Fermi pocket change in ${\mathrm{LaCoIn}}_{5}$ and ${\mathrm{CeCoIn}}_{5}$, which may be helpful for understanding the important ``large'' and ``small'' Fermi surface issue in heavy-fermion compounds.
Comparing Generalized Linear Model of Count Data: Application towards Pteridophyta species

Pteridophyta is known as “paku-pakis” in Malay and it is one the flora species that exists in ecological system. Besides, Pteridophyta is one of the species that need to be preserved. Either flora or fauna, both are very important to preserve the ecosystem and control the pollution. In order to observe the species, the fundamental unit of all diversity metrics is a count of specific individuals. In some consequences, the uncorrected counts of observed species often used in measuring the diversity which ignore detection together and established methods to be used to account for missed species. Analysis of count data is widely used in engineering, public health, epidemiology, medical studies, ecology and many research of interest. Rarity increases the number of locations with zero detection in excess of those expected under simple models of abundance. The aims of this study are to compare the Generalized Linear Model (GLiM) in the application of Pteridophyta species of count data.
Validity of perturbative methods to treat the spin–orbit interaction: application to magnetocrystalline anisotropy

A second-order perturbation (2PT) approach to the spin–orbit interaction (SOI) is implemented within a density-functional theory framework. Its performance is examined by applying it to the calculation of the magnetocrystalline anisotropy energies (MAE) of benchmark systems, and its efficiency and accuracy are compared with the popular force theorem method. The case studies are tetragonal FeMe alloys (Me=Co, Cu, Pd, Pt, Au), as well as FeMe (Me=Co, Pt) bilayers with (111) and (100) symmetry, which cover a wide range of SOI strength and electronic band structures. The 2PT approach is found to provide a very accurate description for 3d and 4d metals and, moreover, this methodology is robust enough to predict easy axis switching under doping conditions. In all cases, the details of the bandstructure, including states far from the Fermi level, are responsible for the finally observed MAE value, sometimes overruling the effect of the SOI strength. From a technical point of view, it is confirmed that accuracy in the MAE calculations is subject to the accuracy of the Fermi level determination.
Weakly nonlinear mode interactions in spherical Rayleigh–Bénard convection

In an annular spherical domain with separation $d$ , the onset of convective motion occurs at a critical Rayleigh number $Ra=Ra_{c}$ . Solving the axisymmetric linear stability problem shows that degenerate points $(d=d_{c},Ra_{c})$ exist where two modes simultaneously become unstable. Considering the weakly nonlinear evolution of these two modes, it is found that spatial resonances play a crucial role in determining the preferred convection pattern for neighbouring modes $(\ell ,\ell \pm 1)$ and non-neighbouring even modes $(\ell ,\ell \pm 2)$ . Deriving coupled amplitude equations relevant to all degeneracies, we outline the possible solutions and the influence of changes in $d,Ra$ and Prandtl number $Pr$ . Using direct numerical simulation (DNS) to verify all results, time periodic solutions are also outlined for small $Pr$ . The $2:1$ periodic signature observed to be general for oscillations in a spherical annulus is explained using the structure of the equations. The relevance of all solutions presented is determined by computing their stability with respect to non-axisymmetric perturbations at large $Pr$ .
Applying Naïve bayes, BayesNet, PART, JRip and OneR Algorithms on Hypothyroid Database for Comparative Analysis

This research paper intends to provide comparative analysis of Data Mining classification algorithms. Some benchmarking classification algorithms like Naïve Bayes, Bayesian Network, JRip, OneR and PART are selected based on literature survey. These classification algorithms are applied on Hypothyroid health database for the purpose of finding better techniques for classification. The multiple parameters taken into considerations for analytical purpose are accuracy, sensitivity, Precision, False positive Rate and f-measure. Results of all these parameters are taken for all the described classification techniques. At the last the results are provided in tabular form to facilitate comparative analysis for the hypothyroid database.
User Association for Ultra-Dense mmWave Networks With Multi-Connectivity: A Multi-Label Classification Approach

Millimeter wave (mmWave) communication has been considered as a promising means to fulfill the requirements of next generation cellular networks. To conquer the limited coverage and the severe penetration attenuation, we investigate the ultra-dense mmWave networks with multi-connectivity in this letter, which enables users to access multiple mmWave base stations simultaneously. A user association problem is formulated to maximize the system throughput, which is NP-hard. To tackle it, we introduce a multi-label classification approach to solve the proposed combinatorial problem. Numerical results demonstrate the good optimality and low computational complexity of our proposed algorithms.
Development of a Self-Orienting CubeSat Solar Array

The sponsor of this conceptual design project was the Air Force Institute of Technology (AFIT) at WrightPatterson Air Force Base in Dayton, Ohio. AFIT was striving to give CubeSats more capability to conduct research, reconnaissance, and other functions. One of the major barriers for AFIT to overcome to give CubeSats more capability was the ability of the CubeSat to generate usable power while in orbit. All of AFIT’s CubeSats generated the power needed while in orbit with solar panels that are rigidly mounted to the outside of the craft. AFIT believes that a new design for the solar array used on the CubeSat will generate the power needed to increase their capabilities. The design that was deemed the most appropriate at the conclusion of this stage of the project was a design for a two degree of freedom mechanism that is attached to the solar panels to better orient them towards the sun. There are three aspects of the new design coming from this project that will make it unique. 1) Draws no direct power from the CubeSat Energy Storage to perform the movement. 2) Takes up less space on the CubeSat than competing designs. 3) Takes up less of the weight limit of the CubeSat than competing designs. The new Solar Array design should be able to orient four times more solar panel area towards the sun, as compared to the current AFIT design. There will be between 3 to 4 times more energy generation from the new design of solar array as a result, and an increase in the capabilities of the CubeSats. Acknowledgements A special thanks to the staff of the Air Force Institute of Technology and Faculty members of the University of Dayton department of Mechanical & Aerospace Engineering.