Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Cohesion and Inequalities in Italy : Does Social Exclusion Affect Social Cohesion ?

In this paper my aim is the analysis of the relationship between social cohesion and social inequality. In the scientific debate there are two main positions: the first one affirms the existence of a direct negative association between economic inequality and social cohesion. According to this position, the risk associated with poverty is due to its relationship with social exclusion. In fact, the lack of economic resources could lead to a deficiency on the capability to acquire a set of functioning like the taking part in the community life. The second one is related to relative deprivation. Indeed, low levels of social cohesion could be connected to the expectations generated by society. In this paper, the topic of expectations is dealt with reference to the opportunity structure, that is shaped by class, generation and geographic area. I have performed the analysis using individual data and following the structural position thesis. According to this view, people differ in their attitudes subject to their social position. More precisely, I suppose a situational mechanism by which the configuration of individuals’ opportunity – influenced by social structure – defines the process of expectations and beliefs’ formation following the reference group theory. Secondly, according to a behavioural mechanism, the shift from individual expectations and beliefs to attitudes and behaviours connected with social cohesion occurs in according to the mechanism of adaptive formation of the preferences. Using this theoretical approach as a background, I have answered to the following research questions: Which is the impact exerted by the levels of economic inequalities on social cohesion? How does the relationship vary between economic inequalities and social cohesion considering the individual structural position? In this paper I have operazionalised the concept of social cohesion by a set of items about attitudes toward taxation. More precisely, I have chosen the fiscal sphere, because I suppose that it is related to the formation of public goods. As a consequence, a negative attitude towards taxation could be interpreted as a lack in the will of cooperation in the process of public goods’ building. Data from “Survey of Italian Households’ Income and Wealth 2004” have been analysed applying Mokken scale analysis in order to measure social cohesion, then by means of latent class analysis to measure economic inequality. Finally I have performed a linear regression model to assess the effect of economic inequality and social structure on the levels of social cohesion. Results of the analysis show that even though social structure is not able to fully mediated the relationship between social cohesion and economic inequality, it is possible to affirm that the individual position in the social structure plays a more important role than economic inequality in explaining the levels of social cohesion.
TATIANA ALVES RIGAMONTE FERNANDES LACASES DE ACTINOBACTÉRIAS: CARACTERIZAÇÃO, OBTENÇÃO DE NOVOS PRIMERS E EXPRESSÃO HETERÓLOGA NA LEVEDURA Kluyveromyces marxianus UFV-3 DOUTORADO VIÇOSA MINAS GERAIS - BRASIL 2014 TATIANA ALVES RIGAMONTE FERNANDES LACASES DE ACTINOBACTÉRIAS: CARACTERIZAÇÃO

FERNANDES, Tatiana Alves Rigamonte, D.Sc., Universidade Federal de Viçosa, February, 2014. Laccases from actinobacteria: characterization, designing of new primers and heterologous expression in the yeast Kluyveromyces marxianus UFV-3. Adviser: Flávia Maria Lopes Passos. Co-advisers: Wendel Batista da Silveira and Tiago Domingues Zucchi. The great diversity in substrate range oxidized by laccase enzymes has attracted attention in both areas of research: basic, and applied to industrial and environmental activities. Among microbial laccases, little is known about the ones produced by bacteria. In this work, it is highlighted the phylum Actinobacteria, order Actinomycetales, due to its recognized biotechnological importance and capacity of degrading recalcitrant compounds. Owing to the large amounts of enzyme required for industrial application, summed to the difficulty in manipulation of native producers, heterologous expression is here suggested using the yeast Kluyveromyces marxianus as host, which presents the industrial-relevant properties such as fast growth, thermotolerance and status generally regarded as safe (GRAS). The objective of this work was to obtain a strain of Kluyveromyces marxianus expressing and secreting a laccase from actinobacteria; and, in order to accomplish the main objective, we searched for isolating laccase-producing actinobacteria from soil, characterizing the enzyme, isolating a laccase gene, cloning into expression vector and, finally, transforming K. marxianus UFV-3. In this work, an isolation from soil samples provided laccase-producer colonies, having one of the selected been characterized. From literature data, a critical review was produced, and available gene sequences that codify characterized proteins were employed for designing oligonucleotide primers. The new primers proved to be effective for detection of laccase genes in actinobacteria, and, furthermore, allowed distinction of genes that corresponded to two structurally different laccases. After selection of a complete laccase gene, it was cloned into an expression cassette for integration into K. marxianus UFV-3 genome, and data suggest successful integration.
Best feature performance in codeswitched hate speech texts

How well can hate speech concept be abstracted in order to inform automatic classification in codeswitched texts by machine learning classifiers? We explore different representations and empirically evaluate their predictiveness using both conventional and deep learning algorithms in identifying hate speech in a ~48k human-annotated dataset that contain mixed languages, a phenomenon common among multilingual speakers. This paper espouses a novel approach to handle this challenge by introducing a hierarchical approach that employs Latent Dirichlet Allocation to generate topic models that feed into another high-level feature set that we acronym PDC. PDC groups similar meaning words in word families during the preprocessing stage for supervised learning models. The high-level PDC features generated are based on Ombui et al, (2019) hate speech annotation framework that is informed by the triangular theory of hate (Stanberg,2003). Results obtained from frequency-based models using the PDC feature on the annotated dataset of ~48k short messages comprising of tweets generated during the 2012 and 2017 Kenyan presidential elections indicate an improvement on classification accuracy in identifying hate speech as compared to the baseline
Assessing the Reliability of Stress as a Feature of Authorship Attribution in Syllabic and Accentual Syllabic Verse

This work builds on a recent study by one of the authors, which shows that statistics about versification may be used as a feature in the process of authorship attribution. One such statistic is what we have called the stress profile of a poem, a vector consisting of frequencies of stressed syllables at particular metrical positions. Our initial hypothesis was that because syllabic versification (SV) regulates by definition the number of syllables in a line but not the distribution of stresses, it allows authors to individualize their rhythmical style much more than accentual syllabic versification (ASV), where the distribution of stresses is primarily determined by meter. For that reason, we expected the stress profile to be a more reliable indicator of authorship in Spanish SV than in Czech or German ASV. This hypothesis, however, was not supported by our analysis. For most of our samples, German ASV had lower accuracy than Spanish, which we had predicted, but, contrary to our expectations, the accuracy for Czech ASV and Spanish SV were more or less the same. This result led us to hypothesize further that the traditional labels SV and ASV were misleading and we sought to measure the tonic entropy of our data. In this case, Spanish SV, as expected, was found to be the least tonically regular, while there was a significant difference between the two ASV systems: the values for Czech were even closer to Spanish than to the low-scoring German system. This explains why our initial grouping of Czech and German together into a single ASV category was insufficiently nuanced. 202 Quantitative Approaches to Versification
Development and Analysis of Bidirectional Converter for Electric Vehicle Application

This paper proposes an integrated electric motion machine and converter topology represents the capability of bidirectional power flow between the electric vehicle to AC supply and load. The inductance winding of the electric motion machine carrying the bidirectional converter operation. This operation of eliminate the additional inductors for charging and transfer the energy to load. The effectiveness of proposed bidirectional converter is better than other traditional model. In Hybrid Renewable Energy System, the efficiency method of Bidirectional Converter will be used in the applications of Electrical Vehicle which are mostly used in Hill side areas. This concept has been analyzed by the MATLAB Simulink software. It has been applied with inductor to share the current and reduce the switching stress in the converter devices. In this type of system size and weight can be minimized. KeywordsComponent; bidirectional converter, machine inductance, electric vehicle, buck converter
Numerical scheme for the 2 D Maxwell-Bloch equations modeling ultrashort pulses

In this paper, we present a numerical method for the Maxwell-Bloch equations which modelize the nonlinear interaction between a laser and a crystal. The propagation of the laser involves different time and length scales during the nonlinear long time interaction with the matter. We avoid the use of a mesh size smaller than the wavelength. We do not use the paraxial approximation but we use the fact that the solution is rapidely oscillating. In the spirit of [6], we propose a discretization of a singular equation based on a profile decomposition. We obtain a method which is asymptotic preserving (that is the paraxial approximation made on the discretization gives the discretization of the nonlinear Schrödinger equation). We perform 2D computations. We numerically investigate the effectiveness of the WKB expansion and show that, under some assumptions, it may not be always valid. AMS Subject Classification: 81V10, 35Q60, 35L45, 65T50, 34E20
Reliability Enhancement of Active Distribution Grids via Emergency V2G Programs: An Analytical Cost/Worth Evaluation Framework

In this paper, an analytical cost/worth evaluation framework is presented for determination of optimal reliability enhancement plan in active distribution systems. In this regard, it is assumed that emergency vehicle-to-grid (V2G) programs provided by parking lots will be deployed for supplying loads during network outages, and optimal number of participating electric vehicles (EVs) is determined using a reliability cost/worth method. According to the presented framework, distribution system annual social costs are minimized. Annual social costs include annualized investment costs, annual operating costs, annual revenues as well as annual customer interruption costs (CIC). Therefore, estimation procedure of different cost terms are introduced. Moreover, an appropriate analytical framework is presented for calculation of CIC. Furthermore, a simple method is presented for estimating the financial costs of emergency V2G programs. The presented framework is implemented on a test system, various emergency V2G programs are investigated, and the results are discussed.
Scale-Up of Twin-Column Periodic Counter-Current Chromatography for MAb Purification

Periodic countercurrent (PCC) processes increasingly are being evaluated as alternatives to single-column batch capture processes. Some of the advantages of PCC processes over single-column processes include shortening of processing time and/or reduction of required resin volume through increased productivity; reduction in resin costs through improved resin capacity use; and reduction in buffer consumption through increased column loading. Those advantages, however, come with increased equipment complexity and hardware costs. PCC processes and systems with two to up 16 columns of the same type have been proposed. As the number of columns increases, a process becomes more complex, potentially adding pumps, detectors, valves, and plumbing. The PCC process investigated in this study was a CaptureSMB process: a PCC process using two columns and modulated feed flow rates. All PCC processes are based on the concept of loading the first affinity column beyond its dynamic breakthrough capacity and capturing what breaks through in a second column of the same type. So the first column can be loaded to values of 50−80% breakthrough, whereas in batch chromatography, loading is typically performed to values below 1% breakthrough to prevent product loss (1). Based on the adsorption kinetics and mass-transfer properties of commercially available protein A resins, interconnecting more than two columns in the load zone provided little improvement in performance (2, 3). Baur et al. compared the performance of CaptureSMB, threeand four-column PCC processes based on numerical optimization (4). Results showed that a high capacity use (>95%) was achieved across all formats, which is expected because all formats adopt the above-mentioned loading of two columns in series. The authors showed that among the three formats tested, CaptureSMB offers productivity advantages, especially with low and high expression titers. That is because the interconnected loading phase of that process can be adjusted in duration independently of other process steps as titer increases or decreases. Figure 1 outlines the principle of the CaptureSMB process schematically based on one process cycle. In the first step, the two affinity columns are interconnected. The first column is loaded beyond its dynamic breakthrough capacity, followed by a buffer wash to transfer unbound product from the first column to the second column. Product Focus: Antibodies
Transforming Second Language Acquisition Modeling

As more student data becomes available, deep learning techniques can enable stronger analysis of tasks such as language acquisition and allow for more personalized online learning. Duolingo’s Second Language Acquisition Modeling (SLAM) task provides a large corpus of beginner student data to trace how students learn a new language through three different exercises. Doing both language modeling for each exercise and simultaneously using information on student progression over time, SLAM is both a language modeling and cognitive modeling task. We propose to apply various recurrent architectures to SLAM, at the instance, exercise, and user levels. Our main models included a logistic regression model and an exercise level LSTM model. By introducing word embeddings in our models, both our models outperform the Duolingo baseline. Increases in performance from adding a one-hot user encoding shows evidence that learning about different users helps performance. Sentence structure of the exercise and deeper networks clearly matters as well, given the performance improvements moving from Logistic Regression (which was on the instance level) to an exercise level LSTM. Our best model, the exercise level LSTM model, would currently sit at 7th place on the public worldwide leaderboard of all submissions to the English language learning track, with an AUC of 0.840. Our performance is comparable to the 4th-6th places on the leaderboard which have AUC between 0.840 and 0.850. Additionally, we have only hyperparameter tuned the learning rate, and since we have yet to overfit the model we have avoidable bias, indicating that deeper networks and hyperparameter tuning can get us past the current state-of-the-art English track AUC of 0.861. We are continuing to work on the project and will release a paper and results once we beat the state-of-the-art.
Portoroz , Slovenia , August 24-30 , 2008 Estimates of imputed rents and their distributional impact in Greece

Most empirical income studies do not take into account the income advantage that is derived from home ownership, possibly contaminating the results with a bias. Using micro data from the 2004/2005 Greek Household Budget Survey, we estimate the imputed rental income using both a hedonic model and homeowners’ self-assessments. Imputed rents are then added to the standard notion of equivalized disposable income and inequality and poverty are reexamined. We find that in a country such as Greece, where homeownership is widespread, imputed rents are far more equally distributed than disposable income, thus resulting in a significant decrease in both inequality and poverty. Moreover the identification of gainers and losers reveals that demographic groups considered as sensitive to poverty such as the elderly, households that are headed by pensioners or by low educated persons and people who live in rural areas enjoy an improvement in their relative income position.
Thermal Space Chiller Filter Cooling Coil Heating Coil Pump Fan 1 Outdoor Air Damper 5 2 34 Excluded Air Heat Exchanger

Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to maintain thermal comfort and indoor air quality (IAQ), account for almost half of the energy consumed by the buildings. Thus, intelligent scheduling of the building HVAC system has the potential for tremendous energy and cost savings while ensuring that the control objectives (thermal comfort, air quality) are satisfied. Traditionally, rule-based and model-based approaches such as linear-quadratic regulator (LQR) have been used for scheduling HVAC. However, the system complexity of HVAC and the dynamism in the building environment limit the accuracy, efficiency and robustness of such methods. Recently, several works have focused on model-free deep reinforcement learning based techniques such as Deep Q-Network (DQN). Such methods require extensive interactions with the environment. Thus, they are impractical to implement in real systems due to low sample efficiency. Safetyaware exploration is another challenge in real systems since certain actions at particular states may result in catastrophic outcomes. To address these issues and challenges, we propose a modelbased reinforcement learning approach that learns the system dynamics using a neural network. Then, we adopt Model Predictive Control (MPC) using the learned system dynamics to perform control with random-sampling shooting method. To ensure safe exploration, we limit the actions within safe range and the maximum absolute change of actions according to prior knowledge. We evaluate our ideas through simulation using widely adopted EnergyPlus ∗This work has been sponsored by the U.S. Army Research Office (ARO) under award number W911NF1910362 and the U.S. National Science Foundation (NSF) under award number 1911229. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. BuildSys ’19, November 13–14, 2019, New York, NY, USA © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-7005-9/19/11. $15.00 https://doi.org/10.1145/3360322.3360861 tool on a case study consisting of a two zone data-center. Experiments show that the average deviation of the trajectories sampled from the learned dynamics and the ground truth is below 20%. Compared with baseline approaches, we reduce the total energy consumption by 17.1% ∼ 21.8%. Compared with model-free reinforcement learning approach, we reduce the required number of training steps to converge by 10x.
Positive LUO Converter Fed BLDC Motor Driven DPPO Control MPPT for Solar PV Array based Application

This paper is about an efficient brushless dc (BLDC) motor drive for solar photovoltaic (SPV) Array based application. A positive luo converter is utilized to extract the maximum available power from the SPV array and the LUO converter is designed to operate on Discontinuous Conduction Mode DCM for Improve the PF. The LUO converter feeding the 3-phase supply to the BLDC motor via VSI circuit. Low frequency signals used for trigger the switches. Single sensor used for monitor the speed of the BLDC motor. A Direct Power Perturb & Observe (DPPO) algorithm is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed control algorithm eliminates phase current sensors and adapts a fundamental frequency switching of the voltage source inverter (VSI), thus avoiding the power losses due to high frequency switching. No additional control or circuitry is used for speed control of the BLDC motor. The speed control can be achieved through a VSI. An appropriate control of positive luo converter through a Direct power perturb and observe (DPPO) is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system offers soft starting of the BLDC motor. The proposed system is designed and modelled such that the performance is not affected under dynamic conditions. The suitability of proposed system at practical operating conditions is demonstrated through simulation results using MATLAB/Simulink followed by an experimental validation. Keywordbrushless dc (BLDC), solar photovoltaic (SPV), A fuzzy logic control (FLC), maximum power point tracking (MPPT), voltage source inverter (VSI) __________________________________________________________________________________________________
A New Static Var System Auxiliary Controller for Damping Torsional Oscillations in Series Compensated Power System

In this paper the application of an Static Var System (SVS) auxiliary controller for damping torsional oscillations in a series compensated power system is presented. A new auxiliary controller, known as the combined derivative of reactive power and derivative of computed internal voltage auxiliary (CDRPDCIVA) controller has been developed and incorporated in the SVS control system located at the middle of a series compensated long transmission line. Effectiveness of proposed controller is tested on a system having a similar spread of the torsional modes as the first IEEE benchmark model. Eigen value analysis study is conducted for various levels of power transfer and series compensation. The results of eigenvalue analysis are validated by carrying out time domain analysis study based on non linear model. The proposed SVS auxiliary controller along with induction machine damping unit (IMDU) stabilizes all the torsional modes for a wide operating range and thus enhancing the dynamic and transient performance of the system.