Miracle-dz/UniFAR-FTUs huggingface dataset:
Rational Addiction to Cultural Goods: the Effects of Narrative on Consumption and Broadcasting of Fiction in France∗

TV-series have traditionally been watched and monetized through broadcasting on free-to-air and pay television. They are increasingly consumed via on-demand platforms and catch-up services. The purpose of this paper is to evaluate how the narrative structure of TV-series influences their consumption and causes delinearization. We use data from Médiamétrie, reporting the live and catch-up audience for all movies and series aired between 8 p.m and 10 p.m on free and public French TVchannels, from 2011 to 2016. We show that when the story takes several episodes or seasons to develop, consumers are more likely to watch the program on alternative platforms or catch-up. We provide evidence that the consumption of such programs follows a model of rational addiction, where current consumption is influenced by its past and future values. These results allow us to explain recent changes in the industrial organization of the French free-to-air television market. We show how the narrative structure of TV-series can explain broadcasters’ competitive strategies. These results can have important policy implications, as broadcasters are one of the main investors in the production of audiovisual content in France.
CORRUPTION, DEMOCRACY AND ECONOMIC GROWTH: DOES CONDITIONALITY MATTER?

This paper gives insight about the role of democracy in two competing hypotheses whether corruption ‘greases the wheels’ or ‘sands the wheels’ of bureaucracy. The study also examined whether conditional cooperation between corruption and democracy matters or not in this regard. The empirical results indicate that democracy plays an essential role in determining the corruption-growth relationship, as the coefficient of interaction term between corruption and democracy is negative and significant. The results support the hypothesis that corruption greases the wheels of administration and thereby promotes growth in countries having poor democratic norm, and second hypothesis holds in case of higher degree of democracy. The results of the study suggest that promotion of democratic norms is very essential to curb the corruption level and to boost the economic performance of the nation. Because institutional development promotes the check and balance system in the country that enhances economic growth through increase in investment.
Computational Technologies For Accreditation In Higher Education

Academic accreditation and assessment in Higher Education (A3-HE) is, above all, a social status meant to acknowledge that an institution or program is following recognized and requested quality criteria issued from common good practice. In a previous work, we described the main processes involved in A3HE. Two main issues were reported. First, heavy and tedious paperwork characterizes actual academic processes. Second, subjective judgments might interfere with the processes. Indeed, both the internal self-examination undergone by institutions / programs and the external reviewing processes made by recognized accrediting bodies are prone to errors and subjective biases as they are largely based on rules of thumb human judgments – despite the presence of standards. In this paper, we describe a set of computational technologies to address these issues. Emphasis is made on technologies spanning (crude) data, information, refined information including decision support, ultimately leading to the most refined and expensive piece of information, i.e., knowledge and its discovery in large and diversified databases over the Web, based on cloud computing solutions. A human-machine interactive knowledge-based learning control system for A3-HE is our far-reaching goal. However, the A3-HE processes are too complex to be addressed by computerized systems alone. As a result, scaling up to real-life applications still require much time to reach tangible implementations. Keywords— Academic accreditation, Database management system (DBMS), Information system (IS), Decision support system (DSS), Knowledge base conrol system (KBCS), Data mining, Web technologies.
MR fluids for Smart Seismic Protection of Civil Structures

In the paper one focuses on the possibility of mitigating the effects induced in civil structures by earthquakes by means of Magneto-Rheological (MR) dampers. Modeling as well as design issues are discussed, together with some numerical and experimental investigation demonstrating the potentials of such devices. Key-Words: Smart Fluids, Magneto-rheological devices, Structural Control, Dynamics, Modeling and Design, Experimental Investigation,.
Discovery and Ranking of Embedded Uniqueness Constraints

Data profiling is an enabler for efficient data management and effective analytics. The discovery of data dependencies is at the core of data profiling. We conduct the first study on the discovery of embedded uniqueness constraints (eUCs). These constraints represents unique column combinations embedded in complete fragments of incomplete data. We showcase their implementation as filtered indexes, and their application in integrity management and query optimization. We show that the decision variant of discovering a minimal eUC is NP-complete and W[2]-complete. We characterize the maximum possible solution size, and show which families of eUCs attain that size. Despite the challenges, experiments with real-world and synthetic benchmark data show that our column(row)-efficient algorithms perform well with a large number of columns(rows), and our hybrid algorithm combines ideas from both. We show how to rank eUCs to help identify relevant eUCs.
Focusing on patients: differences between the practice of expert and non-expert nephrology nurses

Specialisation in nursing enables a nurse to focus, in much greater depth, on the requisite knowledge and skills for providing patients with the best possible care. Nephrology nursing is one such area where specialisation has evolved. The characteristic focus of practice emerged as an important feature during a study into the process of expertise acquisition in nephrology nursing practice. Using grounded theory methodology, this study involved 6 non-expert and 11 expert nurses and took place in one renal unit in New South Wales. Nephrology nursing practice was observed for 103 hours, and this was immediately followed by semi-structured interviews. The characteristic of focus was conceptualised as the nurses' centre of attention or concentration while they were undertaking nursing activities. Focus ranged from inexperienced non-expert nurses concentrating predominantly on the immediate task at hand, experienced non-expert nurses who focussed on the medium term to expert nurses who viewed actions (and their possible consequences) more broadly and in the longer term. Of significance to nursing, is how nephrology nurses alter their focus of practice as they acquire and exercise their developing expertise in this specialty.
Married in Spite of The Law: An Ethnographic Study on Early Marriage in Zaatari Refugee Camp

Despite being generally forbidden by the Jordanian Law of Personal Status, early marriage among Syrian refugees in the camp of Zaatari is still common, and according to some sources, its incidence has increased during the past years. This practice in Zaatari is regulated by the Syrian adat, that at the same time coexists with Jordanian law and decision norms advocated by International NGOs; giving place to a situation of legal pluralism in the camp. The overall aim of this thesis is to contribute to scholarly debates around the socio-legal adaptation of refugees into a new legal environment by exploring legal pluralism in Zaatari and the interplay between the alternative normative orders that conform it, with a focus on the practice of early marriage. For this purpose, this study is based on ethnographic fieldwork combined with qualitative methods of data collection. (Less)
A thermomechanical material point method for baking and cooking

We present a Material Point Method for visual simulation of baking breads, cookies, pancakes and similar materials that consist of dough or batter (mixtures of water, flour, eggs, fat, sugar and leavening agents). We develop a novel thermomechanical model using mixture theory to resolve interactions between individual water, gas and dough species. Heat transfer with thermal expansion is used to model thermal variations in material properties. Water-based mass transfer is resolved through the porous mixture, gas represents carbon dioxide produced by leavening agents in the baking process and dough is modeled as a viscoelastoplastic solid to represent its varied and complex rheological properties. Water content in the mixture reduces during the baking process according to Fick's Law which contributes to drying and cracking of crust at the material boundary. Carbon dioxide gas produced by leavening agents during baking creates internal pressure that causes rising. The viscoelastoplastic model for the dough is temperature dependent and is used to model melting and solidification. We discretize the governing equations using a novel Material Point Method designed to track the solid phase of the mixture.
Anesthesia With and Without Nitrous Oxide and Long-term Cognitive Trajectories in Older Adults

BACKGROUND: We evaluated the hypothesis that the rate of postoperative decline in global cognition is greater in older adults exposed to general anesthesia with nitrous oxide (N2O) compared to general anesthesia without N2O. METHODS: Longitudinal measures of cognitive function were analyzed in nondemented adults, 70–91 years of age, enrolled in the Mayo Clinic Study of Aging. Linear mixed-effects models with time-varying covariates assessed the relationship between exposure to surgery with general anesthesia (surgery/GA) with or without N2O and the rate of long-term cognitive changes. Global cognition and domain-specific cognitive outcomes were defined using z scores, which measure how far an observation is, in standard deviations, from the unimpaired population mean. RESULTS: The analysis included 1819 participants: 280 exposed to GA without N2O following enrollment and before censoring during follow-up (median [interquartile range {IQR}] follow-up of 5.4 [3.9–7.9] years); 256 exposed to GA with N2O (follow-up 5.6 [4.0–7.9] years); and 1283 not exposed to surgery/GA (follow-up 4.1 [2.5–6.4] years). The slope of the global cognitive z score was significantly more negative following exposure to surgery/GA after enrollment (change in slope of −0.062 [95% confidence interval {CI}, −0.085 to −0.039] for GA without N2O, and −0.058 [95% CI, −0.080 to −0.035] for GA with N2O, both P < .001). The change in slope following exposure to surgery/GA did not differ between those exposed to anesthesia without versus with N2O (estimated difference −0.004 [95% CI, −0.035 to 0.026], P = .783). CONCLUSIONS: Exposure to surgery/GA is associated with a small, but statistically significant decline in cognitive z scores. Cognitive decline did not differ between anesthetics with and without N2O. This finding provides evidence that the use of N2O in older adults does not need to be avoided because of concerns related to decline in cognition.
Transcriptional Mutagenesis Prevents Ribosomal DNA Deterioration: The Role of Duplications and Deletions

Abstract Clashes between transcription and replication complexes can cause point mutations and chromosome rearrangements on heavily transcribed genes. In eukaryotic ribosomal RNA genes, the system that prevents transcription–replication conflicts also causes frequent copy number variation. Such fast mutational dynamics do not alter growth rates in yeast and are thus selectively near neutral. It was recently found that yeast regulates these mutations by means of a signaling cascade that depends on the availability of nutrients. Here, we investigate the long-term evolutionary effect of the mutational dynamics observed in yeast. We developed an in silico model of single-cell organisms whose genomes mutate more frequently when transcriptional load is larger. We show that mutations induced by high transcriptional load are beneficial when biased toward gene duplications and deletions: they decrease mutational load even though they increase the overall mutation rates. In contrast, genome stability is compromised when mutations are not biased toward gene duplications and deletions, even when mutations occur much less frequently. Taken together, our results show that the mutational dynamics observed in yeast are beneficial for the long-term stability of the genome and pave the way for a theory of evolution where genetic operators are themselves cause and outcome of the evolutionary dynamics.
MICROSTRUCTURAL STUDY BY ELECTRON MICROSCOPY OF SONOCHEMICAL SYNTHESIZED TiO2

Sonochemical synthesis of nanostructured TiO2 has been carried out successfully at room temperature. Heat-treatments have been applied to as-prepared sample and the microstructural evolution has been studied by X-ray powder diffraction and electron microscopy (SEM, TEM and HRTEM). The results showed that particle growth process and coarsening mechanism are governed by the mobility of triple junction. Also it was observed that an agglomeration of nanoparticles promotes the growth of well-oriented crystalline twin structures. Furthermore, rutile particles are attached to the anatase particles by forming a coherent interface during the phase transformation anatase-rutile, this interface is energetically preferred nucleation site for rutile phase. Finally, the complete phase transformation to rutile is related with a reduction in the total free energy of the system. Therefore, the microstructural evolution reported herein may open new perspectives for the development of TiO2 nanoparticles as a promising material, which can be widely applied to photocatalytic system.
Classification of Facial Action Units in Brazilian Sign Language

Facial expression (FE) is fundamental for the understanding of Brazilian Sign Language (Libras). FE not only has the function of conveying affective states but also is an essential part of its grammar, i.e., FE is one of the parameters used in the construction of a sign. An automatic recognition model capable of classifying facial expressions can be applied to assistive technologies and further deepening Libras’ linguistic studies. Here we present a method for automatic recognition of facial expressions in Libras by using convolutional neural networks combined with a region feature extraction approach. The obtained experimental recognition rates are close to the state-of-the-art.
Pain in pediatric dentistry

Objectives: To present and analyse the Dental Discomfort Questionnaire (DDQ) for very young children and to assess the possible differences in pain-related behaviours displayed by children with or without reported toothache, and by children with or without decayed teeth. Methods: Based on parental interviews of toddlers referred to a dental care practice twelve pain-related behaviours were identified which formed the Dental Discomfort Questionnaire (DDQ). The DDQ was filled out by parents on behalf of their children (N=146; mean age 47 months). Two third (n=94) of the children were referred to a special dental care centre and one third (n=52) were controls from a day care centre. Results: The results show that the 12 items of the DDQ seem to measure one dimension. However, four items do not correlate with the presence of reported toothache, when these items are removed the DDQ-8 has a satisfactory reliability. All eight behaviours from the DDQ-8 occur significantly more often in children with decayed teeth and toothache than in children without decayed teeth or toothache. Especially behaviours concerning eating or brushing teeth are found to be more often present in children with decayed teeth and toothache. Conclusions: It seems useful to take the child behaviour into account in assessing toothache. The DDQ has shown to be a reliable instrument, which could be helpful in the future for both parents and dentists in identifying toothache in young children.
Preprocessing Images to Improve Deep Neural Networks Classification

The amount of images used to train a classifier has a great impact in the algorithm’s performance. In the domain of e-commerce, most of the dataset is formed by studio images and we demonstrate in this work that by multiplying the amount of images with different transformations can provide a significant boost on the overall performance of the classifier. Moreover, we list the challenges encountered in such task and present the improvements obtained with a classifier trained with additional images that were synthetically generated by applying several transformations on the original dataset. Finally, we show that a Convolutional Neural Network, one of the current prominent algorithms in Computer Vision can be pooled together with binary classifiers for each class of images in order to predict images classes with great accuracy. Keywords—Computer Vision, Image Classification, Deep Learning, Studio images, Convolutional Neural Networks, Preprocessing images
STUDY ON STABILIZATION OF ORGANIC SOIL WITH FLY ASH

Any soil containing a significant amount of organic matter influences its engineering properties. Organic content in soil is a detrimental characteristics for stabilization. Increase in organic content of soil indicates that strength of the soil decreases exponentially. This study illustrates to characterize the stabilized organic soil prepared in the laboratory by using fly ash at varying percentages. To these attempts, different stabilized soils containing fly ash of 0%, 10% and 20% were prepared in the laboratory. Moreover disturbed organic soils were collected from two selected location named Shiromoni and Beel Dakatia at and near Khulna City Corporation Region of Bangladesh having organic content 18% and 16% respectively. In the laboratory standard methods were followed to determine unconfined compressive strength, Atterberg limits, specific gravity and ignition. Results show that unconfined compressive strength of untreated and stabilized soils at varying percentages of fly ash content increases with the increasing percentages of fly ash content but the amount of increase depends on the type of soil and characteristics of the fly ash. The increases in strength and stiffness are attributed primarily to cementing caused by pozzolanic reactions, although the reduction in water content resulting from the addition of dry fly ash solid also contributes to strength gain. The pozzolanic effect appears to diminish as the water content decreases. The significant characteristics of fly ash are to affect the increase in unconfined compressive strength. In addition, results also reveal that water content and optimum moisture content decreases while the liquid limit and plastic limit increase after increasing percentages of fly ash content. This causes because of the addition of the fine particles such as fly ash which caused more water to liquefy the soil.
Novel Compound Heterozygous Mutations in IL-7 Receptor α Gene in a 15-Month-Old Girl Presenting With Thrombocytopenia, Normal T Cell Count and Maternal Engraftment

Patients with severe combined immunodeficiency (SCID) exhibit T lymphopenia and profound impairments in cellular and humoral immunity. IL-7 receptor α (IL-7Rα) deficiency is a rare form of SCID that usually presents in the first months of life with severe and opportunistic infections, failure to thrive and high risk of mortality unless treated. Here, we reported an atypical and delayed onset of IL7Rα-SCID in a 15-month-old girl presenting with thrombocytopenia. Immunological investigations showed a normal lymphocyte count with isolated CD4-penia, absence of naïve T cells, marked hypergammaglobulinemia, and maternal T cell engraftment. Targeted next generation sequencing (NGS) revealed two novel compound heterozygous mutations in the IL-7Rα gene: c.160T>C (p.S54P) and c.245G>T (p.C82F). The atypical onset and the unusual immunological phenotype expressed by our patient highlights the diagnostic challenge in the field of primary immunodeficiencies (PID) and in particular in SCID patients where prompt diagnosis and therapy greatly affects survival.
Rare Earth Element (REE)-Dependent Growth of Pseudomonas putida KT2440 Relies on the ABC-Transporter PedA1A2BC and Is Influenced by Iron Availability

In the soil-dwelling organism Pseudomonas putida KT2440, the rare earth element (REE)-utilizing, and pyrroloquinoline quinone (PQQ)-dependent ethanol dehydrogenase PedH is part of a periplasmic oxidation system that is vital for growth on various alcoholic volatiles. Production of PedH and its Ca2+-dependent counterpart PedE is inversely regulated in response to lanthanide (Ln3+) bioavailability, a mechanism termed the REE-switch. In the present study, we demonstrate that copper, zinc, and in particular, iron availability influences this regulation in a pyoverdine-independent manner by increasing the minimal Ln3+ concentration required for the REE-switch to occur by several orders of magnitude. A combined genetic and physiological approach reveals that an ABC-type transporter system encoded by the gene cluster pedA1A2BC is essential for efficient growth on 2-phenylethanol with low (nanomolar) Ln3+ concentrations. In the absence of pedA1A2BC, a ∼100-fold higher La3+-concentration is needed for PedH-dependent growth but not for the ability to repress growth based on PedE activity. From these results, we conclude that cytoplasmic uptake of lanthanides through PedA1A2BC is essential to facilitate REE-dependent growth on 2-phenylethanol under environmental conditions with poor REE bioavailability. Our data further suggest that the La3+/Fe2+/3+ ratio impacts the REE-switch through the mismetallation of putative La3+-binding proteins, such as the sensor histidine kinase PedS2, in the presence of high iron concentrations. As such, this study provides an example for the complexity of bacteria-metal interactions and highlights the importance of medium compositions when studying physiological traits in vitro in particular in regard to REE-dependent phenomena.
Study of Machine Learning Approaches for Object Tracking in Augmented Reality

Augmented reality (AR) overlaps computer-generated virtual content over real-world images using computing devices with an integrated camera and specialized software. In order to provide a seamless fusion illusion of virtual objects into the real scenes, object tracking is performed. Object tracking techniques play an important role in AR systems, but these techniques are very sensitive to different factors inherent to the image acquisition system and the environment, making their development a very challenging issue. Among others, object occlusion is one of the most difficulting situations in an object tracking system, degrading the whole AR experience. There are different approaches to solve occlusion problems in object tracking. However, recent advances in technology and computational methods suggest that, the inclusion of new technologies, such as machine learning, offers a potential approach to the search of solutions to the problems related to AR systems.