Miracle-dz/UniFAR-FTUs huggingface dataset:
Relações interpessoais entre os profissionais da saúde do Consultório na Rua

This study aimed to analyze the impact of interpersonal relationships among the healthcare professionals of the Street Clinic in a capital of Southern Brazil, when serving the homeless population. The Street Clinic is responsible for the entire homeless population of this municipality access to health services. A qualitative research was conducted with an interview with professionals who are part of this service, document analysis and participant observation. As the team included professionals hired by different organizations, an initial resistance to teamwork was seen. On the other hand, as interpersonal relationships between team members were built, an increase in their Grupo de Pesquisa em Avaliação da qualidade de vida e qualidade de vida no trabalho Ponta Grossa – PR – Brasil v. 05, n. 02, jul./dez. 2020, p. 22-33 DOI: 10.24222/2525-3395.2020v5n2p022 REVISTA STRICTO SENSU
Microstructure and Mechanical Properties of Si3N4 Based Biocomposites

Silicon nitride (Si3N4) is a novel non-oxide bioceramic material that is used as spinal fusion implants, cervical spacers, surgical screws, prosthetic hip and knee joints. Among ceramics, due to good wear properties, biocompatibility and less sensitivity to slow crack growth, Si3N4 is a potential candidate in load bearing applications. However, usage of Si3N4 in biologic systems needs high densification. In order to improve mechanical properties of Si3N4, different biocompatible carbon nano-forms; carbon nanotubes (CNT) and graphene nanoplatelets (GNP) were incorporated into the Si3N4 matrix. Monolithic Si3N4, Si3N4-GNP and Si3N4-CNT composites were produced by spark plasma sintering (SPS). The effects of additives on densification and microstructural behavior, and mechanical properties were investigated
Partial molar compressibility of ions in aqueous solutions of some rare Earth chlorides and nitrates

For some time investigations of the state hydration of ions in water solvent have been concerned with the specific they exhibit in their interactions with water and the corresponding structural reaction the water in response to the presence of the ion. These structure changes are associated with electrostriction around the ion and changes of H-bonding equilibrium among water molecules. Specifities in ion-solvent interaction can only be examined if the individual ionic contribution to the property can be evaluated. In the present paper we report measurement of the apparent molar compressibility φK of some rare Earth chloride and nitrate in aqueous solutions and explore the dependence φK of ionic charge and radius. The individual ionic contribution in the partial molar compressibility⎯K2 of rare Earth chlorides in aqueous solutions has been estimated.
IDENTIFYING CLINICALLY METABOLIC SIGNATURES OF OBESITY IN “NORMAL WEIGHT - OBESITY MIMICKERS

Introduction : Sedentary life and altered dietary habits parallels with increase in normal weight but “lean mass” population. We attempt to study the prevalence of this problem. Material and Method : 182 (male = 73 & female = 109) students with normal Body Mass Index (BMI) were studied. Results : Mean age of either sex was 19 ± 1.23 years. Family history of diabetes mellitus was present in 47.46% and 45.83%; obesity in 2338.98% and 31.25%; hypertension in 18.64% and 12.5%; and coronary artery disease in 3 and 1 male and female students respectively. 13 male and 14 female students did not consent for waist and hip measurement. Students with normal BMI [N=145 (84.30%)], especially female [F=90 (52.33%)], performed mild exercises. Increased waist circumference (WC) and waist – hip ratio (WHR) were 4 & 12, and 12 & 26 found in these male and female students respectively. In students with low birthweight (M=4 & F=9), WC was increased in 01 male and 01 female, and WHR in 02 male and 03 female students. In normal birthweight students (M=13 & F=23), increased WC and WHR were present in 4 and 3, and 2 and 5 male and female students respectively. Conclusion : “Normal weight with Lean mass” students seem to be similar to metabolic active obese and we call them “ Normal Weight Obesity Mimickers ”. Female students are at risk of developing DM & HTN, whereas male at risk of HTN and obesity later on.
Gender, general anxiety, math anxiety and math achievement in early school-age children

Math anxiety is considered a predictor of mathematical achievement, but little is known about its characteristics in young children. In a longitudinal study of first to second grade children, the relationships between gender, general and math anxiety, and math achievement were tested. First, the results indicate that girls in comparison to boys have a higher level of general anxiety and that gender gap in math anxiety is affected by various types of math anxiety. Girls exceed boys in testing and total scores of math anxiety, however, no gender differences in learning math anxiety and math achievement in the first and second years were observed. Secondly, the full mediation effect of general anxiety in the relationship between gender and math anxiety was observed in firstand second-graders. Finally, the relationship between math anxiety and mathematical achievement in first and second grade children was tested, and the results depend on grade, gender, type of math anxiety and the level of general anxiety.
Digital Noncoherent Demodulator of Four-position Differential Phase Shift Keyed Signals

—There is introduced the fast digital noncoherent demodulator of high-frequency differential quadrature phase shift keyed signals that provides the minimum number of arithmetic operations over the carrier frequency period. Thus, for the first time, for digital noncoherent demodulators the problem is solved of increasing the speed of high-frequency signal processing in common data transmission systems, for example, in TETRA, PHS, SDMA, IEEE 802.11 and IEEE 802.11b, ZigBee, etc. These and other data transmission systems operate at low power consumption, and this would need to minimize FPGA resources required for the implementation of the demodulation of high-frequency signals. This can be achieved by minimizing the appropriate computational costs in processing the signals. For this purpose the algorithm is presented that allows fast processing of the signal samples with a subsequent decision on the received symbol. The properties of the responses of the demodulator and its frequency-selective properties are also demonstrated. There is studied the influence of phase and frequency mismatch on the performance of the demodulator. The expressions are found for the symbol-error probability for the case when the information signal is distorted by band Gaussian noise. It is proved that the considered algorithm provides optimal signal extraction against broadband interferences and demonstrates potential interference immunity in the presence of Gaussian noises. It is established that the obtained theoretical results are confirmed by statistical simulation. The
HIV/AIDS-related Knowledge and Behavior among School-attending Afro-Descendant Youths in Ecuador

Background or Objectives: HIV/AIDS transmission in Ecuador is considered a concentrated epidemic; therefore, there are some studies on high risk groups but there is limited published data regarding the HIV/AIDS risk factors among adolescents of African descent. In this study, we sought to explore the determinants of HIV/AIDS-related knowledge and behavior among afro-descendant youths attending schools in the city of Esmeraldas, Ecuador. Methods: A cross-sectional survey among school-attending youths was conducted in Esmeraldas, Ecuador in 2010. Our target population was afro-descendant youths attending the last two years of high school. Thirty public high schools enrolling students in junior and senior years were identified. Outcome data were analyzed in the form of three composite variables. A multivariate linear regression model was built for each outcome. Results: A total of 213 school-attending afro-descendant youths aged 14 to 21 years old were enrolled in this study. Gender distribution was almost equal with a 1:1.17 male to female ratio. Overall, students in this population scored well in comprehensive knowledge of HIV with 88% having medium or higher knowledge. Conclusion and Global Health Implications: Knowledge of HIV and its determinants was medium to high, but knowledge of sexually transmitted diseases was low among afro-descendant Ecuadorian adolescents in our study. Results of this study might be instrumental in facilitating decision-making processes related to the planning, implementation, and evaluation of HIV/AIDS prevention and control strategies in this specific population.
Corporate Governance Models and the Possibility of Future Convergence

This study explains the main corporate governance models used in the United States of America, United Kingdom, Germany and Japan by analyzing their similarities, differences, strengths and weaknesses. In addition, the possibility of future convergence between these models is discussed. Two types of corporate governance models are used in the world: Shareholder and Stakeholder models. In this study, USA, and UK will be analyzed as an example of the shareholder model, whereas Germany and Japan will be discussed as an example of the stakeholder model. The shareholder model emphasizes the benefits of shareholders and the management dominates the decision-making procedure of the companies. The stakeholder model, on the other hand, puts more emphasis on the interests of stakeholders or capital market players such as the workers, suppliers and the public. On the convergence debate, four different arguments are identified: The first and the main one is against convergence seeing it as a distant dream because of the differences between the countries. The second argument supports and expects convergence in the near future. The third argument supports the argument of functional convergence rather than formal convergence. The fourth argument supports the combination of both shareholder and stakeholder models to get effective corporate governance practices. This research supports the first argument which is against convergence, because looking at the differences between the countries in their economic, legal and political frameworks, it is still impossible to suggest convergence. Each country will continue to adopt its style according to its culture and also according to its differing needs.
Implicit questions shape information preferences

We ask questions about everything from why clocks tick to why the sky is blue. Although people sometimes prefer teleological explanations over mechanistic explanations in response to ‘why’ questions, why questions are ambiguous– referring either to a ‘how’ question or a ‘for what purpose’ question. In this paper, we examine the relation between these implicit questions and explanation preferences. First, we asked whether people have specific expectations regarding ‘why’ questions: How do they interpret these ambiguous cases and does this vary across domains? Indeed, people have strong, domain-specific expectations that mirror well-documented explanation preferences. People also have preferences about which specific question they would prefer to have answered. In other words, ‘why’ questions are ambiguous but not treated as such — and this has consequences for downstream explanation preferences. We explore these consequences in light of both the philosophical and psychological literature on explanation.
Does different levels of dried citrus sinensis peel affect on broilers gastrointestinal microbial population

The experiment was conducted to evaluate the effects of different levels of dried citrus sinensis peel (DCSP) on gastrointestinal microbial population of Ross broilers. Four-handred Ross 308 one-day broilers in a completely randomized design with five treatments (four replicates per treatment and each replicate had 20 chicks) were categorized. Each treatment used regulatory diet including 1.5 and 3 percent (DCSP) in the Base diet and in two periods of 1st-21st day and 1st-42nd days and base diet without any additive for six weeks. Data analysis was performed using SAS software and mean comparison was conducted by Duncan method. The results determined that the mean Lactobacilli in ileum on day 42 was not significantly different (p>0.05). The highest rate was concerned to 3% (DCSP) treatment up to the end of rearing period and the lowest one was related to 1.5% (DCSP) treatment up to the end of rearing period. The results from the mean of lactobacilli in cecum in the day 42 indicated that the mean of treatment was not significantly different (p>0.05). The highest rate was concerned to 3% (DCSP) treatment up to the end of rearing period and the lowest one was related to 1.5% (DCSP) treatment up to day 21.
The effectiveness of using augmented reality in teaching geography curriculum on the achievement and attitudes of Omani 10th Grade Students

This study aimed to investigate the effectiveness of using a mobile Augmented Reality application in the geography unit for 10th-grade students on their achievements and attitude. The unit is part of the Omani’s social studies curriculum and entitled, “Environmental Problems and Hazards”. To achieve this purpose, a quasi-experimental study design was conducted. The instruments designed, validated and used to collect data were pre-post achievement tests and attitudinal scale. The participants were 64 male students, divided into two experimental and control groups. The field application period took four weeks in which the control group studied the unit using the normal teaching method, while the mobile Augmented Reality application, HP Reveal, with a website that supports its features, was integrated to teach experimental group. Findings show that there was a statistically significant difference between the pre- and post-field applications in terms of students’ achievement and attitudes. Nevertheless, there was no impact observed when the control and experimental groups’ achievement was compared. The researcher suggested conducting further investigations to examine the value of integrating Augmented Reality in the educational system considering various factors and variables.
Semi-Circular Potential Sweep Voltammetry: Electrochemically Quasi-Reversible System

The novel voltammetry using a semi-circular potential wave for quasi-reversible charge transfer system on electrode is theoretically investigated. Compared with conventional voltammetry based on linear sweep such as linear sweep voltammetry (LSV), semi-circular potential sweep voltammetry (SCV) may decrease the charging current outside the center of potential range and increase the faradaic current at the midpoint due to variable scan rate. In this paper, we investigate the system based on macroelectrode where simple 1 dimensional (1 D) diffusion system is valid with various charge transfer rate constant (k0). In order to observe the amplification at midpoint, voltammetric response with different midpoint ranging from -200 mV to 200 mV are studied. SCVs shows both the shift of peak potential and the amplification of peak current for quasi-reversible electrode reaction while only higher peak current is observed for reversible reaction. Moreover, the higher current at midpoint enable the amplification of current at low overpotential region which may assist the determination of onset potential as a figure-of-merit in electrocatalyst.
ANATOMY OF TRAUMATIC ACID-TREATED INTERNODES OF RICINIUS COMMUNIS FLORA MURRAY SCOTT, BARBARA GILLOOLY BYSTROMl, AND VIRGINIA SJAHOLM

9. SNEDECOR, G. WV. Statistical Methods. Iowa State College Press, Ames. 1950. 10. STOWVE, B. B. , and VAMAKI, T. The history and physiological action of the gibberellins. Ann. Rev. Plant Physiol. 8:181-216. 1957. 11. TOOLE, E. H. ; HENDRICKS, S. B. ; BORTHIWICK, H. A. ; and TOOLE, V. K. Physiology of seed germination. Ann. Rev. Plant Physiol. 7:299-324. 1956. 12. VAN OVERBEEK, J.; BLONDEAU, R. ; and HORNE, V. Differences in activity between 2,4-dichlorophenoxyacetic acid and other auxins, and its significance in herbicidal action. Plant. Physiol. 26:687-696. 1951.
Learning from Suboptimal Demonstration via Self-Supervised Reward Regression

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. However, modern LfD techniques, such as inverse reinforcement learning (IRL), assume users provide at least stochastically optimal demonstrations. This assumption fails to hold in all but the most isolated, controlled scenarios, reducing the ability to achieve the goal of empowering real end-users. Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings through Preference-based Reinforcement Learning (PbRL) to infer a more optimal policy than the demonstration. However, we show that these approaches make incorrect assumptions and, consequently, suffer from brittle, degraded performance. In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function. We empirically validate we can learn an idealized reward function with $\sim0.95$ correlation with the ground truth reward versus only $\sim 0.75$ for prior work. We can then train policies achieving $\sim 200\%$ improvement over the suboptimal demonstration and $\sim 90\%$ improvement over prior work. Finally, we present a real-world implementation for teaching a robot to hit a topspin shot in table tennis better than user demonstration.
Maximum Mean Discrepancy is Aware of Adversarial Attacks

The maximum mean discrepancy (MMD) test, as a representative two-sample test, could in principle detect any distributional discrepancy between two datasets. However, it has been shown that MMD is unaware of adversarial attacks---MMD failed to detect the discrepancy between natural data and adversarial data generated by adversarial attacks. Given this phenomenon, we raise a question: are natural and adversarial data really from different distributions but previous use of MMD on the purpose missed some key factors? The answer is affirmative. We find the previous use missed three factors and accordingly we propose three components: (a) Gaussian kernel has limited representation power, and we replace it with a novel semantic-aware deep kernel; (b) test power of MMD was neglected, and we maximize it in order to optimize our deep kernel; (c) adversarial data may be non-independent, and to this end we apply wild bootstrap for validity of the test power. By taking care of the three factors, we validate that MMD is aware of adversarial attacks, which lights up a novel road for adversarial attack detection based on two-sample tests.
Identifying Learning Rules From Neural Network Observables

The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem. Simulating idealized neuroscience experiments with artificial neural networks, we generate a large-scale dataset of learning trajectories of aggregate statistics measured in a variety of neural network architectures, loss functions, learning rule hyperparameters, and parameter initializations. We then take a discriminative approach, training linear and simple non-linear classifiers to identify learning rules from features based on these observables. We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes, and that these results generalize to limited access to the trajectory and held-out architectures and learning curricula. We identify the statistics of each observable that are most relevant for rule identification, finding that statistics from network activities across training are more robust to unit undersampling and measurement noise than those obtained from the synaptic strengths. Our results suggest that activation patterns, available from electrophysiological recordings of post-synaptic activities on the order of several hundred units, frequently measured at wider intervals over the course of learning, may provide a good basis on which to identify learning rules.
What are the Statistical Limits of Offline RL with Linear Function Approximation?

Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help alleviate the excessive sample complexity burden in modern sequential decision making problems. However, the extent to which this broader approach can be effective is not well understood, where the literature largely consists of sufficient conditions. This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning. Perhaps surprisingly, our main result shows that even if: i) we have realizability in that the true value function of \emph{every} policy is linear in a given set of features and 2) our off-policy data has good coverage over all features (under a strong spectral condition), then any algorithm still (information-theoretically) requires a number of offline samples that is exponential in the problem horizon in order to non-trivially estimate the value of \emph{any} given policy. Our results highlight that sample-efficient offline policy evaluation is simply not possible unless significantly stronger conditions hold; such conditions include either having low distribution shift (where the offline data distribution is close to the distribution of the policy to be evaluated) or significantly stronger representational conditions (beyond realizability).