Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimação de parâmetros da cinética de trânsito de partículas em bovinos sob pastejo por diferentes seqüências amostrais

The objective of this study was to evaluate the fitness capacity of a non-linear model in the process parameter estimation of the solid transit kinetics of bovine at pasture, using different sequences of fecal sampling. Five F1 Limousin X Nellore steers, fistulated in esophagus and rumen on Brachiaria decumbens grazing and concentrate supplemented during the rainy season period were used. The experiment was developed in three experimental periods; in a randomized completely blocks design. Chromium mordant, produced from extrusa samples, was used as marker. The double exponential time-dependent model was adjusted to the curves of fecal excretion of marker, and the following sample sequences were used: SEQ 1 - 22 samples (complete sample sequence); SEQ 2 and SEQ 3 - 17 samples (reduction of collection points in the ascendant and descendent phases of the curve, respectively); and SEQ 4 and SEQ 5 - 13 and 10 fecal samples (reduction of the number of samples in the whole profile of the curve). The reduced sequences were produced starting from the omission of specific points of the complete sample sequence (SEQ 1). The comparison among the estimates of the kinetic parameters and fecal excretion; the descriptive analysis of the necessary number of iterations to the convergence of the model and of the determination coefficient; and the evaluation of lack of fitness showed no differences among the sequences. However, the residual analysis pointed improvements in the graphic behavior and runs of signal profile of the residues with the reduction of the number of collections in SEQ 4 and SEQ 5. In function of small efficiency loss, evaluated from the residual variance, observed in SEQ 5, the employment of 13 fecal collections (SEQ 4) for evaluation of the fecal excretion curve of the marker in similar studies was recommended.
An Unstructured Newton-Krylov Solver for the Compressible Navier-Stokes Equations

We present our recent progress using the Newton-Krylov method to solve three-dimensional aerodynamic flows. An unstructured-grid approach is utilized to handle complex geometries that arise in practical industrial designs. The Spalart and Allmaras turbulence model is used to calculate turbulent eddy viscosities. We employ Newton’s method to obtain the steady-state flow solution for a potentially fast convergence rate, with the linear system that arises solved by a preconditioned Krylov method. Issues with regard to preconditioning of the viscous operator in three dimensions are addressed. An incomplete factorization preconditioner applied to a matrix involving only the nearest neighboring terms is chosen based on numerical experiments. The capability of the current solver is demonstrated by numerical studies over the ONERA M6 wing as well as the DLR-F6 wing-body configuration.
An Finite Element Analysis of Patellar and Quadriceps Tendon Injury during Squatting

Squatting is one of the most fundamental exercises among sports. However, heavy loadings or improper posture may lead to severe injury at the knee, more specifically at the quadriceps tendon. The majority of in vivo and in vitro finite element analysis studies regarding the effects of weightlifting on the knee revolves around subjects who are very familiar to high intensity exercises. Little has been discussed about the effects of squatting on the knee on the average man. Our objective is to find the effects of squatting on the average man that does not frequent in intense exercise. Two different approaches were taken. One method consists of recreating a computational 3D model of an MRI of a healthy male’s knee and, through finite element analysis (FEA), solve for internal loadings by adding appropriate boundary conditions and material coefficients. The other method dwells on capturing subjects squatting with motion capture video and analyzing the kinematics and kinetics data involved in the exercise. Both manners serve as reciprocal validation.
Course Recommender Systems with Statistical Confidence

Selecting courses in an open-curriculum education program is a diﬃcult task for students and academic advisors. Course recommendation systems nowadays can be used to reduce the complexity of this task. To control the recommendation error, we argue that course recommendations need to be provided together with statistical conﬁdence. The latter can be used for computing a statistically valid set of recommended courses that contains courses a student is likely to take with a probability of at least 1 − (cid:15) for a user-speciﬁed signiﬁcance level (cid:15) . For that purpose, we introduce a generic algorithm for course recommendation based on the conformal prediction framework. The algorithm is used for implementing two conformal course recommender systems. Through experimentation, we show that these systems accurately suggest courses to students while maintaining statistically valid sets of courses recommended.
A Hybrid (ACO-PSO) Algorithm Based on Maximum Power Point Tracking and its Performance Improvement within Shadow Conditions

In this study, a hybrid Ant Colony Optimization-Particle Swarm Optimization (ACO-PSO) algorithm was proposed to optimally determine the maximum power point tracking (MPPT) parameters. The main goal of current study is to improve the overall performance of the MPPT system.The efforts of oscillation filtering and noise suppression were taken in this design, as well as the time response and the settling time. The proposed method is employed to track the global MPP under different shadow conditions, based on three different irradiation levels to test the ability and accuracy of the proposed method. The results of tracking MPP by the proposed MPPT technique showed that the improved method tracked the MPP for all the tested cases with a reasonable accuracy and in a very short convergence time as compared to the P&O method.
Toward a deep convolutional LSTM for eye gaze spatiotemporal data sequence classification

Eye gaze movements analysis are being increasingly used in many researches within learning context. Most of those re-searches analyses the eye movements ﬁxations inside some areas of interest, the saccades trajectory and the scanpath. The eye gaze data are spatiotemporal sequences representing the dynamic of the eye ﬁxations in the visual space over the time. In addition, they contain noises caused by diﬀer-ent factors. The task of developing predictive model based on those raw spatiotemporal eye gazes’ sequences is challenging. In this research, we present machine learning approaches that we have successfully used to address those challenges with high accuracy mainly with the deep convolutional LSTM architecture.
Effects of daily handling and other factors on weight gain of mice from birth to six weeks of age

The results of this experiment show that growth rate in mice is so sensitive to environmental factors that any change in the environment such as handling or diet could cause short-term changes in the rate of growth. It is also shown that if there is a period of restricted growth due to an environmental factor such as either handling or diet, this may be followed by a period of compensating rapid growth.
Next-Term Grade Prediction: A Machine Learning Approach

As students progress in their university programs, they have to face many course choices. It is important for them to re-ceive guidance based on not only their interest, but also the “predicted” course performance so as to improve learning experience and optimise academic performance. In this paper, we propose the next-term grade prediction task as a useful course selection guidance. We propose a machine learning framework to predict course grades in a speciﬁc program term using the historical student-course data. In this framework, we develop the prediction model using Factorization Machine (FM) and Long Short Term Memory combined with FM (LSTM-FM) that make use of both student and course attributes as well as past student-course grade data. Our experiment results on a real-world data of an autonomous university in Singapore show that both methods yield better prediction accuracy than the baseline methods. Our methods are also robust to handle cold start courses with the average prediction error can be as low as three quarter grade diﬀerence from the ground truth.
Pick the Moment: Identifying Critical Pedagogical Decisions Using Long-Short Term Rewards

Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identiﬁcation. RL is a machine learning area concerning with inducing eﬀective decision-making policies, following which result in the maximum cumulative reward . Many RL algorithms ﬁnd the optimal policy via estimating the optimal Q-values, which specify the maximum cumulative reward the agent can receive. In our LSTR framework, the long term rewards are deﬁned as Q-values and the short term rewards are determined by the reward function . Experiments on a synthetic GridWorld game and real-world Intelligent Tutoring System datasets show that the proposed LSTR framework indeed identiﬁes the critical decisions in the sequences. Furthermore, our results show that carrying out the critical decisions alone is as eﬀective as a fully-executed policy.
Social Media Mining to Understand the Impact of Co-operative Education on Mental Health

Co-operative education is a form of work-integrated learning that includes both classroom study terms and paid work experience. Research on co-operative education focuses on its benefits to students, employers, and academic institutions. In contrast, we study the impact of co-operative education on students’ mental well-being. To do so, we mine social media content on the Reddit platform, which includes, among many other topics, discussion forums for major U.S. and Canadian colleges. Specifically, we perform topic modelling of discussions related to mental health and co-operative education. We find that students report feelings of self-doubt resulting from a competitive co-op job market, especially when placed in entry-level jobs that are not related to their academic programs, and anxiety due to job interviews, especially when they coincide with exams and other academic deadlines.
A Comparative Study of Traditional and Compressed Scheduling on Undergraduate Construction Students’ Performance

There is a continual need to modify the way tertiary institutions do business to meet the needs of a changing society. The focus has been on success and retention whereas the new strategy in New Zealand supports wider economic growth and prosperity. There is a need for tertiary organisations to think about existing models and means of delivery, inclusive of new and emerging technologies as well as a continued expectation of the ability for tertiary institutions to deliver content via time and cost efficient means. Traditional scheduling involves concurrent enrolment in numerous courses with less contact time over a 16week timeframe, whereas compressed scheduling focusses on 2 courses per 8 weeks with more instructional time per week. This paper evaluates the use of compressed scheduling methods for first and second year courses on an undergraduate programme in construction in New Zealand. The quantitative study compares end of course exam results, gender, age enrolment and residency status of 2 first year courses and 3 second year courses for the students from 2011 to 2016. The outcomes are consistent with the literature and support the proposition that a similar student learning experience can be achieved in traditional and compressed courses. The findings of the study suggest that concerns associated with offering courses or providing alternative teaching pedagogies associated with traditional and compressed scheduling are unfounded. Interestingly the females in the study performed better in a compressed schedule as compared to a traditional schedule and warrants further research.
PROJECTION INFERENCE FOR SET-IDENTIFIED SVARS.1

We study the properties of projection inference for set-identified Structural Vector Autoregressions. A nominal 1 − α projection region collects the structural parameters that are compatible with a 1 − α Wald ellipsoid for the model’s reduced-form parameters (autoregressive coefficients and the covariance matrix of residuals). We show that projection inference can be applied to a general class of stationary models, is computationally feasible, and—as the sample size grows large—it produces regions for the structural parameters and their identified set with both frequentist coverage and robust Bayesian credibility of at least 1 − α. A drawback of the projection approach is that both coverage and robust credibility may be strictly above their nominal level. Following the work of Kaido, Molinari, and Stoye (2016), we ‘calibrate’ the radius of the Wald ellipsoid to guarantee that—for a given posterior on the reduced-form parameters— the robust Bayesian credibility of the projection method is exactly 1 − α. If the bounds of the identified set are differentiable, our calibrated projection also covers the identified set with probability 1 − α. We illustrate the main results of the paper using the demand/supply-model for the U.S. labor market in Baumeister and Hamilton (2015).
More Data and Better Keywords Imply Better Educational Transcript Classification?

Building and especially improving a classiﬁcation kernel represents a challenging task. The works presented in this paper continue an already developed semi-supervised classiﬁcation approach that aimed at labelling transcripts from educational videos. We questioned whether the size of the ground-truth data-set (Wikipedia articles) or the quality of the key-words used in the semi-supervised labelling have a signiﬁcant impact on the accuracy metrics of the ﬁnal obtained data model. Experimental results took into consideration three Wikipedia data-sets of Small , Medium and Large sizes. For each data-set there were used three sets of keywords: oﬀered by video authors, determined by rake-nltk on available transcripts and determined by rake-nltk on Wikipedia articles that serve as training and testing data for the LDA model that determine keywords on the transcripts. Experiments show that the size of the data-set has little importance, while the quality of the keywords has a more signiﬁcant impact. Therefore, an improved version of the previously developed classiﬁer has been obtained by improving the quality of the keywords involved in semi-supervised training. This result paves the way towards further improvements that may ﬁ-nally be deployed as within a recommender system of educational videos at the Universitat Polit`ecnica de Val`encia.
Trade Patterns in East Asia: Implications for the Debate on Mega- FTA Initiatives

This paper documents and analyzes emerging trade patterns in Asia, with special reference to the implications of the development of global production sharing with a view to informing the contemporary policy debate on forming two mega-FTA initiatives, Trans-Pacific Partnership (TPP) and Regional Comprehensive Economic Partnership (RCEP). The analysis reveals that the degree of dependence of these countries on this new form of global division of labour in East Asia is much larger compared to Europe and North America. Global production sharing has certainly strengthened economic interdependence among the countries in the region, but the dynamism of the regional cross-border production networks depends inexorably on global, rather than regional, trade in final goods. The findings of this paper make a strong case for a global, rather than a regional, approach to trade and investment policy making.
Proximate analysis, phtochemical screening and antioxidant activity of different strains of Ganoderma lucidum (Reishi Mushroom)

Ganoderma lucidum, a medicinal macrofungi, has been hailed in the orient since ancient times [1]. Content of numerous biocomponents has allured this mushroom globally [2]. The last decade has witnessed G. lucidum – based pharmaceuticals, cosmetics and toiletries. These are produced from different parts of the mushroom, including mycelia, spores, and fruit body. The specifi c applications and attributed health benefi ts of G. lucidum include control of blood glucose levels, modulation of the immune system, hepatoprotection, bacteriostasis, and more. Though beliefs regarding the health benefi ts of are based largely on anecdotal evidence, traditional use and cultural mores, recent reports provide scientifi c support to some of the ancient claims of the health benefi ts of G. lucidum [3]. More extensive study is called for elucidating the health benefi ts provided by this mushroom especially for the ever increasing food and nutrition oriented therapeutic purposes. In this regard, proximate analysis and phytochemical screening of G. lucidum spur high.
Modeling undesirable factor in efficiency assessment of agricultural sector within EU-27 countries and over a time period

Agriculture plays an important role in economy influencing both society and environment due to its crucial contribution to food security, employment opportunities and climate change. According to the energy and climate policy framework, the target for 2030 is reduction of 40% of greenhouse gas (GHG) emissions in the European Union without any decline in productivity of production. One critical challenge in assessing the productivity is involving the undesirable outputs a long with a series of time. This approach essentially requires applying new management methods. In this study we applied a slack based measurement (SBM) of efficiency in the presence of undesirable outputs in a window data envelopment analysis (WDEA) framework to evaluate the crop production sector of 27 EU countries. The required data consisting the average input costs (€ ha) (including fertilizer, protection, seed and other costs as the model inputs), the gross domestic product (€ ha) and the total GHG emissions (kg CO2-eq ha) as the model desirable and undesirable outputs were collected from FADN and Eurostat databases. To indicate the ideal window width, the efficiency scores of ten window data envelopment analysis (WDEA) models were evaluated. Obtained results showed the highest efficiency scores for Spain, Greece, Italy and Malta. To compare the proposed model with the conventional ones, the results of a SBM-WDEA incorporated with undesirable outputs have been compared with a SBM-WDEA model without undesirable output and a SBM-DEA model with undesirable output and without window analysis. The results advocate in favor of the new model, showing promise for its better performance.
Diagnosis and Dynamic Data Reconciliation in Complex Technological Systems

Abstrac t : In this paper, data verification and data reconciliation methods, based on the mass and energy balances in complex technological systems are considered. Diagnostic procedures for gross and systematic measurement errors detection and their isolation in dynamical mode are developed. The fuzzy logic methods are used for the problem solving of fault measurement diagnosis and data reconciliation. Some results of the proposed methods application on chemical plant are presented. CSCC'99 Proceedings, Pages:4831-4837
A Mathematical Theory of Natural and Artificial Selection, Part V: Selection and Mutation

New factors arise in a species by the process of mutation. The frequency of mutation is generally small, but it seems probable that it can sometimes be increased by changes in the environment (1,2). On the whole mutants recessive to the normal type occur more commonly than dominants. The frequency of a given type of mutation varies, but for some factors in Drosophila it must be less than 10−6, and is much less in some human cases. We shall first consider initial conditions, when only a few of the new type exist as the result of a single mutation; and then the course of events in a population where the new factor is present in such numbers as to be in no danger of extinction by mere bad luck. In the first section the treatment of Fisher (3) is followed.
Checking Invariant Confluence, In Whole or In Parts

Strongly consistent distributed systems are easy to reason about but face fundamental limitations in availability and performance. Weakly consistent systems can be implemented with very high performance but place a burden on the application developer to reason about complex interleavings of execution. Invariant confluence provides a formal framework for understanding when we can get the best of both worlds. An invariant confluent object can be efficiently replicated with no coordination needed to preserve its invariants. However, actually determining whether or not an object is invariant confluent is challenging. In this paper, we establish conditions under which a commonly used sufficient condition for invariant confluence is both necessary and sufficient, and we use this condition to design a general-purpose interactive invariant confluence decision procedure. We then take a step beyond invariant confluence and introduce a generalization of invariant confluence, called segmented invariant confluence, that allows us to replicate non-invariant confluent objects with a small amount of coordination. We implement these formalisms in a prototype called Lucy and find that our decision procedures efficiently handle common real-world workloads including foreign keys, escrow transactions, and more.
Malicious Face Book Application Using FRAppE Algorithm

With 20 million introduces multi day, outsider applications are a noteworthy purpose behind the notoriety and addictiveness of Facebook. Lamentably, programmers have understood the capability of utilizing applications for spreading malware and spam. The issue is as of now noteworthy, as we find that at any rate 13% of applications in our dataset are vindictive. Up until this point, the exploration network has concentrated on identifying malignant posts and battles. In this paper, we make the inquiry: Given a Facebook application, would we be able to decide whether it is malignant? Our key commitment is in creating FRAppE—Facebook's Rigorous Application Evaluator—ostensibly the primary device concentrated on distinguishing malignant applications on Facebook. To create FRAppE, we utilize data accumulated by watching the posting conduct of 111K Facebook applications seen crosswise over 2.2 million clients on Facebook. To begin with, we recognize an arrangement of highlights that assistance us recognize malevolent applications from amiable ones. For instance, we locate that pernicious applications frequently share names with different applications, and they ordinarily ask for less consents than generous applications. Second, utilizing these distinctive highlights, we demonstrate that FRAppE can identify vindictive applications with 99.5% exactness, with no false positives and a high obvious positive rate (95.9%). At last, we investigate the environment of vindictive Facebook applications and distinguish components that these applications use to proliferate. Curiously, we locate that numerous applications conspire and bolster one another; in our dataset, we find 1584 applications empowering the viral proliferation of 3723 different applications through their posts. Long haul, we consider FRAppE to be a stage toward making an autonomous guard dog for application appraisal and positioning, in order to caution Facebook clients before introducing applications.