Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Multigrid Algorithmsfor the Navier–Stokes Equations

Anisotropies occur naturally in computational fluid dynamics where the simulation of small-scale physical phenomena, such as boundary layers at high Reynolds numbers, causes the grid to be highly stretched, leading to a slowdown in convergence of multigrid methods. Several approaches aimed at making multigrid a robust solver have been proposed and analyzed in the literature using the scalar diffusion equation. However, they have rarely been applied to solving more complicated models, such as the incompressible Navier–Stokes equations. This paper contains the first published numerical results of the behavior of two popular robust multigrid approaches (alternating-plane smoothers combined with standard coarsening and plane-implicit smoothers combined with semi-coarsening) for solving the 3-D incompressible Navier–Stokes equations in the simulation of the driven-cavity and a boundary layer over a flat plate on a stretched grid. Grid size, grid stretching, and Reynolds number are the factors considered in evaluating the robustness of the multigrid methods. Both approaches yield large increases in convergence rates over cellimplicit smoothers on stretched grids. The combination of plane-implicit smoothers and semi-coarsening was found to be fully robust in the flat-plate simulation up to Reynolds numbers 10 6 and the best alternative in the driven-cavity simulation for Reynolds numbers above 10 3. The alternating-plane approach exhibits a better behavior for lower Reynolds numbers (below 10 3) in the driven-cavity simulation.
Using Machine Learning to Reduce Ensembles of Geological Models for Oil and Gas Exploration

Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However estimating the amount of Oil In Place (OIP) relies on computing with a very significant number of geological models which, due to the ever increasing capability to capture and refine data, is becoming infeasible. As such data reduction techniques are required to reduce this set down to a much smaller yet still fully representative ensemble. In this paper we explore different approaches to identifying the key grouping of models, based on their most important features, and then using this information select a reduced set which we can be confident fully represent the overall model space. The result of this work is an approach which enables us to describe the entire state space using only 0.5% of the models, along with a series of lessons learnt. The techniques that we describe are not only applicable to oil and gas exploration, but also more generally to the HPC community as we are forced to work with reduced data-sets due to the rapid increase in data collection capability.
Long-term seasonal characterization and evolution of extreme drought and flooding variability in northwest Algeria

A three decade-long study on the variability of drought in relation to the contribution of rainfall was conducted at the Wadi Mekerra watershed, located in northwest Algeria, covering the period from 1973 to 2005. The runoff and rainfall data were analyzed using the Mann-Kendall test, the double mass curve method and the SPI index. A rupture of the studied series appeared during the 1980s. The rainfall and runoff trends and contributions were in general, sharply reduced. The region experienced extreme drought between 1981 and1989, and between 1993 and 2001, rainfall contributions were greater than 60%. This increase, which was recorded in August, September and October for all the parameters studied, shows the importance of the superficial runoff component when combined with decreased infiltration. These climatic conditions reduce the natural recharging of groundwater, and cause an increased susceptibility to soil erosion, reduced agricultural production and an increased risk of floods.
Neopterin and CXCL-13 in Diagnosis and Follow-Up of Trypanosoma brucei gambiense Sleeping Sickness: Lessons from the Field in Angola

Human African Trypanosomiasis may become manageable in the next decade with fexinidazole. However, currently stage diagnosis remains difficult to implement in the field and requires a lumbar puncture. Our study of an Angolan cohort of T. b. gambiense-infected patients used other staging criteria than those recommended by the WHO. We compared WHO criteria (cell count and parasite identification in the CSF) with two biomarkers (neopterin and CXCL-13) which have proven potential to diagnose disease stage or relapse. Biological, clinical, and neurological data were analysed from a cohort of 83 patients. A neopterin concentration below 15.5 nmol/L in the CSF denoted patients with stage 1 disease, and a concentration above 60.31 nmol/L characterized patients with advanced stage 2 (trypanosomes in CSF and/or cytorachia higher than 20 cells) disease. CXCL-13 levels below 91.208 pg/mL denoted patients with stage 1 disease, and levels of CXCL-13 above 395.45 pg/mL denoted patients with advanced stage 2 disease. Values between these cut-offs may represent patients with intermediate stage disease. Our work supports the existence of an intermediate stage in HAT, and CXCL-13 and neopterin levels may help to characterize it.
Estimation of Impedance and Susceptance Parameters of a 3-Phase Cable System Using PMU Data

This paper proposes a new regression-based method to estimate resistance, reactance, and susceptance parameters of a 3-phase cable segment using phasor measurement unit (PMU) data. The novelty of this method is that it gives accurate parameter estimates in the presence of unknown bias errors in the measurements. Bias errors are fixed errors present in the measurement equipment and have been neglected in previous such attempts of estimating parameters of a 3-phase line or cable segment. In power system networks, the sensors used for current and voltage measurements have inherent magnitude and phase errors whose measurements need to be corrected using calibrated correction coefficients. Neglecting or using wrong error correction coefficients causes fixed bias errors in the measured current and voltage signals. Measured current and voltage signals at different time instances are the variables in the regression model used to estimate the cable parameters. Thus, the bias errors in the sensors become fixed errors in the variables. This error in variables leads to inaccuracy in the estimated parameters. To avoid this, the proposed method uses a new regression model using extra parameters which facilitate the modeling of present but unknown bias errors in the measurement system. These added parameters account for the errors present in the non- or wrongly calibrated sensors. Apart from the measurement bias, random measurement errors also contribute to the total uncertainty of the estimated parameters. This paper also presents and compares methods to estimate the total uncertainty in the estimated parameters caused by the bias and random errors present in the measurement system. Results from simulation-based and laboratory experiments are presented to show the efficacy of the proposed method. A discussion about analyzing the obtained results is also presented.
Circularly Polarized Retrodirective Antenna Array for Wireless Power Transmission

A retrodirective antenna (RDA) array for wireless power transmission (WPT) is presented. Applications include the wireless charging of mobile phones and other handheld devices. The reported RDA defines an active high-power transmitter module that retrodirects a received beacon tone back to a mobile unit by circularly polarized (CP) free-space radiation. In addition, this RDA architecture uses a network of four subarrays, defined by a total of 16 radiating patch elements, in an effort to boost the transmit gain while also reducing the supporting RF hardware requirements when compared to a more conventional RDA. Measurements and simulations in the reactive near-field of the system are in agreement in terms of the tracking capabilities of the high-power CP-RDA. Power levels in excess of 27 dBm were measured at 2.4 GHz at a receiver module, and when this RF power was rectified, more than 350 mW at dc was observed. To the best knowledge of the authors, this is the first demonstration of a WPT system using a mixer-based analog RDA. Previous low-power demonstrations were more complex computer-controlled systems that did not offer any real-time tracking ability. Other applications for the proposed RDA include target tracking, sensor charging, and other WPT systems.
Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure

Reinforcement learning (RL) is a model-free adaptive approach to learn a non-linear control law for flight control. However, for flat-RL (FRL) the size of the search space grows exponentially with the number of states, resulting in low sample efficiency. This research aims to improve the efficiency with Hierarchical Reinforcement Learning (HRL). Performance in terms of the number of samples and the mean tracking error is evaluated on an altitude reference tracking task using a simulated F16 aircraft model. FRL is used as the baseline performance index. HRL is used to define a three-level learning structure, re-using an existing flight control structure. Finally, options is used with HRL to add temporal abstraction. It is shown that by re-using the flight control structure the learning process is made more sample efficient. Adding options further increases this efficiency, but does not lead to better tracking performance.
NATURAL QUESTION GENERATION

Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii) solely rely on cross-entropy loss that leads to issues like exposure bias and inconsistency between train/test measurement, or (iii) fail to fully exploit the answer information. To address these limitations, in this paper, we propose a reinforcement learning (RL) based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator with a novel Bidirectional Gated Graph Neural Network based encoder to embed the passage, and a hybrid evaluator with a mixed objective combining both cross-entropy and RL losses to ensure the generation of syntactically and semantically valid text. We also introduce an effective Deep Alignment Network for incorporating the answer information into the passage at both the word and contextual levels. Our model is end-to-end trainable and achieves new state-of-the-art scores, outperforming existing methods by a significant margin on the standard SQuAD benchmark.
Analysing the Service Delivery Gap for a Power Utility Company in the Pre 2016 Period: A Case Study of Zimbabwe Electricity Transmission and Distribution Company (ZETDC)

Due to rising customer dissatisfaction in service quality delivery by ZETDC customers, it became imperative that a study be undertaken to identify areas of mismatch in the expected service and the actual service delivered. Gap 3 of the gaps model proposed by Parasuraman et al. (1985) was applied to ZETDC Marondera to identify the extent of the delivery gap to enable the designing of a service quality improvement program that could be adopted by the service provider. A quantitative research design was carried out using a cross sectional descriptive survey, where a self-completion questionnaire was developed for the gap model and distributed to a sample of 250 respondents using a convenience sampling technique to ZETDC customers in Marondera who included domestic and rural households, commercial institutions and farmers to determine their perceptions of service quality. Data analysis was conducted on the 210 responses that were returned. Findings show the Service delivery gap is +0.125. This is a positive rating which means that no gap exists between service quality specifications and actual service delivery.
Facial Emoticons – Facial Expressions as a Means of Interaction

Facial expressions are highly effective non-verbal means to share emotions among humans. Nowadays, in the cybernetic panorama, emoticons are used as surrogate facial expressions in electronic-mediated communication. As a new modality, it provides a natural means of interaction, opening a feedback channel that makes it possible to interact without using a keyboard or a mouse. We present FacialEmoticons, a facial expression recognition library that can be used as the basis for meaningful interaction. After face tracking and feature detection, Bayesian Classifiers are used to infer the expression on the user’s face. Our methodology requires low computational power yielding recognition rates comparable to those of humans. Our results show the viability for facial-expression-based real-time interaction.
(2017). Multiple Deprivation, Severity and Latent Sub-Groups: Advantages of Factor Mixture Modelling for Analysing Material

Material deprivation is represented in different forms and manifestations. Two individuals with the same deprivation score (i.e. number of deprivations), for instance, are likely to be unable to afford or access entirely or partially different sets of goods and services, while one individual may fail to purchase clothes and consumer durables and another one may lack access to healthcare and be deprived of adequate housing. As such, the number of possible patterns or combinations of multiple deprivation become increasingly complex for a higher number of indicators. Given this difﬁculty, there is interest in poverty research in understanding multiple deprivation, as this analysis might lead to the identiﬁcation of meaningful population sub-groups that could be the subjects of speciﬁc policies. This article applies a factor mixture model (FMM) to a real dataset and discusses its conceptual and empirical advantages and disadvantages with respect to other methods that have been used in poverty research. The exercise suggests that FMM is based on more sensible assumptions (i.e. deprivation covary within each class), provides valuable information with which to understand multiple deprivation and is useful to understand severity of deprivation and the additive properties of deprivation indicators.
Le traitement percutané du carcinome hépato-cellulaire

Le carcinome hépatocellulaire (CHC) est une des tumeurs malignes les plus fréquentes (notamment chez les sujets originaires d’Afrique australe ou du Sud-Est asiatique). La survie spontanée est de l’ordre de 1 à 6 mois après la découverte de la tumeur. Jusqu’à présent, le seul traitement considéré comme curatif est la chirurgie, au moyen d’hépatectomie réglée ou de transplantation. Suivant les séries, une survie à 5 ans est alors possible pour 12 à 46 % des malades. Chez les malades cirrhotiques, la surveillance échographique régulière peut permettre de découvrir des tumeurs sans expression clinique, ce qui rend possible la mise en œuvre d’un traitement. La chirurgie est le traitement de choix chez les malades qui peuvent en bénéficier. Il a toutefois fallu développer des techniques palliatives en raison du petit nombre de malades opérables, inférieur à 10 %, soit en raison du caractère diffus ou multifocal du CHC, soit en raison du mauvais état du foie non tumoral chez les malades cirrhotiques. Le carcinome hépato-cellulaire représente 5 % de tous les cancers dans le monde (500 000 morts/an).
The Relationship between Spasticity and Lower Extremity Strength with Functional Mobility Following Chronic Stroke

Background: Spasticity and muscle weakness are the primary impairments that result in activity limitation after stroke. Functional mobility is the ability to transfer independently from one place to another that depends on the extent of impairments affecting body function. The knowledge of relationship between the physical consequences of stroke and functional limitation helps therapist to implement the most effective rehabilitation approach to improve mobility. Objective: The purpose of this study was to clinically assess the relationship between spasticity and lower extremity strength with functional mobility in hemi paretic stroke subjects. Methods: In this cross sectional analytical study using a convenience sampling, 30 (18 men, 12 women) participants with post stroke duration of 3-24 months participated. Spasticity of knee extensor and ankle plantar flexors was evaluated with Modified Tardieu scale. Lower extremity strength was measured with Motricity Index. Functional mobility was assessed by the Rivermead mobility index, Timed Up and Go test, 6 Min Walk Test and 10- Meter Walk Test. For analysis of data the Pearson correlation coefficient was used. Results: The results showed that there was no statistical significant relationship between the lower extremity spasticity and all functional mobility variables. The lower extremity strength and functional mobility variables were significantly correlated (p 0.70). Conclusion: It seemed that lower extremity spasticity was not correlated to functional mobility after stroke. The rehabilitation for decreasing lower extremity spasticity would not be functionally efficient. There should be increased focus on rehabilitation of lower extremity strength in order to enhance functional mobility.
Temperature Transient Tests: Modeling, Interpretation, and Nonlinear Parameter Estimation

This study presents semi-analytical and analytical solutions based on a coupled transient wellbore/reservoir thermal model to predict temperature transient measurements made under constant rate and bottom-hole pressure production as well as variable rate and bottom-hole pressure production histories in a vertical or an inclined wellbore across from the producing horizon or at a gauge depth above it. Slightly compressible, single-phase, and homogeneous infinite-acting single-layer geothermal reservoir system is considered. The models account for Joule-Thomson heating/cooling, adiabatic fluid expansion, conduction and convection effects both in the reservoir and wellbore. The transient wellbore model accounts for friction and gravity effects. The solutions of the analytical and semi-analytical reservoir models are verified by use of a general-purpose thermal simulator. Wellbore temperatures at a certain gauge depth are evaluated through a wellbore thermal energy equation coupling the reservoir temperature equation. It is shown that unlike the “sandface” temperature measurements made close to the producing zone, the temperature measurements made at locations significantly above the producing horizon are dependent upon the geothermal gradient and radial heat losses from the wellbore fluid to the formation on the way to gauge and hence more difficult to interpret for well productivity evaluation and reservoir characterization. The solutions can be used as forward models for estimating the parameters of interest by nonlinear regression built on a gradient-based maximum likelihood estimation (MLE) method. A methodology, based on straight line analyses of flow regimes (as derived from analytical solutions) identified on log-log diagnostic plots of sandface and wellbore temperature-derivative data, is proposed to obtain good initial guesses of parameters which derive the MLE objective function to have reliable optimized estimates.
PLANETARY LOW-FREQUENCY RADIO ASTRONOMY WITH LARGE GROUND-BASED INSTRUMENTS

Existing (UTR-2, GMRT, VLA, Nançay Decameter Array) and emerging (LOFAR) large low-frequency radio instruments start providing unprecedented capabilities in terms of sensitivity, angular resolution, polarization measurements, or all together, opening new perspectives for planetary studies, among which the detection and study of solar system planetary lightning, the fast radio imaging of Jupiter's magnetosphere, including synchrotron emission from radiation belts as well as decameter emission from high magnetic latitudes, and the search for radio emission from the plasma environment of exoplanets. Science objectives, recent results and ongoing programs are summarized here. RFI mitigation techniques now allow to work to some extent in the heavily polluted low-frequency radio environment of the Earth. Ionospheric propagation effects bring strong perturbations to both the amplitude and phase of received signals, especially near solar maximum. Feasibility of VLBI observations below 40 MHz is discussed.
Global reading and writing methodology: analysis of a software implementation on first-graders

The purpose of the following article is to show the results after designing and applying a literacy software, which was based on the reading and writing Global Method. Among the most challenging issues to work on, it was visible some reading and writing processes of teaching centered on pronunciation and reproduction of the written code; students’ lack of interest on literacy activities during classes and a low parents/carers’ monitoring of students’ learning process on these two abilities.
J. Gen. Appl. Microbiol

Cellulose is the most abundant carbohydrate on Earth, with up to 1.55 ¥ 109 t of cellulose produced globally each year via photosynthesis, of which 89% has not been utilized by humans (Lynd et al., 2002). Lignocellulose is formed from cellulose, hemicellulose, and lignin via various chemical bonds. It has a complex molecular structure, which is hard to break down by any single hydrolytic enzyme, and often a synergistic effect of multiple enzymes is required to complete the degradation of lignocellulose (Jeffries et al., 2007). Cellulase is widely applied in animal feeds, foods, textiles, papermaking, environmental protection, medicine, and other fields (Cha et al., 2007; Christner et al., 1992; Li et al., 2009). Screening for highly efficient, acidophilic, alkali-resistant, and thermophilic lignocellulolytic enzymes has been an important subject of recent studies. Gayal (Bos frontalis) is a semi-wild cattle breed that lives in mountainous areas at altitudes of 1,500–3,800 m. The animals graze throughout the year and feed on bamboos, reeds, and weeds. Long-term living in the harsh environment has equipped gayals with extremely high resistance characteristics, including resistance to coarse feeds and alpine environments. Studies have found that gayal has a significantly greater digestive ability than the local cattle (Bos taurus) for dry matters, organic matters, neutral detergent fiber, acidic detergent fiber, and acidic lignin of bamboo (Deng et al., 2007). Besides, gayal has a significantly higher stress resistance to harsh environments and a higher growth rate than the local cattle (Xi et al., 2007), which may be associated with its special germplasm resource of ruminal microorganisms. The study by Leng et Gene cloning and expression of fungal lignocellulolytic enzymes from the rumen of gayal (Bos frontalis)
Edinburgh Research Explorer Praseodymium polyhydrides synthesized at high temperatures and pressures

Rare earth element polyhydrides have been predicted to exhibit high-T c superconductivity at extreme compressions. Through a series of in-situ high-pressure high-temperature x-ray powder diﬀraction experiments combined with density functional theory (DFT) calculations, we report the emergence of polyhydride species in the praseodymium-hydrogen system. We initially observe the formation of PrH 3 , which continuously increases in hydrogen content on compression towards PrH 4 . Laser heating PrH 4 in a hydrogen medium at pressures of 85 GPa leads to the synthesis of both PrH 9 and PrH 7 . Both structures are characterized by hexagonal arrays of praseodymium atoms surrounded by hydrogen clathrate cages.