Miracle-dz/UniFAR-FTUs huggingface dataset:
Análise de metacaracterísticas para classificação de uso e cobertura do solo utilizando Random Forest

This work analyzes the impact of the use of meta-features generated by the TWDTW algorithm for mapping land use and cover using Random Forest. The tests were carried out by classifying nine classes, for a set of samples from Mato Grosso region, in Brazil. Our results show that the meta-features are promising for the improvement of accuracy, increasing the overall accuracy of the tested models. The most significant improvements occur in the producer accuracy of the classes more difficult to classify. The importance of meta-features in the classification was significantly greater than features extracted from the EVI and NDVI indices and NIR and MIR bands. Resumo. Este trabalho analisa o impacto do uso de metacaracterı́sticas geradas pelo algoritmo TWDTW para mapeamento do uso e cobertura do solo utilizando Random Forest. Os testes foram realizados classificando nove classes, para um conjunto de amostras da região do Mato Grosso, no Brasil. Nossos resultados mostram que as metacaracterı́sticas são promissoras para a melhora de acurácia, aumentando a acurácia global dos modelos testados. As melhoras mais significativas ocorrem na acurácia do produtor das classes de maior dificuldade de classificação. A importância das metacaracterı́sticas na classificação foi significativamente maior do que as caracterı́sticas extraı́das dos Índices EVI e NDVI e Bandas NIR e MIR.
The Perceptions of Chinese ELT Stakeholders on the Employment of Foreign English Language Teachers

 This article reports on part of the findings of an investigation into the perceptions of Chinese English as a foreign language (EFL) education stakeholders on native-speakerism. Data were collected via semi-structured interviews with 26 students, 14 teachers and eight EFL program administrators from six Chinese universities. The analysis of data reveals that the three groups of participants as an entirety expected their universities to hire native English speaker teachers (NESTs), particularly those from Britain and/or America and a certain proportion of them expressed preference for white native English speaker (NES) teachers. The vast majority of the students and teachers were also found to be unaware of workplace inequalities between NNESTs and local Chinese EFL teachers, contending that NESTs deserve better treatment. Although many administrators displayed critical awareness on this issue, they argued that the inequalities need tolerating for the well-being of their EFL programs. All these findings are indicative of the persistence of native-speakerism among Chinese ELT stakeholders and by extension the tenacity of this chauvinistic ideology across the terrain of EFL education in
Antiallergic properties of Tinospora cordifolia in animal models

1. The anti-allergic properties of an aqueous extract of the stem of Tinospora Cordifolia was evaluated on histamine induced bronchospasm in guineapigs, capillary permeability in mice and mast cell disruption in rats. 2. The extract significantly decreased the bronchospasm induced by 5% histamine aerosol, decreased capillary permeability and reduced the number of disrupted mast cells in the above experimental animals respectively.
A General Statistic to Test an Optimally Weighted Combination of Common and/or Rare Variants

Both genome-wide association study and next generation sequencing data analyses are widely employed in order to identify disease susceptible common and/or rare genetic variants in many large scale genetic studies. Rare variants generally have large effects though they are hard to detect due to their low frequency. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some ad hoc assumptions (e.g. ignoring dependence between rare variants). In this study, we analytically derive optimal weights for both common and rare variants and propose a General and novel approach to Test association between an Optimally Weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. We conduct extensive simulation studies to evaluate the performance of G-TOW. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared, when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. In addition, we applied G-TOW and other competitive methods to test association for schizophrenia. The G-TOW have successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic (WSS) and the sequence kernel association test (SKAT). G-TOW also showed much stronger significance (p-value=0.0037) than our previously developed method named Testing the effect of an Optimally Weighted combination of variants (TOW) (p-value=0.0143) on gene FYN. FYN is a member of the protein-tyrosine kinase oncogene family that phosphorylates glutamate metabotropic receptors and ionotropic N-methyl-d-aspartate (NMDA) receptors. NMDA modulates trafficking, subcellular distribution and function. It is involved in neuronal apoptosis, brain development and synaptic transmission and lower expression, which has been observed in the platelets of schizophrenic patients compared with controls. The application for schizophrenia indicates that G-TOW is a powerful tool in genome-wide association studies.
Trait-Unconsciousness, State-Unconsciousness, Preconsciousness, and Social Miscalibration in the Context of Implicit Evaluation

Implicit evaluations are often assumed to reflect “unconscious attitudes. ” We review data from our lab to conclude that the truth of this statement depends on how one defines “unconscious. ” A trait definition of unconscious according to which implicit evaluations reflect cognitions that are introspectively inaccessible at all times appears to be inaccurate. However, when unconscious is defined as a state which cognitions can be in at specific times, some data suggest that the cognitions reflected on implicit evaluations may sometimes unfold without direct awareness, in that people seem to rarely pay attention to them. Additionally, people appear to be miscalibrated in their reports in that they construe even conscious biases in self-serving ways. This analysis suggests that implicit evaluations do not reflect unconscious cognitions per se, but rather awareness-independent cognitions that are often preconscious and miscalibrated. Discussion centers on the meaning of this analysis for theory and application.
Comparative and Combined Effect of Turmeric and Ginger Supplementation on Growth, Carcass Characteristics, Blood Parameters and Economics of Productions in Broiler Birds

Day-old broiler chicks (n=182) were distributed randomly in seven dietary treatments. The treatments included dietary supplementation of turmeric powder (TP) and ginger powder (GP) alone or in combination, each at 0.5 and 1.0 percent levels, i.e., T1 (control), basal diet alone; T2, 0.5% TP; T3, 1.0% TP; T4, 0.5% GP; T5, 1.0% GP; T6, both TP and GP each at 0.5%; T7, both TP and GP each at 1% levels. The experiment was continued for 35 d. At 35d, significantly (P 0.05) evident among groups T1, T4, T5 and T7. No significant difference was found in feed consumption among treatment groups. The FCR was significantly (P 0.05) among birds of all dietary treatments. The margin of profit/kg LW was recorded higher in T3 and lowest in T7 group. It is concluded that supplementation of 1% turmeric powder in broiler ration improved the BW and feed efficiency, and lowered total cholesterol and triglyceride levels accompanying abetter economics of production.
A combined hybrid mixed element method for incompressible miscible displacement problem with local discontinuous Galerkin procedure

In this article, we propose a combined hybrid discontinuous mixed finite element method for miscible displacement problem with local discontinuous Galerkin method. Here, to obtain more accurate approximation and deal with the discontinuous case, we use the hybrid mixed element method to approximate the pressure and velocity, and use the local discontinuous Galerkin finite element method for the concentration. Compared with other combined methods, this method can improve the efficiency of computation, deal with the discontinuous problem well and keep local mass balance. We study the convergence of this method and give the corresponding optimal error estimates in L∞(L2) for velocity and concentration and the super convergence in L∞(H1) for pressure. Finally, we also present some numerical examples to confirm our theoretical analysis.
Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes

Floods often affect large regions and cause adverse societal impacts. Regional flood hazard and risk assessments therefore require a realistic representation of spatial flood dependencies to avoid the overestimation or underestimation of risk. However, it is not yet well understood how spatial flood dependence, that is, the degree of co‐occurrence of floods at different locations, varies in space and time and which processes influence the strength of this dependence. We identify regions in the United States with seasonally similar flood behavior and analyze processes governing spatial dependence. We find that spatial flood dependence varies regionally and seasonally and is generally strongest in winter and spring and weakest in summer and fall. Moreover, we find that land‐surface processes are crucial in shaping the spatiotemporal characteristics of flood events. We conclude that the regional and seasonal variations in spatial flood dependencies must be considered when conducting current and future flood risk assessments.
Norway Spruce Survival Rate in Two Forested Landscapes, 1975–2016

The increasing frequency and severity of natural disturbances (e.g., storms and insect outbreaks) due to climate change are expected to reduce the abundance of Norway spruce stands in the European forests. Under such conditions, the assessment of status quo on focusing on survival of Norway spruce stands are essential for the agility of forest management strategies. The dynamics (mortality rate) of Norway spruce stands in hemiboreal forests based on forest inventories for the period from 1975 to 2016 (inventories of 1975, 1985, 1999, 2011 and 2016) were analyzed in two forest landscapes in the western and eastern parts of Latvia (Vane and Dviete, respectively). The spatiotemporal changes in age-dependent mortality differing by abundance of Norway spruce and disturbance regime were assessed, focusing on the transitions of stands between age groups (inventories). The age-related changes in probability of stands transitioning into the next age group contrasted (p < 0.001) between sites. In Vane, the survival of stands between inventories was constant (ca. 90%), while in Dviete, it decreased sharply from 85.7% during 1985–1999 inventories to 49.3% in 2011–2016. Age-related decreases in stand survival showed local dependencies between both landscapes, namely, in Vane, notable decreases started from 61 years, while in Dviete, the downward trends started already from 31 years, probably due to different disturbance regimes. This suggests that, in forest management planning, the different outcomes for mortality patterns between both landscapes must be considered and should not be generalized for a whole country.
Realized Measures to Explain Volatility Changes over Time

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.
Molecular Identification of Virulence Genes of Staphylococcus Aureus Isolated from Milk Products

Food intoxication by S.aureus was listed as the third etiology of food intoxication all over the world. This study was conducted to investigate the prevalence rate of S.aureus in skim milk soft cheese( Kariesh cheese) and ice cream samples from local markets in Menoufia province, Egypt. Two-hundred samples (100 for each) were examined bacteriologically for prevalence of Staphylococcus aureus through culturing on specific media and biochemical identification. In addition to the molecular characterization of some virulence and antibiotic resistance genes in isolated strains. The obtained results revealed that the prevalence rate of S. aureus was 24% in kariesh cheese with an average count of 1.22x 103 1.39 x 102 CFU/g and 16% in ice-cream samples with an average count of 1.04 x103 ± 8.89x10 CFU/ml . Furthermore, the molecular characterization using specific primers sets for detection of Coa, Spa and blaZ genes of S. aureus was performed by amplification of these primers at single amplicon at 630 bp, 226 bp, and 173bp respectively. The molecular results confirmed the detection of these genes in all tested isolates of S.aureus. The obtained results proved that PCR technique is more sensitive and specific in detection of these genes than other conventional techniques and help to understanding the role of these genes in pathogenicity pathway in different disease conditions and construction of effective control measure.
Institutional Design and Elite Support for Climate Policies: Evidence from Latin American Countries

Abstract Which institutional features do Latin American elites favor for local climate change policies? Climate change mitigation requires active local-level implementation, but it remains unclear which institutional arrangements maximize support for environmental rules. In this paper, we run a conjoint experiment with elite members of 10 Latin American countries and ask respondents to evaluate institutional designs drawn from a pool of 5,500 possible local climate governance arrangements. We find that Latin American elites prefer international organizations to formulate climate policies, support imposing increasing fines on violators, and favor renewing agreements every 5 years. We also find that elites support both international institutions and local courts to mediate conflicts, but they distrust non-governmental organizations and reject informal norms as a means of conflict resolution. Our results identify possible challenges in crafting local climate mitigation policies and offer new insights about how to integrate local and international levels in environmental agreements.
Interference Mitigation using Adaptive Beamforming with RLS Algorithm for Coexistence between 5G and Fixed Satellite Services in C-Band

Smart antenna system is one of the most suitable techniques making the sharing between incumbent Fixed Satellite Services (FSS) and the Fifth Telecommunication Generation (5G) possible, because adaptive beamforming can make null the beam towards the FSS earth station. This paper proposes the use of Recursive Least Square (RLS) algorithm for adaptive beamforming in 5G Base Station (BS) having a null beam toward FSS earth station. We perform a computer simulation to evaluate the proposed RLS algorithm and compare the result in terms of separation distance between FSS earth station and 5G BS with and without RLS algorithm. The results confirmed that the RLS algorithm for adaptive beamforming makes the coexistence between 5G and FSS possible indicated by the significant reduction of separation distance between FSS earth station and 5G BS leading to the minimum interference of 5G BS to the FSS earth station.
Complementary Roles for Ventral Pallidum Cell Types and Their Projections in Relapse

The ventral pallidum (VP) is a key node in the neural circuits controlling relapse to drug seeking. How this role relates to different VP cell types and their projections is poorly understood. Using male rats, we show how different forms of relapse to alcohol-seeking are assembled from VP cell types and their projections to lateral hypothalamus (LH) and ventral tegmental area (VTA). Using RNAScope in situ hybridization to characterize activity of different VP cell types during relapse to alcohol-seeking provoked by renewal (context-induced reinstatement), we found that VP Gad1 and parvalbumin (PV), but not vGlut2, neurons show relapse-associated changes in c-Fos expression. Next, we used retrograde tracing, chemogenetic, and electrophysiological approaches to study the roles of VPGad1 and VPPV neurons in relapse. We show that VPGad1 neurons contribute to contextual control over relapse (renewal), but not to relapse during reacquisition, via projections to LH, where they converge with ventral striatal inputs onto LHGad1 neurons. This convergence of striatopallidal inputs at the level of individual LHGad1 neurons may be critical to balancing propensity for relapse versus abstinence. In contrast, VPPV neurons contribute to relapse during both renewal and reacquisition via projections to VTA. These findings identify a double dissociation in the roles for different VP cell types and their projections in relapse. VPGad1 neurons control relapse during renewal via projections to LH. VPPV neurons control relapse during both renewal and reacquisition via projections to VTA. Targeting these different pathways may provide tailored interventions for different forms of relapse. SIGNIFICANCE STATEMENT Relapse to drug or reward seeking after a period of extinction or abstinence remains a key impediment to successful treatment. The ventral pallidum, located in the ventral basal ganglia, has long been recognized as an obligatory node in a 'final common pathway' for relapse. Yet how this role relates to the considerable VP cellular and circuit heterogeneity is not well understood. We studied the cellular and circuit architecture for VP in relapse control. We show that different forms of relapse have complementary VP cellular and circuit architectures, raising the possibility that targeting these different neural architectures may provide tailored interventions for different forms of relapse.
Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlooking this fact generally leads to weak classifiers for predicting the minority class (crashes with higher severity). In this paper, in order to handle imbalanced accident datasets and provide a better prediction for the minority class, the random undersampling the majority class (RUMC) technique is used. By employing an imbalanced and a RUMC-based balanced training set, we propose the calibration, validation, and evaluation of four different crash severity predictive models, including random tree, k-nearest neighbor, logistic regression, and random forest. Accuracy, true positive rate (recall), false positive rate, true negative rate, precision, F1-score, and the confusion matrix have been calculated to assess the performance. Outcomes show that RUMC-based models provide an enhancement in the reliability of the classifiers for detecting fatal crashes and those causing injury. Indeed, in imbalanced models, the true positive rate for predicting fatal crashes and those causing injury spans from 0% (logistic regression) to 18.3% (k-nearest neighbor), while for the RUMC-based models, it spans from 52.5% (RUMC-based logistic regression) to 57.2% (RUMC-based k-nearest neighbor). Organizations and decision-makers could make use of RUMC and machine learning algorithms in predicting the severity of a crash occurrence, managing the present, and planning the future of their works.
NUTRIENT CONTENT OF SOYBEAN MEAL FROM DIFFERENT ORIGINS BASED ON NEAR INFRARED REFLECTANCE SPECTROSCOPY

Near infrared reflectance spectroscopy (NIRS) has become common techniques to estimate chemical composition of feed ingredient for poultry. Two experiments were performed: first was to compare the capability of NIRS system from three laboratories (E, A and T) to measure nutrient composition of soybean meal (SBM); and the second was to evaluate nutrient composition and quality of 59 samples of SBM from Argentine, Brazil and US using NIRS from T-laboratory. Thirty samples of SBM was used in the first study and the result showed that all NIRS systems were able to estimate proximate, amino acids, metabolizable energy (ME) and carbohydrate components. The second experiment indicated that there were some differences in proximate composition (especially protein), total amino acids and digestible amino acids among SBM from different origins. Brazilian SBM had 2% higher protein and amino acid compared to US or Argentine SBM (P<0.05). However, US SBM had slightly higher ME (20 and 40 kcal kg -1 ) compared to Brazilian and Argentine SBM, respectively. ME is positively correlated with protein (0.50) and fat content (0.58) but negatively correlated with fiber (-0.74) and NSP (-0.61). Stepwise regression analysis demonstrated that ME can be estimated using equation ME (kcal kg -1 ) = 75.7 – 21.0 x Fiber + 87.4 x Fat + 32.9 x Protein + 17.6 x NFE with reasonable accuracy (R 2 = 0.995). In conclusion NIRS can be used to estimate nutrient content of SBM. Brazilian SBM has higher protein and amino acids, but US SBM has slightly higher ME content.