Miracle-dz/UniFAR-FTUs huggingface dataset:
Ajuste en luminancia y espacios de color para generar contrastes visibles para personas con daltonismo tricromático anómalo

Color is something intrinsic to human vision, contrary to what one might think it is not absolute but relative to the person and the environment where it is observed. Perception is given by 2 kind of cells present in the eyes: cones and rods, these cells absorb light wavelengths that are transformed into primary colors (red, green and blue); therefore all the color gamut. Deficiencies on these cells may cause colors to be observed differently by different individuals. From the complete lack of a primary color to the confusion of some color tones with similar luminance. This deficiency is known as color blindness and just in Mexico it affects more than 10 million people. Focus for this work is at anomalous trichromacy; which is described as partial fails over the channels but not the complete lack of one of them. A key variable for this thesis is luminance and how it may be used to identify zones that are not differentiable for an individual with this color deficiency. Luminance is an intrinsic color property that could be understood as the light intensity a color holds. Using opponent colors theory by Hering, where a color is less its counterpart as long as it moves towards the opposite direction in a color graph, it is shown how through the use of luminance it is possible to identify zones that were previously not perceptible for anomalous trichromats and therefore be modified via color spaces transformations, thresholds and matrix increments over those identified zones. The proposed algorithm is fairly quick compared with other proposals (Jia-Bin at 2009) and it presents an average improvement of 58% over images with well-defined problematic areas like the Ishihara plates, however, it still presents some flaws at color saturation at images loaded with many and not well defined problematic areas. This algorithm represents a fair baseline for future works and it opens the door to be combined with other technics like border detection and the set of local limits for saturated tones in order to obtain better results.
Amazonia Phytotherapy Reduces Ischemia and Reperfusion Injury in the Kidneys

Acute kidney injury (AKI) is defined as a sudden decrease in kidney function. Phytomedicines have shown positive effects in the treatment of AKI worldwide. The aim of this study was to evaluate the effect of Abuta grandifolia on the renal function of rats submitted to AKI. A phytochemical study of the plant was performed through liquid chromatography coupled with mass spectrometry (CL-EM) and DPPH and ABTS antioxidant tests. Renal function tests were performed in 20 male adult Wistar rats weighing from 250 to 300 g distributed in the following groups: SHAM (submitted to laparotomy with simulation of renal ischemia); ABUTA (animals that received 400 mg/kg of AG, orally—VO, once a day, for 5 days, with simulation of renal ischemia); I/N (animals submitted to laparotomy for clamping of bilateral renal pedicles for 30 min, followed by reperfusion); ABUTA + I/R (animals that received AG—400 mg/kg, 1× per day, VO, for 5 days, submitted to renal ischemia after treatment with herbal medicine). The results suggest that the consumption of Abuta grandifolia promoted renoprotection, preventing the reduction of renal function induced by ischemia, oxidizing activity, and deleterious effects on the renal tissue, confirmed by the decrease of oxidative metabolites and increase of antioxidants in the animals’ organisms.
Properties of the spectral response cost functional employed in the inverse wave-scattering problem of the retrieval of the shear body wavespeed of the homogeneous solid occupying a half space bounded by a plane stress-free surface

Parseval's theorem leads to the finding that the minima of a least-squares spectral response cost functional K are at the same positions as the minima of a least squares signal response functional C. We describe the useful functional properties of K, in the context of a simple geophysical inverse problem pertaining to the retrieval of the shear wavespeed in a homogeneous underground, notably for enabling the location of its global minimum and dealing with the secondary minima. We show how the width of the search interval, the number and positions of the sensors, as well as the central frequency and bandwidth of the spectrum of the probe radiation, condition the aspect of the cost functional, particularly as regards the number of secondary minima and the depth of the trough associated with the global minimum. Finally, we evaluate the influence of prior uncertainties on the accuracy of the retrieval (via K) of the shear body wavespeed of the underground.
Iterative Learning Control with Forgetting Factor for Urban Road Network

In order to improve the traffic condition, a novel iterative learning control (ILC) algorithm with forgetting factor for urban road network is proposed by using the repeat characteristics of traffic flow in this paper. Rigorous analysis shows that the proposed ILC algorithm can guarantee the asymptotic convergence. Through iterative learning control of the traffic signals, the number of vehicles on each road in the network can gradually approach the desired level, thereby preventing oversaturation and traffic congestion. The introduced forgetting factor can effectively adjust the control input according to the states of the system and filter along the direction of the iteration. The results show that the forgetting factor has an important effect on the robustness of the system. The theoretical analysis and experimental simulations are given to verify the validity of the proposed method.
Temporally resolved measurements of heavy, rigid fibre translation and rotation in nearly homogeneous isotropic turbulence

A two orthogonal view, holographic cinematography system (volume of $17\times 17\times 17~\text{mm}^{3}$ ) was used to measure three-dimensional fibre translational velocities, orientations and rotation rates in near homogeneous isotropic air turbulence (HIT). Flow characteristics were determined from temporally resolved particle image velocimetry measurements. Two sets of rigid, nylon fibres having the same nominal length (0.5 mm) but different diameters (13.7 and $19.1~\unicode[STIX]{x03BC}\text{m}$ ), were released in near HIT at a Taylor microscale Reynolds number of $Re_{\unicode[STIX]{x1D706}}\approx 130$ and tracked at more than five times the Kolmogorov frequency. The ratio of fibre length to the Kolmogorov length scale was 2.8 and the two sets were characterized by Stokes numbers of 1.35 and 2.44, respectively. As a result of increased inertia, the probability density functions (PDFs) of the fluctuating fibre translational velocities were narrower than the ones of the air and the fibre velocity autocorrelations decreased at a decreasing rate. While fibre orientations in the cameras’ frame of reference were random as a result of the strong turbulence, it was shown that fibres align with the flow to minimize drag. PDFs of the fibre rotation rates indicated the occurrence of extreme rotation rate events. Furthermore, increasing inertia lowered the normalized, mean squared fibre rotation rates in comparison to results obtained for neutrally buoyant fibres having the same aspect ratio and including the effect of preferential alignment. The present results compare well to direct numerical simulations including the effect of fibre inertia.
Ethnopharmacological Profile of Curcuma aromatica Salisb

This review consists of summary of one of the important plants Curcuma aromatica Salisb. which has a very great importance since past times. Since from decades its extracts from its parts have a great use for healing wound property also used as a flavouring agent, preservative, colouring agent too. Turmeric has a long history of therapeutic usage in South Asia. This literature has been collected from different online sources like PubMed, Google Scholar, Research Gate etc. The abstract of knowledge has also been gained from the local people who have been using this wild herb because of its importance in the field of nutrition, medicinal and also added with the flavouring agent quality. There are many bioactive compounds that are responsible for various biological properties like anti-diabetic, anti-bacterial, anti-inflammatory, wound healing, anti-melanogenic, antitumour, anti-oxidant, anti-cancer, anti–platelet, anti-nephrotoxic, anticancer and anti-repellent activity to treat various kinds of diseases. This review contains a systematic and traditional knowledge collected on the areas like habitat, area of distribution and its pharmacological importance and its morphological characters, phytochemistry and also the ethnomedicinal uses from the various regions of Himachal Pradesh.
Les surfaces gagnent du volume

Ameliorer l'apparence des personnages virtuels modernes des jeux video et du cinema passe aujourd'hui par le mariage entre un theoreme d'analyse vectoriel classique, la geometrie et l'informatique. Nous proposons dans cet article d'expliquer en quoi l'application du theoreme de Green-Ostrogradsky dans le domaine de l'image de synthese permet de garantir que les personnages virtuels maintiennent leur volume constant lorsqu'ils sont animes.
Improvement of Objective Image Quality Evaluation Applying Colour Differences in the CIELAB Colour Space

Objective full-reference image quality algorithms are often designed considering the luminance component of images. In this work perceived colour distance is employed in a simple and functional way in order to improve these full-reference image quality algorithms. The difference between colours in the CIELAB colour space is employed as perceived colour distance. This quantity is used to process images that are to be feed to full-reference image quality algorithms. This image processing stage consists of identifying the image regions or pixels that are expected to be perceived identically by a human observer in both the reference image and the image having its quality evaluated. In order to verify the validity of the proposal, objective scores are compared with subjective ones for public available image databases. Despite being a very simple strategy, the proposed approach was effective to improve the agreement between subjective and the SSIM (Structural Similarity Index Metric) objective score.
CONTENT BASED IMAGE RETRIEVAL USING SVM ALGORITHM AND CONTOURLET TRANSFORM COEFFICIENTS DISTRIBUTION.

Conventional content-based image retrieval schemes may suffer from practical applications. Image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors the interactive retrieval of such image classes is then very difficult. To address this challenge, we propose the support vector machine (SVM) algorithm with contourlet transform coefficients distribution. SVM is used to find out the optimal result and to evaluate the generalization ability under the limited training samples. It gives faster result as compared to other. An SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Features of the face image are extracted in the spectrum domain using contourlet Transform and this transform addresses the problem of representing the images with smooth contours in different directions by providing two additional properties which are directionality and anisotropy. This method will overcome the introduction of the
Combining heuristic and exact approaches for solving the routing and spectrum assignment problem

Efficient algorithms for solving the routing and spectrum assignment (RSA) problem in flex-grid optical networks are of paramount importance to leverage network efficiency. Exact approaches based on integer linear programming (ILP) models are known to be computationally hard and hence, difficult to solve for realistic models while heuristics have inherent downsize of local optimal trapping. In addressing those issues, this paper firstly presents a novel and efficient heuristic based on the genetic algorithm (GA) to provide (near-) optimal solutions to the RSA problem. The effectiveness of the GA-based algorithm is benchmarked against typically used reference heuristics for a set of network scenarios and it is shown that in addition to the better performance than conventional heuristics in the literature, the proposed GA technique indeed could achieve optimal solutions in most cases while the running time is still manageable. Secondly, we propose to make use of efficient solutions from the heuristic approaches to set the upper-bound and warm-start point for ILP formulation. This combination has been shown very productive in enhancing running time performance and increasing the capability of achieving an optimal solution and/or improving the solution quality in a given time span compared with traditional ways for solving ILP models.
Modelos para séries temporais utilizando as distribuições normal generalizada e log-normal generalizada

From the generalized normal distribution and concepts of the generalized autoregressive moving averages models we introduce the generalized normal-ARMA model as an alternative way to model time series exhibiting symmetry and tails that may be lighter or heavier when compared the normal distribution. We present application for proposed model using three time series in the hydrology, economy and publics policy areas. The proposed model is presented as good alternative when compared to ARMA model with normal distribution. We extended this model the case of the asymmetric time series. In this case we used the Box-Cox transformation, denoted by Box-Cox generalized normal ARMA. The particular case, when we use the logarithmic transformation is called generalized log-normal ARMA. We adjusted the models with transformation to the series on monthly average affluent streamflow of the Furnas and Sobradinho hydroelectric plants. We obtain the prediction values for the model with transformation, that are better when compared with the model without transformation. To treat time series that exhibit periodic in the correlation function we defined three extensions for periodic autoregressive model, called generalized normal periodic autoregressive model, generalized log-normal periodic autoregressive model and Box-Cox generalized normal periodic autoregressive model. We can observed that the series on monthly average affluent streamflow of the Furnas and Sobradinho hydroelectric plants have periodic correlation. We present two applications of periodic models from these series. In the models, we note that is not necessary the use of generalized normal distribution in every months, just in some the generalized normal distribution presented better results than the normal distribution. Finally, we define the generalized normal zero inflated distribution and the generalized normal zero inflated ARMA model for time series. Adopting the model for series that have zero inflation and the maximum likelihood method for estimation of parameters, we
The impact of Covid-19 on Nigerian food systems

The Nigerian food system is facing major challenges with high population growth, a high number of people living in extreme poverty, rapid urbanization, and stagnating agricultural productivity. Socioeconomic status is a key indicator of the Nigerian food system. This paper aims to examine the impact of COVID-19 on Nigerian food systems. These reviews were obtained from white papers written by organizations like the United Nations (UN), and other studies on food systems, agriculture, and the socioeconomic status of Nigerians concerning the COVID-19 pandemic were reviewed. In recent years, the world has recorded several incidences of disasters that have disrupted the food system, leading to evidence of food insecurity. The most recent is the outbreak of COVID-19, which is more than just a disease. It resulted in the combined disruption of global economic and social stability. The food system and all that is dependent on it were severely affected by COVID-19. The global situation of poverty and malnutrition was exacerbated as a result of the direct and indirect impact of the COVID-19 pandemic. In a phone interview, it was recorded that 37% of Nigerian households experienced a drastic drop in income, pushing many to the brink or into the threshold of poverty. Many of the Nigerians interviewed agreed that the quantity and quality of their diet had diminished in comparison to the same period in the previous year. Another survey in Nigeria reported that 23.8% of its respondents reduced the portion of their meals, 20.8% reduced their frequency from 3 to 2 meals per day, and 13.8% substituted highly nutritious foods with less nutritious and cheaper ones to cope with hunger during the Pandemic. To cushion the impact of COVID-19 on Nigeria's food systems, there is a need to reinforce the existing incentives for restructuring Nigeria's economy away from reliance on oil. Further, remittances from other countries would also contribute towards the development of local sectors such as agriculture and food manufacturing companies, which can generate employment and feed Nigeria’s growing population. Also, the development of the agro-food system will necessitate investments in research and rural infrastructure. In this way, federal and state policies can aid recovery from COVID-19 and help Nigeria build more resilient food systems. Key words: COVID-19, Pandemic, Nigeria, Food systems, Agriculture, Food security, Food safety, Socio-economic impact
Impact of the gut-lung axis on tuberculosis susceptibility and progression

Tuberculosis (TB) has remained at the forefront of the global infectious disease burden for centuries. Concerted global efforts to eliminate TB have been hindered by the complexity of Mycobacterium tuberculosis (Mtb), the emergence of antibiotic resistant Mtb strains and the recent impact of the ongoing pandemic of coronavirus disease 2019 (COVID19). Examination of the immunomodulatory role of gastrointestinal microbiota presents a new direction for TB research. The gut microbiome is well-established as a critical modulator of early immune development and inflammatory responses in humans. Recent studies in animal models have further substantiated the existence of the ‘gut-lung axis’, where distal gastrointestinal commensals modulate lung immune function. This gut microbiome-lung immune crosstalk is postulated to have an important correlation with the pathophysiology of TB. Further evaluation of this gut immunomodulation in TB may provide a novel avenue for the exploration of therapeutic targets. This mini-review assesses the proposed mechanisms by which the gut-lung axis impacts TB susceptibility and progression. It also examines the impact of current anti-TB therapy on the gut microbiome and the effects of gut dysbiosis on treatment outcomes. Finally, it investigates new therapeutic targets, particularly the use of probiotics in treatment of antibiotic resistant TB and informs future developments in the field.
Feedback Control of Nonlinear Hyperbolic PDE Systems Inspired by Traffic Flow Models

This paper investigates and provides results, including feedback control, for a nonlinear, hyperbolic, one-dimensional partial differential equation (PDE) system on a bounded domain. The considered model consists of two first-order PDEs with a dynamic boundary condition on the one end and actuation on the other. It is shown that, for all positive initial conditions, the system admits a globally defined, unique, classical solution that remains positive and bounded for all times; these properties are important, for example for traffic flow models. Moreover, it is shown that global stabilization can be achieved for arbitrary equilibria by means of an explicit boundary feedback law. The stabilizing feedback law depends only on collocated boundary measurements. The efficiency of the proposed boundary feedback law is demonstrated by means of a numerical example of traffic density regulation.
Exploring the Synergistic Impacts of Cover Crops and Fertilization on Soil Microbial Metabolic Diversity in Dryland Soybean Production Systems Using Biolog EcoPlates

The metabolic diversity of soil microbiota embodies diverse functional capabilities that support ecosystem resilience, driving essential biogeochemical processes and facilitating the optimization of sustainable agricultural systems. Integrating cover crops into agricultural systems cultivates a diverse array of metabolic activities among soil microbes, synergistically enhancing ecosystem services and bolstering soil health for sustainable and productive farming practices. In an effort to gain deeper insights and expand our knowledge, we conducted a study examining the effects of cover crops and fertilizer sources, thereby shedding light on their combined impacts on the metabolic activity dynamics of soil microbial communities. In this investigation, we employed a split-plot design with two factors: (a) cover crop with three solo cover crop species—Cereal rye (Secale cereale), wheat (Triticum aestivum), hairy vetch (Vicia villosa), and one mixture of mustard (Brassica rapa) and cereal rye (Secale cereale) (CC-mix), (b) Fertilizer source includes poultry litter, chemical fertilizer, and no-fertilizer treatments. We assessed the metabolic potential of soil microbiota by using carbon substrates utilizing Biolog EcoPlates. The findings revealed that the plots with CC-mix treatment exhibited greater metabolic diversity compared to the other treatments, while among the fertilizer sources, poultry litter demonstrated higher metabolic activity. Furthermore, both treatment factors predominantly metabolized carbohydrates and polymers compared to other carbon substrate categories. The principal component analysis accounted for 46.4% of the variance, collectively represented by PC1 and PC2, emphasizing the substantial contributions of carbohydrates, amino acids, and carboxylic acids to the observed metabolic diversity. Canonical correspondence analysis revealed that pH had positively correlated with microbial functional diversity, whereas total carbon (TC), total nitrogen (TN), and water-stable aggregates (WSA) showed a negative correlation. In conclusion, cover cropping and type of fertilizer source had a notable impact on soil microbial functional diversity, with the cover crop mixture exhibiting a more pronounced influence than the individual cover crop treatments.
Capturing a News Frame – Comparing Machine-Learning Approaches to Frame Analysis with Different Degrees of Supervision

ABSTRACT The empirical identification of frames drawing on automated text analysis has been discussed intensely with regard to the validity of measurements. Adding to an evolving discussion on automated frame identification, we systematically contrast different machine-learning approaches with a manually coded gold standard to shed light on the implications of using one or the other: (1) topic modeling, (2) keyword-assisted topic modeling (keyATM), and (3) supervised machine learning as three popular and/or promising approaches. Manual coding is based on the Policy Frames codebook, providing an established base that allows future research to dovetail our contribution. Analysing a large dataset of 12 Austrian newspapers’ EU coverage over 11 years (2009–2019), we contribute to addressing the methodological challenges that have emerged for social scientists interested in employing automated tools for frame analysis. While results confirm the superiority of supervised machine-learning, the semi-supervised approach (keyATM) seems unfit for frame analysis, whereas the topic model covers the middle ground. Results are extensively discussed regarding their implications for the validity of approaches.