Miracle-dz/UniFAR-FTUs huggingface dataset:
Theorgano-mineral fertilization on the yield of faba bean in soil and hydroponics in protected agriculture

The faba bean is a crop of great social and economic importance in the High Valleys of the Central Table of Mexico, having as a final destination the human and animal consumption. Despite its importance, grain yields are low, with inadequate fertilization being one of the reasons. The present investigation was carried out with the purpose of determining the effect of organo-mineral fertilization on the yield of faba bean, soil and substrate tezontle. The research was conducted from December 2013-April 2014, in the municipality of San Pedro Cholula, Puebla, under greenhouse conditions, using Cochinera Cultivation seed. Two factors were studied: foliar liquid biofertilization and Steiner nutrient solution concentration at 50% and 100%. The biofertilizers studied were from Taiwanese continuous flow digesters located in the municipalities of San Felipe Teotlalcingo and Huejotzingo, Puebla. The experimental design of the works was completely random and factorial treatment arrangement, with five repetitions. The results showed that, for the cultivation in soil, the treatments did not present differences in the yield of fresh fruit, but there were significant differences in yield of dry fruit where the application of liquid biofertilizer from San Felipe Teotlalcingo had the best results. While the application of a complete organo-mineral fertilization in tezontle substrate resulted in the best response in yield of fresh and dry fruit.
The Role of Destination Experience in the Sustainability of the Image of the Tourist Destination

The concept of destination image is closely related to the brand image of the destination. A good image is a step in branding the destination. The image of the destination can be a primary, secondary or global one, the latter incorporating the first two. The sustainability of a positive image of the destination is based on both a positive secondary image and a positive global image. The purpose of this research is to analyze separately the two types of images for a given tourist destination that has registered in recent years a remarkable increase in the number of visitors. The research is based on a questionnaire-based survey of a sample of 607 people. The collected data were processed with SPSS and the results show significant differences between the two types of images (secondary image and global image), a dangerous situation in the medium and long term for destination management. The nuances in the perception of the image of the destination on the two types of respondents (who experienced respectively who did not experience the destination) can be explained by the aggressive strategy of promoting the tourist destination, but inefficient strategy for younger age groups. The study allows the formulation of conclusions and measures to correct
Learning sparse conditional distribution: An efficient kernel-based approach

: This paper proposes a novel method to recover the sparse structure of the conditional distribution, which plays a crucial role in subsequent statistical analysis such as prediction, forecasting, conditional distribution estimation and others. Unlike most existing methods that often require explicit model assumption or suﬀer from computational burden, the proposed method shows great advantage by making use of some desirable properties of reproducing kernel Hilbert space (RKHS). It can be eﬃciently implemented by optimizing its dual form and is particularly attractive in dealing with large-scale dataset. The asymptotic consistencies of the proposed method are established under mild conditions. Its eﬀectiveness is also supported by a variety of simulated examples and a real-life supermarket dataset from Northern China.
Physically-aware Generative Network for 3D Shape Modeling

Shapes are often designed to satisfy structural properties and serve a particular functionality in the physical world. Unfortunately, most existing generative models focus primarily on the geometric or visual plausibility, ignoring the physical or structural constraints. To remedy this, we present a novel method aimed to endow deep generative models with physical reasoning. In particular, we introduce a loss and a learning framework that promote two key characteristics of the generated shapes: their connectivity and physical stability. The former ensures that each generated shape consists of a single connected component, while the latter promotes the stability of that shape when subjected to gravity. Our proposed physical losses are fully differentiable and we demonstrate their use in end-to-end learning. Crucially we demonstrate that such physical objectives can be achieved without sacrificing the expressive power of the model and variability of the generated results. We demonstrate through extensive comparisons with the state-of-the-art deep generative models, the utility and efficiency of our proposed approach, while avoiding the potentially costly differentiable physical simulation at training time.
Health practices of domination and exclusion: views of activists, professionals and researchers on the situation of forced sterilization of women and girls with disabilities in Spain

In 2017, the United Nations declared that 38 countries, including Spain, continued to practice forced sterilizations on women and girls with disabilities, despite it being a practice widely condemned by various international human rights organizations. This study analyzed the situation of this practice in Spain, from the perspective of activists, professionals and researchers with experiences related to this group. The methodology used was qualitative, based on Constructivist Grounded Theory. Semi-structured in-depth interviews were applied to 22 informants, representing 6 autonomous communities of the country. The participants identified a model of health practices of domination and exclusion, where the sexuality and reproduction of women with disabilities has been the object of expropriation, alienation and contempt by systems, structures and differentiated policies, with little transfer of the global framework of human rights to their reality. Sexual violence has gone unnoticed, becoming naturalized by the environment, and has promoted mechanisms of social exclusion and health inequities, depriving them of their fundamental human rights. As it is a situation that is replicated in various parts of the world, it should be considered an issue of relevance for international public health.
Supplementary: Practical Single-Image Super-Resolution Using Look-Up Table

With RAISR RAISR [9] works in two-stage that consists of a global and a local enhancement, with the latter part using a hash table. They compute image gradient and SVD for each patch of the input image for hash table keys, and this would take more computation time than our interpolation step. We note that a direct comparison is difficult with RAISR as the code is not publicly available. We can estimate the runtime of RAISR as roughly 97ms when running on our desktop computer (Intel Xeon CPU E31230 v3 @ 3.30GHz with 32GB RAM), by comparing the reported runtime of A+ in RAISR paper and our paper. In addition, an unofficial implementation of RAISR1 takes 2.6s on the same machine. With Other Deep SR Models Some methods which have not been compared with are – PAMS [7], BSRN [14], FALSR [4], ESRN [10] and TPSR [6]. They made valuable efforts to improve the efficiency of deep SR models but they still consist of a number of convolutional layers. We show some quantitative comparisons in Table 1. It is difficult to compare runtimes directly because the codes are not available (except for PAMS), however, we can roughly estimate the runtimes of each method based on the model size and the number of multiplication-addition operations. Based on the runtime of FSRCNN, we can conclude that our method is much faster and efficient than the others. However, the deep SR methods show good PSNR.
Genome-wide analysis, identification, evolution and genomic organization of dehydration responsive element-binding (DREB) gene family in Solanum tuberosum

Background The dehydration responsive element-binding (DREB) gene family plays a crucial role as transcription regulators and enhances plant tolerance to abiotic stresses. Although the DREB gene family has been identified and characterized in many plants, knowledge about it in Solanum tuberosum (Potato) is limited. Results In the present study, StDREB gene family was comprehensively analyzed using bioinformatics approaches. We identified 66 StDREB genes through genome wide screening of the Potato genome based on the AP2 domain architecture and amino acid conservation analysis (Valine at position 14th). Phylogenetic analysis divided them into six distinct subgroups (A1–A6). The categorization of StDREB genes into six subgroups was further supported by gene structure and conserved motif analysis. Potato DREB genes were found to be distributed unevenly across 12 chromosomes. Gene duplication proved that StDREB genes experienced tandem and segmental duplication events which led to the expansion of the gene family. The Ka/Ks ratios of the orthologous pairs also demonstrated the StDREB genes were under strong purification selection in the course of evolution. Interspecies synteny analysis revealed 45 and 36 StDREB genes were orthologous to Arabidopsis and Solanum lycopersicum, respectively. Moreover, subcellular localization indicated that StDREB genes were predominantly located within the nucleus and the StDREB family’s major function was DNA binding according to gene ontology (GO) annotation. Conclusions This study provides a comprehensive and systematic understanding of precise molecular mechanism and functional characterization of StDREB genes in abiotic stress responses and will lead to improvement in Solanum tuberosum.
Practical Settlement Bounds for Proof-of-Work Blockchains

Nakamoto proof-of-work ledger consensus currently underlies the majority of deployed cryptocurrencies and smart-contract blockchains. While a long and fruitful line of work has succeeded to identify its exact security region---that is, the set of parametrizations under which it possesses asymptotic security---the existing theory does not provide concrete settlement time guarantees that are tight enough to inform practice. In this work we provide a new approach for obtaining concrete and practical settlement time guarantees suitable for reasoning about deployed systems. We give an efficient method for computing explicit upper bounds on settlement time as a function of primary system parameters: honest and adversarial computational power and a bound on network delays. We implement this computational method and provide a comprehensive sample of concrete bounds for several settings of interest. We also analyze a well-known attack strategy to provide lower bounds on the settlement times. For Bitcoin, for example, our upper and lower bounds are within 90 seconds of each other for 1-hour settlement assuming 10 second network delays and a 10% adversary. In comparison, the best prior result has a gap of 2 hours in the upper and lower bounds with the same parameters.
Machine Learning Techniques and Syntactic Pattern Recognition Based Heart Disease Prediction for Smart Health

Cardiovascular disease (CVD) may sometimes unexpected loss of life. It affects the heart and blood vessels of body. CVD plays an important factor of life since it may cause death of human. It is necessary to detect early of this disease for securing patients life. In this chpter two exclusively different methods are proposed for detection of heart disease. The first one is Pattern Recognition Approach with grammatical concept and the second one is machine learning approach. In the syntactic pattern recognition approach initially ECG wave from different leads is decomposed into pattern primitive based on diagnostic criteria. These primitives are then used as terminals of the proposed grammar. Pattern primitives are then input to the grammar. The parsing table is created in a tabular form. It finally indicates the patient with any disease or normal. Here five diseases beside normal are considered. Different Machine Learning (ML) approaches may be used for detecting patients with CVD and assisting health care systems also. These are useful for learning and utilizing the patterns discovered from large databases. It applies to a set of information in order to recognize underlying relationship patterns from the information set. It is basically a learning stage. Unknown incoming set of patterns can be tested using these methods. Due to its self-adaptive structure Deep Learning (DL) can process information with minimal processing time. DL exemplifies the use of neural network. A predictive model follows DL techniques for analyzing and assessing patients with heart disease. A hybrid approach based on Convolutional Layer and Gated-Recurrent Unit (GRU) are used in the paper for diagnosing the heart disease.
On the fundamental group of the freudenthal compactification of CW complexes

The Freudenthal compactification provides a functor from the category of locally finite CW complexes and proper maps into the category of compact spaces and continuous maps. It is natural to ask how a topological invariant such as simple– connectedness behaves under such a functor. In this thesis, we prove that the Freudenthal compactification of a simply–connected space is not necessary simply– connected. Moreover, we will introduce an algebraic property which ensures for CW complexes that simple–connectedness is preserved under the Freudenthal functor.
The Association of Spirituality and Well-Being in South African and Ugandan Samples

The Association of Spirituality and Well-Being in South African and Ugandan Samples Alicia Jane Doman Department of Counseling Psychology and Special Education, BYU Doctor of Philosophy Literature has shown a correlation between spirituality and well-being, but this has not been thoroughly studied outside North America, with very few studies conducted in Africa. This study compared data from two sub-Saharan African nations, Uganda and South Africa, that differ markedly in terms of multiple factors that affect well-being, such as mortality rates and access to healthcare, as well as educational and personal growth opportunities. Survey data were collected using the measures of The Spiritual Involvement and Beliefs Scale and Ryff's Personal Well-Being Scale. There were four racial groups represented in the data: Black South Africans, Coloureds, White South Africans, and Black Ugandans. The results showed both similarities and differences among the racial groups in regard to spirituality and well-being. White South Africans had the lowest levels of spirituality, but the groups were fairly equivalent in terms of overall well-being, which finding was surprising, given the fact that the South Africans have much better material conditions and survival rates than Ugandans. Group differences were also observed in terms of the positive and negative aspects of well-being, which were highly correlated in the data from White South Africans but weakly correlated for the other groups. The correlations between the measures of spirituality and wellbeing were positive for all groups, with that association explaining a remarkable 32% of the variance in the Black Ugandan sample but only 4% of the variance in the White South African sample. These data confirm the salience of spirituality to well-being, particularly among the samples indigenous to sub-Saharan Africa.
Oral health literacy of persons withmigration background — first results of the MuMi study

Background. First investigations indicate a migration background of residents in Germany as a discrete risk factor for poor oral health. A lower level of oral health literacy among people with a migration background is considered a reason worthy of being investigated. Aim. This article presents results on oral health literacy and oral health gained from the MuMi study (promoting oral health and oral health literacy of people with a migration background). Methods. The oral health and oral health literacy as well as the sociodemographics of patients with and without migration background were examined in 40 dental surgeries in Hamburg, Germany. Associations betweenmigrant status, oral health, and oral health literacy were analyzed with logistic regressions. Potential confounders were gradually integrated into the multivariate analyses. Results. Patientswith andwithout amigration background differed significantly in oral health literacy and clinical parameters of oral health (approximal plaque index and degree of caries restoration). The logistic regression analysis revealed highly significant associations betweenmigration background, oral health literacy, and oral hygiene, while also accounting for education and socioeconomic status. Discussion.Migration background constitutes a discrete risk factor for lower oral health and oral health literacy for these relevant population groups. This fact needs stronger reflection in further research and political decision-making in order to promote equality of oral health opportunities.
An immune-protein signature combining TRAIL, IP-10 and CRP for accurate prediction of severe COVID-19 outcome

BACKGROUND Accurately identifying COVID-19 patients at-risk to deteriorate remains challenging. Tools integrating host-protein expression have proven useful in determining infection etiology and hold potential for prognosticating disease severity. METHODS Adults with COVID-19 were recruited at medical centers in Israel, Germany, and the United States. Severe outcome was defined as intensive care unit admission, non-invasive or invasive ventilation, or death. Tumor necrosis factor related apoptosis inducing ligand (TRAIL) and interferon gamma inducible protein-10 (IP-10; also known as CXCL10) and C-reactive protein (CRP) were measured using an analyzer providing values within 15 minutes. A signature indicating the likelihood of severe outcome was derived generating a score (0-100). Patients were assigned to 4 score bins. RESULTS Between March and November 2020, 518 COVID-19 patients were enrolled, of whom 394 were eligible, 29% meeting a severe outcome. The area under the receiver operating characteristic curve (AUC) of the signature was 0.86 (95% confidence interval: 0.81-0.91). Performance was not confounded by age, sex, or comorbidities and superior to IL-6 (AUC 0.77; p = 0.033) and CRP (AUC 0.78; p < 0.001). Likelihood of severe outcome increased significantly (p < 0.001) with higher scores. The signature differentiated patients who further deteriorated after meeting a severe outcome from those who improved (p = 0.004) and projected 14-day survival probabilities (p < 0.001). CONCLUSION The derived immune-protein signature combined with a rapid measurement platform is an accurate predictive tool for early detection of COVID-19 patients at-risk for severe outcome, facilitating timely care escalation and de-escalation and appropriate resource allocation. FUNDING MeMed funded the study
Isometric Multi-Shape Matching —Supplementary Material—

Figure 1. Left: We present a novel approach for isometric multi-shape matching based on matching each shape to a (virtual) universe shape (shown semi-transparent). Our formulation represents point-to-point correspondences between shapes i and j as the composition of the shape-to-universe permutation matrix Pi and the universe-to-shape permutation matrix P j . By doing so, the pairwise matchings Pij = PiP > j are by construction cycle-consistent. Middle: Our formulation successfully solves isometric multi-matching of partial shapes. Right: Due to the cycle-consistency we can use our correspondences to faithfully transfer textures across a shape collection.
Multibiometric System for Iris Recognition Based Convolutional Neural Network and Transfer Learning

Multimodal biometric methods have been commonly used by several implementations because of its capability to work with a variety of important drawbacks in unimodal biometric methods, such as noise affectability, populace coverage, intraclass variety, vulnerability to spoofing, and non-universality. In this research, a multimodal biometric realtime method is suggested depending upon the design of a deep learning model for pictures of a person’s (right & left) irises. This system has been implemented by combining the characteristics of convolution neural networks and transfer learning techniques. Through this research, the training system focused on a collection of the back_propagation technique with Adam’s optimization approach utilized to modify weights and adjust learning rates during the learning process. The efficiency of the system is examined on two public datasets obtained in various conditions: IITD and CASIA-Iris-V3 Interval. The implemented system gives an accuracy of 99% for both left & right IITD iris datasets and accuracy of (94% and 93%) for the left and right iris for CASIA-iris-V3 interval datasets respectively after training. An OpenCV library for image pre-processing, Keras, and sci-kit learn python libraries for feature extraction and recognition has been utilized.
Levels of some Heavy Elements in Tissues and Shells of Aquatic Mollusca (Viviparous and Anodonta) in Baghdad City, Iraq.

Some heavy elements accumulated levels, such as Lead, Zinc, Copper, Iron, Cobalt, Cadmium and Mercury were measured in molluscs that inhabitant the irrigation channel of University of Baghdad in AL- Jadriya districts. The shells and tissues of two Molluscs groups, gastropods genus Viviparous and bivalve genus Anodonta, as well as samples of gastropods eggs and water, were analyzing. The results showed that the highest concentration of Lead, Zinc, Copper, and Iron were recorded in Viviparous shells compared to Anodonta shells, but both Cadmium and Cobalt concentrations were identical in both genera shells, any presence of mercury was not recorded in any of these samples. The tissue samples recorded a rise in Zinc and Copper concentration in Viviparous in comparison with Anodonta, but the Lead, Iron and Cadmium concentrations recorded the highest values in Anodonta. Both elements Cobalt and Mercury concentration showed a similarity in both genera. The gastropods eggs were recorded high concentrations of heavy elements as follows Iron (550.00 ±0.57), Lead (128.00±0.57), Zinc (54. ±000.57), Copper (84.00±0.57), Cadmium (0.300±0.05), Cobalt (0.050±0.01) and Mercury (0.001±0.00) ppm. But water samples showed low concentrations in these elements, Iron (0.200±0.05), Lead (0.033±0.008), Zinc (0.025±0.001), Copper (0.008±0.001), Cadmium (0.004±0.001) and Cobalt (0.001±0.00) ppm.
Comparisons of Soft Decision Decoding Algorithms Based LDPC Wireless Communication System

The Low Density Parity Check (LDPC) forward error correction code provides significant results that have been very close to Shannon limit. This paper compares the Bit Error Rate (BER) performance of soft decision decoding algorithms of LDPC codes on AWGN channel. Devising soft decision decoding algorithms which are good in BER performance requires a comparison of probabilistic, log domain and Min-Sum methods. Simulations are conducted with different parameters using MATLAB workspace to evaluate the LDPC system performance. Results have shown that Min-Sum has outperformed the algorithms of Log Domain and Prob. Domain with a considerable amount of the Signal to Noise Ratio (SNR). Finally, the optimal LDPC parameter values have been selected based on the achieved results which provide superior results.