Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced Basis Methods for Uncertainty Quantification

In this work we review a reduced basis method for the solution of uncertainty quantification problems. Based on the basic setting of an elliptic partial differential equation with random input, we introduce the key ingredients of the reduced basis method, including proper orthogonal decomposition and greedy algorithms for the construction of the reduced basis functions, a priori and a posteriori error estimates for the reduced basis approximations, as well as its computational advantages and weaknesses in comparison with a stochastic collocation method [I. Babuska, F. Nobile, and R. Tempone, SIAM Rev., 52 (2010), pp. 317--355]. We demonstrate its computational efficiency and accuracy for a benchmark problem with parameters ranging from a few to a few hundred dimensions. Generalizations to more complex models and applications to uncertainty quantification problems in risk prediction, evaluation of statistical moments, Bayesian inversion, and optimal control under uncertainty are also presented to illustrat.
Comparative Evaluation of 0.25% Lemongrass Oil Mouthwash and 0.2% Chlorhexidine Mouthwash in Fixed Orthodontic Patients Suffering from Gingivitis.

AIM The aim of this study is to compare the antiplaque and antigingivitis efficacy of 0.25% lemongrass oil mouthwash and 0.2% chlorhexidine mouthwash in patients undergoing fixed orthodontic treatment, who are suffering from gingivitis. MATERIALS AND METHODS A total of 60 patients undergoing fixed orthodontic treatment with mild-to-moderate gingivitis were selected for the study. The patients were randomly divided into three groups of twenty each, that is, group I: 0.25% lemongrass oil mouthwash (n = 20); group II: 0.2% chlorhexidine mouthwash (n = 20); and group III: oral prophylaxis (n = 20). Baseline gingival index (GI) and plaque index (PI) were accessed followed by oral prophylaxis was done and the PI score was set to zero for all the patients. Patients were asked to swish their mouth with their respective mouthwashes and brushing (twice daily), that is, morning and before bedtime for 21 days. The PI and GI scores were recorded for all three groups on the 14th and the 21st days. The post hoc Bonferroni test was used for multiple comparisons of mean differences among variables after the application of the analysis of variance (ANOVA) test for comparison within the groups. RESULTS A lower PI and the GI were found in the lemongrass oil mouthwash group by the 14th and the 21st days, respectively, a statistically significant difference (p < 0.001) compared to the chlorhexidine mouthwash group. CONCLUSION The findings of the current study suggested that 0.25% lemongrass oil mouthwash has the potential to be used as a natural or herbal alternative to chlorhexidine mouthwash. CLINICAL SIGNIFICANCE It can be suggested that 0.25% lemongrass oil mouthwash may be a good herbal alternative to mouthwash containing 0.2% chlorhexidine gluconate.
Residential Load Forecasting: An Online-Offline Deep Kernel Learning Method

Residential load forecasting (RLF) is critical for power system operations. Different from traditional system-level load forecasting, studying RLF faces the challenges of high uncertainty. Besides, learning temporal dynamics within the residential load sequences is important. However, existing methods fail to effectively tackle the fore-mentioned challenges simultaneously. In this article, a deep kernel is proposed by integrating the deep soft Spiking Neural Networks (sSNN), which is then applied to perform Gaussian Process (GP) regression. The constructed regressor investigates the temporal dynamics within the residential load sequence and retains the probabilistic advantages for uncertainty estimates. Furthermore, to better address the high uncertainty of RLF, a learning scheme combing both offline and online learning is specifically developed for the regressor. Such a learning scheme contributes to fully exploring historical information while learning the uncertainty from real-time data. The effectiveness of the proposed method is demonstrated on three public and actual residential load datasets.
Learning without Smoothness and Strong Convexity

Recent advances in statistical learning and convex optimization have inspired many successful practices. Standard theories assume smoothness—bounded gradient, Hessian, etc.—and strong convexity of the loss function. Unfortunately, such conditions may not hold in important real-world applications, and sometimes, to fulfill the conditions incurs unnecessary performance degradation. Below are three examples. 1. The standard theory for variable selection via `1-penalization only considers the linear regression model, as the corresponding quadratic loss function has a constant Hessian and allows for exact second-order Taylor series expansion. In practice, however, nonlinear regression models are often chosen to match data characteristics. 2. The standard theory for convex optimization considers almost exclusively smooth functions. Important applications such as portfolio selection and quantum state estimation, however, correspond to loss functions that violate the smoothness assumption; existing convergence guarantees for optimization algorithms hence do not apply. 3. The standard theory for compressive magnetic resonance imaging (MRI) guarantees the restricted isometry property (RIP)—a smoothness and strong convexity condition on the quadratic loss restricted on the set of sparse vectors—via random uniform sampling. The random uniform sampling strategy, however, yields unsatisfactory signal reconstruction performance empirically, in comparison to heuristic sampling approaches. In this thesis, we provide rigorous solutions to the three examples above and other related problems. For the first two problems above, our key idea is to instead consider weaker localized versions of the smoothness condition. For the third, our solution is to propose a new theoretical framework for compressive MRI: We pose compressive MRI as a statistical learning problem, and solve it by empirical risk minimization. Interestingly, the RIP is not required in this framework.
Bootstrapping the empirical bounds on the variability of sample entropy in 24-hour ECG recordings for 1 hour segments

We investigate the variability of one of the most often used complexity measures in the analysis of the time series of RR intervals, i.e. Sample Entropy. The analysis is carried out for a dense matrix of possible r thresholds in 79 24h recordings, for segments consisting of 5000 consecutive beats, randomly selected from the whole recording. It is repeated for the same recordings in random order. This study is made possible by the novel NCM algorithm which is many orders of magnitude faster than the alternative approaches. We find that the bootstrapped standard errors for Sample entropy are large for RR intervals in physiological order compared to the standard errors for shuffled data which correspond to the maximum available entropy. This result indicates that Sample Entropy varies widely over the circadian period. This paper is purely methodological and no physiological interpretations are attempted. MSC 2010: 92B25, 92B15, 68W99
Supplementation with spinach-derived thylakoid augments the benefits of high intensity training on adipokines, insulin resistance and lipid profiles in males with obesity

Introduction This study investigated the effects of 12 weeks of high-intensity functional training (HIFT) combined with spinach-derived thylakoid supplementation on some selected Adipokines and insulin resistance in males with obesity. Method Sixty-eight participants (mean age: 27.6 ± 8.4 yrs.; mean height: 168.4 ± 2.6 cm; mean weight: 95.7 ± 3.8 kg, mean BMI: 32.6 ± 2.6 kg/m2) were randomly divided into four groups of 17 per group: Control group (CG), Supplement group (SG), Training group (TG), and Training + supplement group (TSG). Following baseline measurements, the two training groups (TG and TSG) started the 12 weeks of exercise training program (3 sessions per week). A total of 36 sessions lasting up to 60 min were included in the HIFT program using the CrossFit program. The eligible participants received 5 g/day of thylakoid-rich spinach extract or matching placebo as 5 g/day of raw corn starch (one sachet, 30 min before lunch) for 12 weeks. Baseline assessments were obtained 48 hours before the start of the training protocols and 48 hours after the last training session in all groups. Results There were significant interactions (p<0.001 for all) between exercise and time for adiponectin (ES:0.48), leptin (ES:0.46), resistin (ES:0.3), omentin (ES:0.65), vaspin (ES:0.46), visfatin (ES:0.62), apelin (ES:0.42), RBP4 (ES:0.63), chemrin (0.36) and semaphorin3c (ES: 0.5). Plasma levels of semaphorin3c were significantly correlated (p<0.05) with body weight (r= 0.57), BMI (r= 0.43), FFM (r= -0.612), FAT (r= 0.768), VO2peak (r=-0.53), insulin (r= 0.756), glucose (r= 0.623), and HOMA-IR (r= 0.727). There were also significant group differences in insulin (ES: 0.77), glucose (ES: 0.21), and HOM-IR (ES: 0.44) (p<0.05). Discussion Our findings indicate that 12 weeks of HIFT supplemented with spinach-derived thylakoid reduced levels of leptin, resistin, vaspin, visfatin, apelin, RBP4, chemrin, semaphorin3c and insulin resistance while increasing adiponectin and omentin levels in men with obesity.
Lie groupoids and Lie algebroids in differential geometry, by Kirill Mackenzie

4. G. D. Mostow and P. Deligne, Monodromy of hypergeometric functions and nonlattice integral monodromy, Inst. Hautes Etudes Sci. Publ. Math. 46 (1983). 5. E. Picard, Sur les fonctions hyperfuchsiaes provenant des séries hypergéométriques de deux variables, Ann. École Norm. Sup. 52 (1885), 357-384. 6. I. R. Shafarevich, Basic algebraic geometry, Grundlehren Math. Wiss. , Band 213, Springer-Verlag, Berlin and New York, 1974.
Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem

In this study, artificial neural networks (ANNs) are being used to diagnose hair loss in patients. An autoimmune condition known as Alopecia Areata (AA) results in hair loss in the affected area. The most recent figures from throughout the world show that AA affects 1 in 1000 persons and has a 2% incidence rate. Based on the look of photographs with healthy hair in the dataset, machine learning techniques were employed to classify the conditions. Before making predictions, each of these ANNs algorithms creates a prediction model using pictures of healthy hair. The aim of this study is to evaluate the accuracy of neural networks for alopecia detection in human subjects. The study presents a classification framework for distinguishing between healthy hairs (HHs) and Alopecia Areata (AA). The framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement and segmentation techniques to enhance the quality of the images. Additionally, Data Augmentation (DA) is employed to generate additional data and improve the precision of the proposed framework. To extract features from the images, two powerful techniques are utilized. The Visual Geometry Group (VGG), which consists of very deep convolutional networks designed for large-scale image recognition, is employed. VGG networks have proven to be effective in learning complex features directly from data, eliminating the need for manual feature extraction. Additionally, a Convolutional Neural Network (CNN), a deep learning network architecture specifically designed for image processing tasks, is employed. To create a machine learning model for classification, the Support Vector Machine (SVM) approach is utilized. SVM is a widely used algorithm in supervised learning, capable of solving both classification and regression problems. Its versatility and effectiveness make it a suitable choice for the classification task in this study. By combining the CLAHE enhancement, segmentation, data augmentation, feature extraction using VGG and CNN, and classification using SVM, the proposed framework aims to accurately classify HHs and AA cases.
Fin-EMRC: An Efficient Machine Reading Comprehension Framework for Financial Entity-Relation Extraction

Extracting entities and their relationships from financial documents is crucial for analyzing and predicting future market trends. However, the current state of the art in this field faces two major challenges: multiple sentences between related entities and poor few-shot performance caused by the vast amount of knowledge required in the financial domain. To address these challenges, we propose a framework for entity-relation extraction in financial documents that leverages multi-turn machine reading comprehension (MRC) and a Longformer model to handle long text dependencies. Furthermore, we propose a knowledge-enhanced method that incorporates structured knowledge from the financial domain. We also propose a new query template scheme that reduces the computational complexity of inferring complicated entity relations, thus improving the inference efficiency of the MRC framework. We evaluated our proposed framework on both general domain datasets and a real-world annotated Financial Entity-Relation (FinER) dataset. The results demonstrate that our Fin-EMRC framework outperforms currently available state-of-the-art methods in terms of entity and relation extraction accuracies. Moreover, the proposed efficient framework requires only 1.8 to 2.7 times less time for inference compared to the standard MRC framework.
Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable l1-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.
Hyponym Relation Extraction from Hyperlinks using Motif Patterns with Feature Combination

: The project presents the grammatical relationship between concepts in Knowledge Graphs such as Word-Net and DBpedia. Past work on grammatical relationship method has either the form grammatical system connection between concepts. To develop the grammatical relationship method, namely w path, to connect those two ways, using Information Content to weight the abridged path distance between ideas. Every collection-based Information Content is computed from the allocation ideas over a textual collection. Weighing the grammatical relationship between the communication is an important action in modify processing field. The project planned a new relationship measure. So, the new hyponym relation extraction approach based on the network pattern of Wikipedia hyper-sonic was planned. Every hyper-sonic was mapped 13 types of three-node patterns.
The Evolution of Life Is a Road Paved with the DNA Quadruplet Symmetry and the Supersymmetry Genetic Code

Symmetries have not been completely determined and explained from the discovery of the DNA structure in 1953 and the genetic code in 1961. We show, during 10 years of investigation and research, our discovery of the Supersymmetry Genetic Code table in the form of 2 × 8 codon boxes, quadruplet DNA symmetries, and the classification of trinucleotides/codons, all built with the same physiochemical double mirror symmetry and Watson–Crick pairing. We also show that single-stranded RNA had the complete code of life in the form of the Supersymmetry Genetic Code table simultaneously with instructions of codons’ relationship as to how to develop the DNA molecule on the principle of Watson–Crick pairing. We show that the same symmetries between the genetic code and DNA quadruplet are highly conserved during the whole evolution even between phylogenetically distant organisms. In this way, decreasing disorder and entropy enabled the evolution of living beings up to sophisticated species with cognitive features. Our hypothesis that all twenty amino acids are necessary for the origin of life on the Earth, which entirely changes our view on evolution, confirms the evidence of organic natural amino acids from the extra-terrestrial asteroid Ryugu, which is nearly as old as our solar system.
Methods for Optimal Output Prediction in Computational Fluid Dynamics.

Methods for Optimal Output Prediction in Computational Fluid Dynamics by Steven M. Kast Chair: Krzysztof J. Fidkowski In a Computational Fluid Dynamics (CFD) simulation, not all data is of equal importance. Instead, the goal of the user is often to compute certain critical outputs – such as lift and drag – accurately. While in recent years CFD simulations have become routine, ensuring accuracy in these outputs is still surprisingly difficult. Unacceptable levels of output error arise even in industry-standard simulations, such as the steady flow around commercial aircraft. This problem is only exacerbated when simulating more complex, unsteady flows. In this thesis, we present a mesh adaptation strategy for unsteady problems that can automatically reduce errors in outputs of interest. This strategy applies to problems in which the computational domain deforms in time – such as flapping-flight simulations – and relies on an unsteady adjoint to identify regions of the mesh contributing most to the output error. This error is then driven down via refinement of the critical regions in both space and time. Here, we demonstrate this strategy on a series of flapping-wing problems in two and three dimensions, using high-order discontinuous Galerkin (DG) methods for both spatial and temporal discretizations. Compared to other methods, results indicate that this strategy can deliver a desired level of output accuracy with significant reductions in computational cost. After concluding our work on mesh adaptation, we take a step back and investigate another idea for obtaining output accuracy: adapting the numerical method itself. In particular, we show how the test space of discontinuous finite element methods can be “optimized” to achieve accuracy in certain outputs or regions. In this work, we compute test functions that ensure accuracy specifically along domain boundaries. These regions – which are vital to both scalar outputs (such as lift and drag) and distributions (such as pressure and skin friction) – are often the most important from an engineering standpoint.
Terrestrial Laser Scanning for 3D Assets Registry

This study aimed to assess the feasibility of using terrestrial laser scanning (TLS) technology for creating 3D asset registries in facilities management. The research utilized a case study approach to scanning an industrial facility, and the resulting data was processed to create a 3D model of the assets. The study found that TLS technology can produce highly accurate and detailed 3D models of assets, which can aid in asset management and maintenance. However, the technology has some limitations, such as cost and the need for skilled operators. The study suggests that TLS technology can be a valuable tool for asset management and recommends further research in this area.
Association between frequency of breakfast consumption and cardiometabolic risk in Peruvian university teachers, 2019–2020

Background Although the relationship between health status and dietary intake has been extensively studied in the general population, there is a lack of research that has specifically examined the association between frequency of breakfast consumption and cardiometabolic risk in university teachers. Objective To determine the association between the frequency of breakfast consumption and cardiometabolic risk in university teachers. Methods A cross-sectional study was conducted in 176 teachers from a private university located in the eastern region of Lima, Peru (Mage: 37.0 years; SD: 0.8, range: 24–59 years). The study was conducted during the period from December 2019 to February 2020. Information was collected on anthropometric and biochemical parameters and frequency of breakfast consumption. Multinomial logistic regression models were used to explore the association between frequency of breakfast with sociodemographic, anthropometric, and biochemical variables. Results The highest prevalence of excess body weight (44.4%) was observed in those who consumed breakfast 0 to 2 days/week, but without statistical differences. Those who reported Low-density lipoprotein cholesterol (LDL-C) < 160 mg/dL were 77% less likely to fall into the 3–5 day/week breakfast frequency category than those who reported a regular frequency of breakfast (6 to 7 days/week) (Adjusted OR = 0.23, 95% CI 0.08 to 0.73; p < 0.05). In addition, teachers who reported a breakfast frequency of 3 to 5 days/week were 83% more likely to have a glucose concentration < 110 mg/dL compared to those who consumed breakfast of 6 to 7 days/week (Adjusted OR = 0.17, 95% CI 0.04 to 0.75; p < 0.05). Conclusion Skipping breakfast for an extended period of time can have detrimental effects on cardiometabolic health. Promoting the benefits of breakfast could be a health message of great public health interest.
Storm Katie – Rapid cyclogenesis and damaging winds of Easter Monday 2016

Storm Katie was the 11th officially named storm of the winter and spring seasons of 2015/16, affecting primarily southern parts of England on Easter Monday (28 March 2016). The potential for disruptive winds was identified at a relatively long lead time, with communication to this effect being made by the Met Office 7 days before the storm arrived in the UK. This article looks at the forecasts and warnings in advance of the storm, examines the evolution of the system with a brief discussion on the application of conceptual models and then rounds up with the impacts from the associated wind, rain and snow.
Habilidades de dirección hotelera en tiempos de crisis

Las organizaciones hoteleras requieren de profesionales que posean las competencias directivas para la correcta gestión de los hoteles que lideran. Por este motivo, este estudio pretende investigar cuál sería el perfil ideal de la dirección hotelera de establecimientos de 4 y 5 estrellas en tiempos de crisis. Para identificar las habilidades prioritarias para ese perfil directivo se realizan 151 encuestas a directores de hoteles y a profesionales de los departamentos de recursos humanos de empresas hoteleras. Adicionalmente, la propuesta interconecta la dirección hotelera, la gestión de crisis y las competencias personales, ofreciendo especial atención a las habilidades blandas (o soft skills) y a la inteligencia emocional. Los resultados sugieren competencias esenciales a tener en cuenta para directivos hoteleros y su selección y formación por parte de los técnicos de recursos humanos, manifestando la necesidad de priorizar las habilidades sociales en la industria hotelera.