Miracle-dz/UniFAR-FTUs huggingface dataset:
1.La gerencia del marketing educativo en el contexto universitario del estado Yaracuy

Nowadays, the universities must achieve their mission in environments of uncertainty; in this sense, it is proposed that the management targeting the consolidation of the academic offer must develop strategies like marketing as a tool for recruiting students. From this perspective, it was proposed a doctoral dissertation pursuing the goal of building a theoretical approach of educational marketing management in the higher education context in Yaracuy. For the study, the qualitative approach was selected, with an ontological view towards the theory of multiple realities. The study was epistemologically based on the intersubjectivity. Phenomenology and hermeneutics were used as methods because they contribute to show the intersubjective meanings that social actors possess on educational marketing. The selected technique for gathering information was the in-deep interview to nine (9) social actors belonging to three universities from Yaracuy. Sources triangulation was used to unveil the findings, among which highlights that students capacitation is favored by the institutional image, educational quality and image of the university to the community.
A new procedure for elimination of atrial fibrillation associated with mitral valve disease: a proof-of-concept study

Background: Left atrial enlargement and fibrosis have been linked to the pathogenesis of atrial fibrillation (AF). The authors aimed to introduce a novel concept and develop a new procedure for AF treatment based on these characteristics. Methods: The study included three stages. The first stage was a descriptive study to clarify the characteristics of the left atrial enlargement and fibrosis’ distribution in patients with mitral valve disease and long-standing persistent AF. Based on these characteristics, the authors introduced a novel concept for AF treatment, and then translated it into a new procedure. The second stage was a proof-of-concept study with this new procedure. The third stage was a comparative effectiveness research to compare the clinical outcomes between patients with this new procedure and those who received Cox-Maze IV treatment. Results: Based on the nonuniform fashion of left atrial enlargement and fibrosis’ distribution, the authors introduced a novel concept: reconstructing a left atrium with appropriate geometry and uniform fibrosis’ distribution for proper cardiac conduction, and translated it into a new procedure: left atrial geometric volume reduction combined with left appendage base closure. As compared to the Cox-Maze IV procedure, the new procedure spent significantly shorter total surgery time, cardiopulmonary bypass time, and aortic cross-clamp time (P<0.001). Besides, the new procedure was related to a shorter ICU stay period (odd ratio (OR)=0.45, 95% CI=0.26–0.78), lower costs (OR=0.15, 95% CI=0.08–0.29), and a higher rate of A wave of transmitral and transtricuspid flow reappearance (OR=1.76, 95% CI=1.02–3.04). Conclusions: The new procedure is safe and effective for eliminating AF associated with mitral valve disease.
Regularization Explanation of Adjustable Histogram Equalization and Its Improvement

In view of histogram equalization regarded as typical ill-posed problem,the adjustable histogram equalization method is explaned by regularization theory,and its improvement is proposed in order to obtain the better effect of image enhancement.Firstly,the objective function of adjustable histogram equalization method is simplified and explaned by means of information energy as a regular item.Secondly,in consideration of the dual relationship between information entropy and information energy,the new adjustable histogram! equalization model based on information entropy as regularization is obtained.Thirdly,the regular item coefficient choosing method of adjustable histogram equalization is presented by means of information entropy or energy of gray levels probability distribution.Finally,the adjustable histogram equalization improvement method based on modifying cumulative probability of gray levels is proposed in order to improve the image enhancement effect of traditional histogram equalization method.Experimental results show that the improvement method of adjustable histogram equalization is effective.
Reasoning with propositional knowledge: frameworks for boolean satisfiability and knowledge compilation

This dissertation is concerned with two fundamental problems that arise when reasoning about knowledge expressed in propositional logic. The first problem is the Boolean satisfiability problem, whereas the second problem is known as knowledge compilation. We introduce several frameworks for studying reasoning algorithms related to these problems. Based on the proposed frameworks, we derive practical algorithms for more efficient reasoning as well as theoretical results that shed light on the strengths and limitations of various reasoning approaches. In the case of Boolean satisfiability, we propose a framework that captures the essence of the state-of-the-art algorithm for solving this problem. Based on this framework, we answer an open question by precisely characterizing the inferential power of this algorithm. Even though this algorithm has traditionally been regarded as a combinatorial search algorithm, we are able to achieve this characterization using only the language of proof complexity. We then propose two practical techniques for improving the efficiency of the algorithm from this perspective. In the case of knowledge compilation, we propose a new propositional language that could provide compact representations of propositional knowledge. This language also serves as a framework that unifies many well-known languages, including the influential ordered binary decision diagram (OBDD). We then study the new language, as well as some of its distinguished restrictions, in terms of (i) useful reasoning operations it allows and (ii) its ability to represent propositional knowledge compactly. We also introduce a framework for characterizing the sizes of Boolean functions represented in this language. We derive lower and upper bounds that subsume existing bounds known for the size of OBDDs. We show that our bound results can be used to obtain interesting size bounds for various Boolean functions.
Inner approximation of convex cones via primal-dual ellipsoidal norms

We study ellipsoids from the point of view of approximating convex sets. Our focus is on finding largest volume ellipsoids with specified centers which are contained in certain convex cones. After reviewing the related literature and establishing some fundamental mathematical techniques that will be useful, we derive such maximum volume ellipsoids for second order cones and the cones of symmetric positive semidefinite matrices. Then we move to the more challenging problem of finding a largest pair (in the sense of geometric mean of their radii) of primal-dual ellipsoids (in the sense of dual norms) with specified centers that are contained in their respective primal-dual pair of convex cones.
On Carbon Substitution and Storage Factors for Harvested Wood Products in the Context of Climate Change Mitigation in the Norwegian Forest Sector

Abstract Harvested wood products (HWP) can play an important role in climate-smart bioeconomic transformation. They contribute to climate change mitigation through two main mechanisms: carbon storage and substitution. Norway has ambitions to strengthen the contribution of its forest sector in climate change mitigation. Ideally, the future production and use of HWPs would increasingly shift towards products with high carbon storage and substitution benefits. We collected data from the literature and, when necessary, supplemented it with our own calculations, on carbon storage and substitution factors of HWPs that seemed relevant in evaluating the climate change mitigation potential in the context of the Norwegian forest sector. There are many uncertainties in the parameters. We identified and examined in more detail some uses of wood for industrial products that offer clear substitution benefits and, in some cases, long-term carbon storage. Wood-based construction materials, textile fibres, and insulation materials are examples of such products that could have high potential in the bioeconomy transformation in Norway.
The genetic and dietary landscape of the muscle insulin signalling network

Metabolic disease is caused by a combination of genetic and environmental factors, yet few studies have examined how these factors influence signal transduction, a key mediator of metabolism. Using mass spectrometry-based phosphoproteomics, we quantified 23,126 phosphosites in skeletal muscle of five genetically distinct mouse strains in two dietary environments, with and without acute in vivo insulin stimulation. Almost half of the insulin-regulated phosphoproteome was modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affected insulin signalling in a strain-dependent manner. Our data revealed coregulated subnetworks within the insulin signalling pathway, expanding our understanding of the pathway’s organisation. Furthermore, associating diverse signalling responses with insulin-stimulated glucose uptake uncovered regulators of muscle insulin responsiveness, including the regulatory phosphosite S469 on Pfkfb2, a key activator of glycolysis. Finally, we confirmed the role of glycolysis in modulating insulin action in insulin resistance. Our results underscore the significance of genetics in shaping global signalling responses and their adaptability to environmental changes, emphasizing the utility of studying biological diversity with phosphoproteomics to discover key regulatory mechanisms of complex traits.
Defining cat mitogenome variation and accounting for numts via multiplex amplification and Nanopore sequencing

Hair shed by domestic cats is a potentially useful source of forensic evidence. Analysable hair DNA is predominantly mitochondrial, but the recent domestication history of cats means that mtDNA diversity is low. A 402-bp control region segment is usually sequenced, defining only a small number of distinct mitotypes in populations. Previously, we used a long-amplicon approach to sequence whole mitogenomes in a sample of blood DNAs from 119 UK cats, greatly increasing observed diversity and reducing random match probabilities. To exploit this variation for forensic analysis, we here describe a multiplex system that amplifies the cat mitogenome in 60 overlapping amplicons of mean length 360 bp, followed by Nanopore sequencing. Variants detected in multiplex sequence data from hair completely mirror those from long-amplicon data from blood from the same individuals. However, applying the multiplex to matched blood DNA reveals additional sequence variants which derive from the major feline nuclear mitochondrial insertion sequence (numt), which covers 7.9 kb of the 17-kb mitogenome and exists in multiple tandem copies. We use long-amplicon Nanopore sequencing to investigate numt variation in a set of cats, together with an analysis of published genome sequences, and show that numt arrays are variable in both structure and sequence, thus providing a potential source of uncertainty when nuclear DNA predominates in a sample. Forensic application of the test was demonstrated by matching hairs from a cat with skeletal remains from its putative mother, both of which shared a globally common mitotype at the control region. The random match probability (RMP) in this case with the CR 402-bp segment was 0.21 and this decreased to 0.03 when considering the whole mitogenome. The developed multiplex and sequencing approach, when applied to cat hair where nuclear DNA is scarce, can provide a reliable and highly discriminating source of forensic genetic evidence. The confounding effect of numt co-amplification in degraded samples where mixed sequences are observed can be mitigated by variant phasing, and by comparison with numt sequence diversity data, such as those presented here.
Is EEG is better left alone?

A recent article (Delorme, A., 2023, EEG is better left alone. Scientific Reports, 13, 2372. https://doi.org/10.1038/s41598-023-27528-0) proposed a metric to determine the benefit of applying pre-processing methods to EEG data. Using that metric, it concluded that most pre-processing methods do not improve data quality. The question is revisited in this short paper, using a sample of the same data as used by that paper. It is argued that the metric is of limited applicability, and that in some situations pre-processing might be critical to make good use of the data. Note: This paper was submitted to Scientific Reports as a commentary to the paper cited and rejected without review.
Cellstitch: 3D cellular anisotropic image segmentation via optimal transport

Background Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice. Methods To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. Results We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. Conclusions The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
Contrôle de l'équation des ondes dans des ouverts comportant des coins

On etudie l'equation des ondes avec conditions de Dirichlet dans un onvert borne a coins (inclus dans R 2 ). On demontre que la condition de controle geometrique introduite par C. Bardos, G. Lebeau et J. Rauch est une condition suffisante pour la controlabilite exacte au bord de ce systeme.
Problème sommatoire par maximum d'entropie

We introduce a summation method of Fourier series for probability measures with compact support by «maximum entropy». We study the efficiency of this method: type and speed of convergence, comparison with other usual linear methods (Dirichlet and Fejer kernel). We especially deal with measures similar to finite linear combinations of Dirac masses On introduit une methode de sommation de serie de Fourier des mesures de probabilite a support compact par «maximum d'entropie» et on s'interesse a l'efficacite de cette sommation: convergence (en quel sens), vitesse de convergence, comparaison avec diverses sommations lineaires usuelles (noyau de Dirichlet, de Fejer). En particulier, on etudie des densites ressemblant a une somme finie (en un sens a preciser) de masses de Dirac
RPCGB Method for Large-Scale Global Optimization Problems

In this paper, we propose a new approach for optimizing a large-scale non-convex differentiable function subject to linear equality constraints. The proposed method, RPCGB (random perturbation of the conditional gradient method with bisection algorithm), computes a search direction by the conditional gradient, and an optimal line search is found by a bisection algorithm, which results in a decrease of the cost function. The RPCGB method is designed to guarantee global convergence of the algorithm. An implementation and testing of the method are given, with numerical results of large-scale problems that demonstrate its efficiency.
The Effect of Diabetes and Hyperglycemia on Horizontal Guided Bone Regeneration: A Clinical Prospective Analysis

Guided bone regeneration (GBR) is a reconstructive procedure for treating atrophic alveolar ridges. This study aims to assess the correlation between different glycemic control levels and clinical findings in patients undergoing horizontal GBR before implant placement. The study population consisted of all patients requiring horizontal GBR. Patients were divided into three groups based on HbA1c levels: non-diabetic normoglycemic patients (HbA1c < 5.7%), non-diabetic hyperglycaemic patients (HbA1c < 6.5%), and patients with controlled diabetes (HbA1c < 7%). The primary outcomes were the horizontal (mm) and vertical (mm) dimensional changes of the alveolar ridge 6 months after the procedure. The study sample consisted of 54 patients. Sixty-eight implants (95.8%) were classified as “successful,” meaning the possibility of inserting a standard-sized implant following the GBR (diameter ≥ 4 mm). There was a statistically significant difference between the three groups in terms of horizontal gain at 6 months: in particular, there was a statistically significant difference between group 1 and group 2 (p = 0.026) and between group 1 and group 3 (p = 0.030). The present investigation showed that patients with HbA1c levels below 7% could undergo GBR and obtain a statistically significant horizontal bone gain.
The Role of Trace Elements and Minerals in Osteoporosis: A Review of Epidemiological and Laboratory Findings

The objective of the present study was to review recent epidemiological and clinical data on the association between selected minerals and trace elements and osteoporosis, as well as to discuss the molecular mechanisms underlying these associations. We have performed a search in the PubMed-Medline and Google Scholar databases using the MeSH terms “osteoporosis”, “osteogenesis”, “osteoblast”, “osteoclast”, and “osteocyte” in association with the names of particular trace elements and minerals through 21 March 2023. The data demonstrate that physiological and nutritional levels of trace elements and minerals promote osteogenic differentiation through the up-regulation of BMP-2 and Wnt/β-catenin signaling, as well as other pathways. miRNA and epigenetic effects were also involved in the regulation of the osteogenic effects of trace minerals. The antiresorptive effect of trace elements and minerals was associated with the inhibition of osteoclastogenesis. At the same time, the effect of trace elements and minerals on bone health appeared to be dose-dependent with low doses promoting an osteogenic effect, whereas high doses exerted opposite effects which promoted bone resorption and impaired bone formation. Concomitant with the results of the laboratory studies, several clinical trials and epidemiological studies demonstrated that supplementation with Zn, Mg, F, and Sr may improve bone quality, thus inducing antiosteoporotic effects.
Automatic Penaeus Monodon Larvae Counting via Equal Keypoint Regression with Smartphones

Simple Summary To enhance farming efficiency, most farmers prefer to purchase Penaeus larvae as opposed to hatching them themselves. However, counting small and highly congested larvae during transactions is a challenging task to accomplish manually. We intend to improve counting precision and decrease human labor costs. In this work, an equal keypoint regression method is proposed to address these challenges. We employed five different types of smartphones to capture thousands of high-resolution images under various challenging environmental conditions. Then, we selected 1420 images to build a high-resolution dataset. In addition, this high-resolution dataset included general point annotations for use. Following training with this dataset, we obtained a model that we tested with a real Penaeus monodon larvae dataset. The results showed that the average model accuracy for the 720 images with seven density groups in the test dataset was 93.79%. Ultimately, our trained model demonstrated greater efficiency than the classical density map algorithm. Abstract Today, large-scale Penaeus monodon farms no longer incubate eggs but instead purchase larvae from large-scale hatcheries for rearing. The accurate counting of tens of thousands of larvae in these transactions is a challenging task due to the small size of the larvae and the highly congested scenes. To address this issue, we present the Penaeus Larvae Counting Strategy (PLCS), a simple and efficient method for counting Penaeus monodon larvae that only requires a smartphone to capture images without the need for any additional equipment. Our approach treats two different types of keypoints as equip keypoints based on keypoint regression to determine the number of shrimp larvae in the image. We constructed a high-resolution image dataset named Penaeus_1k using images captured by five smartphones. This dataset contains 1420 images of Penaeus monodon larvae and includes general annotations for three keypoints, making it suitable for density map counting, keypoint regression, and other methods. The effectiveness of the proposed method was evaluated on a real Penaeus monodon larvae dataset. The average accuracy of 720 images with seven different density groups in the test dataset was 93.79%, outperforming the classical density map algorithm and demonstrating the efficacy of the PLCS.
The Adsorption Performance of Porous Activated Carbons Prepared from Iron (II) Precursors Precipitated on the Porous Carbon Matrix Thermolysis

The creation of new compounds featuring high adsorption and catalytic performance with magnetic properties of the material is one of the important fields of magnetochemistry. The typical synthetic schemes of magnetic carbonaceous adsorbents are rather complicated due to the use of inert atmosphere and difficult wet methods of magnetite precipitation. The arising experimental issues prevent industrial production of magnetically activated carbons. In order to overcome these obstacles, we suggested a novel approach to porous carbons: magnetite composite synthesis based on iron (II) salt precipitation on porous carbon and subsequent thermolysis. We facilitated the process at the stage of the material washing. The synthetic route used is simple and can be applied industrially. The present paper is focused on the adsorption performance of a product prepared from commercial activated carbons BAU-A and AG-3. The porous structure was studied with low-temperature nitrogen adsorption that revealed surface area decreased by 26% in the case of BAU-A and 40% in the case of AG-3 with an increase in mesopore volume. Phenol and nitrobenzene adsorption from water solution was tested with magnetic carbon prepared from BAU-A. The adsorption isotherms obtained are described well using the Langmuir model. The limiting adsorption value in the case of magnetic porous carbon is lower than in the case of pristine carbon. The relative decrease in limiting adsorption value is close to the relative decrease in the specific surface area. The adsorption constant remains the same, showing that adsorption centers of phenol and nitrobenzene are the same for porous magnetic carbon and its activated carbon precursor. Thus, we showed in the present study that the magnetically activated carbons we developed almost retain the adsorption performance of their activated carbon precursors.