# Universidad de La Rioja (Spain)

• Logroño, La Rioja, Spain
Recent publications
In this work, a uniparametric generalization of the iterative method due to Kurchatov is presented. The iterative model presented is derivative‐free and approximates the solution of nonlinear equations when the operator is non‐differenciable. As the accessibility of the Kurchatov method is usually a problem in the application of the method, since the set of initial guesses that guarantee the convergence of the method is small, the main objective of this work is to improve the Kurchatov iterative method in its accessibility while maintaining and even increasing its speed of convergence. For this purpose, we introduce a variable parameter in the iterative function of the Kurchatov method that allows us to get a better approximation of the derivative by using a symmetric uniparameteric first‐order divided difference operator. We perform a complex dynamic study that corroborate the improvements in the accessibility region. Moreover, a complete analysis of the local and semilocal convergence is established for the new uniparametric iterative method. Finally, we apply the theoretical results to solve a nonlinear integral equation showing the usefulness of the work.
The research paper presents the heterogeneous computing model for analysis & restoration of human walking deformity and posture instability. Gait related walking activities are very important for analysis of postural instability, repairment of gait abnormality, diagnosis of cognitive declination, enhance the cognitive ability of human centered humanoid robot system, and many clinical diagnosis e.g. Parkinson, pathological gait, freezing of gait etc., at an early stage. For experiment analysis, ten different lower limb activities are being considered of healthy and crouch walking subjects. Total 25 healthy and 10 crouch walk subjects are considered for experiment purpose of different age groups, sex, and mental status. To achieve this objective the pattern of 10 different rehabilitation activities are captured using RGB-Depth (RGB-D) camera and classified using heterogeneous deep learning models. Different deep learning models Convolutional Neural Network (CNN) and CNN-LSTM (CNN-Long Short Term Memory) are used for classification of these rehabilitation exercises. The RGB-D data is obtained using Microsoft Kinect v2 sensor on 100Hz sampling frequency. Experimental results have shown significant activity recognition accuracy with 96% and 98% for CNN and CNN-LSTM models respectively.
Vineyards and their associated socio-economic activities are relevant sectors worldwide. Still, this agroecosystem is one of the most intensely managed crops and erosion-prone land areas. The conventional viticulture practices to control pests, diseases, and weeds, like tillage and agrochemical applications, accelerate the loss of soil biodiversity and compromise the presence of beneficial soil organisms such as the entomopathogenic nematodes (EPNs). Such human disturbances in the agroecosystems can strongly affect abiotic (e.g., soil texture and properties) and biotic factors (natural enemies and potential competitors) that modulate the EPN activity as biological control agents. For the first time in viticulture, this study aimed to investigate the impact of differentiating management on the EPN community and associated soil organisms and if their assemblage will provide indicators of better practices for sustainable farming. We hypothesized that organic pest management and alternative strategies to tillage might enhance the abundance and activity of the native EPN community in vineyard soils. In autumn 2019, we collected two composite soil samples from 80 vineyards distributed across the Guaranteed Designation of Origin (denominated DOCa) Rioja region. The sites belonged to one category of each of the two factors: pest management (integrated vs. organic, 40 plots each) and soil managing (tillage vs. cover cropping, 48 and 32 vineyards, respectively). Isolated through sucrose-gradient centrifugation and employing species-specific primers/probe qPCR sets, we investigated the presence of ten EPN species and associated soil organisms: four free-living nematodes (FLNs), six nematophagous fungi (NF), and two ectoparasitic bacteria (EcPB). Besides, we estimated the EPN activity using the traditional insect-bait method. We included in the analysis twenty soil variables to characterize the evaluated treatments and assess their impact on soil organism distribution. Our results provide evidence on the support of organic viticulture to beneficial soil organisms, notably the activity of native EPNs. We also reported a higher abundance of S. feltiae (the predominant steinernematid species in Europe) and FLNs for organic farming than IMP, while the presence of NF and EcPB resulted in unaffected. Contrarily, the soil management practices considered did not differ in their impact on EPNs or their natural enemies/competitors, even if contrasted for several soil properties. Future research may expand the screened soil-dwelling species using novel molecular technics to unravel their complex interactions and determine the best farming practices to preserve soil health.
Background and aims Since the acceptance of Internet gaming disorder (IGD) as a “disorder due to addictive behaviors”, research has proliferated exponentially. The present review focuses on the conceptualization of IGD, its diagnosis and assessment, associated factors and existing prevention and treatment plans to address it. Results and conclusions The discrepancies between the diagnostic criteria for IGD proposed by the two central diagnostic entities, as well as the questioning of their clinical validity, have generated multiple proposals for the diagnosis and psychometric evaluation of IGD. Likewise, there have been numerous suggestions to prevent this pathology, with the involvement of governments, the gaming industry and health institutions. Finally, multiple treatment plans have been proposed, both pharmacological and psychological, although only the efficacy of cognitive behavioral therapy has been tested. It is essential, therefore, to delve deeper into this disorder by addressing the central limitations of the current literature.
At the time of designing structures up to date, the density and magnitude of the load have increased, and the requirements for regulation have also become more stringent. To ensure the essential requirements, especially the mechanical resistance and stability, the numerical modelling of the structure is carried out according to the current regulations. Due to various assumptions, idealization, discretization, and parameterizations that are introduced numerical modelling, obtained numerical model may not always reflect the actual structural behavior. It is known that these structures have a hidden resistance that can be determined by combining experimental investigations (static or/and dynamic tests) and finite element model updating methods to minimize the differences between the actual and predicted structural behavior. This paper provides a review of the FEMU process and methods used and summarizes the FEMU approach to help future engineers to select the appropriate method for solving some discussed issues. First, the main terms important for understanding FEMU are introduced. The whole process of model updating is described step by step: selection of updating parameters (design variables), definition of the model updating problem, its solution using different FEMU methods. An overview of the following methods is given: sensitivity-based, maximum likelihood, non-probabilistic, probabilistic, response surface and regularization methods. Each of the method is presented with the corresponding mathematical background, implementation steps, and examples of studies from the literature.
We prove that the fundamental function of any almost greedy basis of $$L_p$$ L p , $$1<p<\infty$$ 1 < p < ∞ , grows as either $$(m^{1/p})_{m=1}^\infty$$ ( m 1 / p ) m = 1 ∞ or $$(m^{1/2})_{m=1}^\infty$$ ( m 1 / 2 ) m = 1 ∞ .
Aims To explore caregivers' needs and problems in three European countries and associate the clusters of caregivers' needs with their sociodemographic characteristics. Design A qualitative focused mixed methods design was used. Methods In total, 52 caregivers of heart failure (HF) people were interviewed in three European countries between March 2017 and December 2018. Transcripts were analysed using the seven‐phase method of the exploratory multidimensional analysis according to Fraire with Reinert lexical classes findings were organized in dendrograms. Mayring's content analysis was also performed. Results Three clusters of caregivers were identified: spouses, adult children and non‐family members. Caregivers not only provide HF patients with vital unpaid support for their physical and emotional needs, but they are continually trying to cope with their social isolation and deteriorating health. Conclusions Informal caregiving emerged as a complex process influenced by various sociodemographic factors. Gender, relationship type and economic status are the important factors to be considered planning to develop approaches to address the needs of caregivers serving people with heart failure. Impact A comprehensive understanding of the nature of informal caregiving of individuals with heart failure, the complexity of the real‐world sociodemographic and cultural factors is warranted. The use of the EMDA method gave us the possibility of processing large masses of qualitative data through rapid, complex calculations. In detail, AATD allowed us to study in deep the significant fuzziness of what caregivers expressed and to analyse the content of the entire interviews and to produce global knowledge by using multi‐dimensional statistical methods to grasp the fundamental sense of the interviews, beyond the simple words. Three clusters were identified in the samples, including spouses, adult children and non‐family members. This study demonstrated that some sociodemographic characteristics could lead to everyday needs. Therefore, these demographic characteristics should be considered in developing targeted interventions. The research was conducted in Europe, but the technique shown can be replicated everywhere. The findings not only impact nursing but can be extended to all those stakeholders who concur with a public health educational mission. Patient or Public Contribution Carers were involved in this study after the discharge of their loved ones or at the time of the outpatient visit. They were involved after they had been observed in their dynamics of involvement in caring of the familiars or friends with heart failure.
Hypothenar hammer syndrome is a rare cause of vascular insufficiency. Generally, patients report a history of repetitive trauma to the hypothenar region of the hand. Symptoms often consist of cold intolerance, pain, paleness, and paresthesia due to digital ischemia. The severity of these symptoms will depend on the extent of ulnar artery occlusion and the presence or absence of collaterals between this artery's superficial and deep branches. It is a rare clinical entity, which on multiple occasions requires a surgical approach. We present a 63-year-old man with bilateral Raynaud's phenomenon secondary to hypothenar hammer syndrome successfully treated by vascular repair surgery. In patients with Raynaud's phenomenon, it is important to know that there are reversible causes such as hypothenar hammer syndrome.
Purpose: To test the performance of different algorithms that can be used in interlaboratory comparisons based on dicentric chromosome analysis, and to evaluate the impact of considering a priori values different to calculate individual laboratory performance based on the ionizing radiation dose estimation. Methods: Mean and standard deviation estimations in inter-laboratory comparisons are tested on simulated data and data from previously published inter-laboratory comparisons using three robust algorithms, algorithm A, Algorithm B and Q/Hampel, all programmed in R-project language and implemented in a Shiny application. The simulated data were generated assuming three different probabilities to contaminate inter-laboratory comparisons samples with atypical dose values. Comparison between different algorithms was also done using published exercises where blood samples were irradiated at 0 and 0.7 Gy that represent a challenge for the assessment of an inter-laboratory comparison. Results: The best performance was obtained with the Q/Hampel algorithm for the estimation of the dose mean and with the algorithm B for the estimation of the dose standard deviation under the conditions tested in the simulations. The Q/Hampel algorithm showed the best performance when non-irradiated samples were evaluated and there was a high proportion of identical values. The presence identical values causes the Algorithm B to fail. Real examples illustrating the need to consider standard deviation priors, and the need to use algorithms resistant to a high proportion of identical values are presented. Conclusions: Q/Hampel algorithm is a serious candidate to estimate the dose mean in the inter-laboratory comparisons, and to estimate both parameters when the proportion of identical values equals or higher than the half of the results. When the proportion of identical values is less than the half of the results, the Algorithm B should be considered as a candidate to estimate the standard deviation in the inter-laboratory comparisons with small number of laboratories. We remark that special attention is needed to establish prior definitions of standard deviation in the assessment of inter-laboratory dicentric assay comparisons.
Chiral bicyclic N,O-acetal isoserine derivatives have been synthesized by an acid-catalyzed tandem N,O-acetalization/intramolecular transcarbamoylation reaction between conveniently protected l-isoserine and 2,2,3,3-tetramethoxybutane. The delicate balance of the steric interactions between the different functional groups on each possible diastereoisomer controls their thermodynamic stability and hence the experimental product distribution. These chiral isoserine derivatives undergo diastereoselective alkylation at the α position, proceeding with either retention or inversion of the configuration depending on the relative configuration of the stereocenters. Quantum mechanical calculations revealed that a concave-face alkylation is favored due to smaller torsional and steric interactions at the bicyclic scaffold. This synthetic methodology gives access to chiral β2,2-amino acids, attractive compounds bearing a quaternary stereocenter at the α position with applications in peptidomimetic and medicinal chemistry. Thus, enantiopure α-alkylisoserine derivatives were produced upon acidic hydrolysis of these alkylated scaffolds. In addition, α-benzylisoserine was readily transformed into a five-membered ring cyclic sulfamidate, which was ring opened regioselectively with representative nucleophiles to yield other types of enantiopure β2,2-amino acids such as α-benzyl-α-heterofunctionalized-β-alanines and α-benzylnorlanthionine derivatives.
Automatic yield monitoring and in-field robotic harvesting by low-cost cameras require object detection and segmentation solutions to tackle the poor quality of natural images and the lack of exactly-labeled datasets of consistent sizes. This work proposed the application of deep learning for semantic segmentation of natural images acquired by a low-cost RGB-D camera in a commercial vineyard. Several deep architectures were trained and compared on 85 labeled images. Three semi-supervised learning methods (PseudoLabeling, Distillation and Model Distillation) were proposed to take advantage of 320 non-annotated images. In these experiments, the DeepLabV3+ architecture with a ResNext50 backbone, trained with the set of labeled images, achieved the best overall accuracy of 84.78%. In contrast, the Manet architecture combined with the EfficientnetB3 backbone reached the highest accuracy for the bunch class (85.69%). The application of semi-supervised learning methods boosted the segmentation accuracy between 5.62 and 6.01%, on average. Further discussions are presented to show the effects of a fine-grained manual image annotation on the accuracy of the proposed methods and to compare time requirements.
Severe traumatic injury is one of the main global health issues which annually causes more than 5.8 million worldwide deaths. Uncontrolled haemorrhage is the main avoidable cause of death among severely injured individuals. Management of trauma patients is the greatest challenge in trauma emergency care, and its proper diagnosis and early management of bleeding trauma patients, including blood transfusion, are critical for patient outcomes. Aim: We aimed to describe the epidemiology of transfusion practices in severe trauma patients admitted into Spanish Intensive Care Units. Material and methods: We performed a multicenter cross-sectional study in 111 Intensive Care Units across Spain. Adult patients with moderate or severe trauma were eligible. Distribution of frequencies was used for qualitative variables and the mean, with its 95% CI, for quantitative variables. Transfusion programmes, the number of transfusions performed, and the blood component transfused were recorded. Demographic variables, mortality rate, hospital stay, SOFA-score and haemoglobin levels were also gathered. Results: We obtained results from 109 patients. The most transfused blood component was packet red blood cells with 93.8% of total transfusions versus 43.8% of platelets and 37.5% of fresh plasma. The main criteria for transfusion were analytical criteria (43.75%), and acute anaemia with shock (18.75%) and without haemodynamic impact (18.75%). Conclusion: Clinical practice shows a ratio of red blood cells, platelets, and Fresh Frozen Plasma (FFP) of 2:1:1. It is necessary to implement Massive Transfusion Protocols as they appear to improve outcomes. Our study suggests that transfusion of RBC, platelets and FFP in a 2:1:1 ratio could be beneficial for trauma patients.
Two stage grid search accepted as a promising heuristic search technique, involves a search performed in two stages. In the first stage a search is performed in coarse grain/low resolution to identify the optimal region and, in the second stage, a fine grain/high resolution search is performed in the neighborhood of the optimal region to identify the optimal parameters. Performing a search in two stages considerably reduces the computational complexity when compared to the basic grid search algorithm. However, an exhaustive search is to be carried out in the subspace during the second stage which may again be a computationally expensive task. The main contribution of this paper is to develop a new heuristic search technique which explores the discrete parameter space dimension wise recursively. The time complexity of the proposed algorithm is less than that of the two-stage grid search. The performance of the proposed algorithm in terms of required number of probes and time for optimal model selection, compared with the two-stage grid search, is verified for correctness and efficiency.
Purpose: To define a set of proposals that would improve the current management of chronic obstructive pulmonary disease (COPD) within the Spanish National Healthcare System (SNHS) from a comprehensive multidisciplinary perspective and to assess the impact of its implementation from clinical, healthcare, economic, and social perspectives. Patients and methods: A group of 20 stakeholders related to COPD (healthcare professionals, patients, and informal caregivers, among others) participated in an online Delphi process to agree on a set of 15 proposals that would improve the current management of COPD within the SNHS in four areas: diagnosis, risk stratification, management of exacerbations, and management of stable COPD. A one-year forecast-type social return on investment (SROI) analysis was used to estimate the impact that implementing the set of proposals would have in relation to the investment required. A sensitivity analysis was used to test the strength of the model when varying assumption-based data-points. Results: The hypothetical implementation of the complete set of 15 proposals would require a €668 million investment and would generate a €2079 million social impact concerning savings for the SNHS and quality of life improvements for patients and their informal caregivers, among others. Accordingly, for every euro invested in the set of proposals, a social return of €3.11 would be generated (€2.71 in the worst-case scenario and €3.62 in the best-case scenario) of both tangible (32.56%) and intangible nature (67.44%). Conclusion: Altogether, implementing this set of 15 proposals would generate a positive social impact, threefold the required investment. The results may inform decisions relative to healthcare policy and practice regarding COPD management within the SNHS, further contributing to reduce the large burden of COPD.
An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes.
The objective of this study is to analyse the effect of four export barrier groups − human capital, cultural, administrative, and financial − on the product export barrier. The study participants constitute a statistically significant sample of 318 exporting companies in Brazil. The research model is tested using structural equation modelling, specifically the partial least squares (PLS-SEM) technique, and SmartPLS version 3.2.9. The results confirm that there is a significant effect of three export barriers − human capital, cultural, and financial − on the product export barrier. The effect of the administrative barrier on the product barrier is not verified. Besides, the effects of the human capital barrier on the cultural barrier and the administrative barrier on the financial barrier are verified. The mutual interaction between export barriers makes it advisable to manage each type of barrier.
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