6 research outputs found
Analysis of local microstructure and hardness of 13mm gauge 2024-T351 AA friction stir welds
No full textThe friction stir welding process has been used to join 13mm gauge 2024-T351 aluminium alloy plates together. A detailed microstructural study of the resulting weld was carried out using Differential Scanning Calorimetry (DSC), hardness testing, Scanning Electron Microscopy (SEM) and Electron Backscatter Diffraction (EBSD). DSC was used to explain the hardness results at a number of regions across the weld in terms of co-cluster dissolution and reformation and S phase formation, coarsening and dissolution. The “onion rings” structure found in the nugget weld was shown to be the result of a combination of the slight grain size variations and a change in nature and size of the particles present (i.e. intragranular versus intergranular). The variation in corrosion properties and hardness of the rings is discussed in terms of the local microstructure and quench sensitivities
Spaces of vector-valued continuous functions with the Dunford-Pettis property. (Spanish: Espacios de funciones continuas vectoriales con la propiedad de Dunford-Pettis).
No full textA Banach space E has the Dunford-Pettis property (D.P.P.) if for every pair of sequences(x n ) in E and (x ′n ) in E ′, both weakly convergent to zero, we have that (x′n (x n )) tends to zero. P. Cembranos [Bull. Austral. Math. Soc. 28 (1983), no. 2, 175–186;] has proved that, if K is a compact Hausdorff dispersed space, then the following holds: For every Banach E with the D.P.P., the Banach space C(K,E) of continuous functions from K into E has the D.P.P.
In the note under review the author proves that this property characterizes compact dispersed spaces.Depto. de Análisis Matemático y Matemática AplicadaFac. de Ciencias MatemáticasTRUEpu
Agreement of Tear Break-Up Time and Meniscus Height between Medmont E300 and Visionix VX120+
No full textThe goal of this study was to analyze the agreement between the Medmont E300 and the Visionix VX120+ systems in terms of non-invasive tear break-up time (NIBUT) and tear meniscus height (TMH) measurements. A total of 60 eyes (30 healthy subjects) were enrolled. NIBUT and TMH were evaluated with Medmont E300; first NIBUT, NIBUT50%, and TMH were evaluated with Visionix VX120+. Both evaluations were performed in a random order by the same clinician for right, left, and both eyes. The Medmont E300 provided significantly higher NIBUT than Visionix VX120+ for first NIBUT in right, left, and both eyes (p ≤ 0.003) and NIBUT50% in left and both eyes (p ≤ 0.042). The TMH measured with VX120+ was significantly higher than with Medmont E300 considering both eyes (p = 0.037). No significant correlations were found between both devices for either NIBUT (p ≥ 0.11) or TMH (p ≥ 0.09). Passing–Bablok regression analyses revealed poor agreement between devices for NIBUT and TMH outcomes. VX120+ is expected to provide substantial lower first NIBUT values than the NIBUT measured by Medmont E300. Clinicians should consider not using both instruments as interchangeable for dry eye diagnosis.The author D.P.P. has been supported by the Ministry of Economy, Industry and Competitiveness of Spain within the program Ramón y Cajal, RYC-2016-20471. This research received no external funding. E.M.P. has been supported by European Union-NextGenerationEU
Clinical Characterization of Oculomotricity in Children with and without Specific Learning Disorders
No full textChildren with specific learning disorders have been associated with oculomotor problems, with their analysis even suggested to be a potential diagnostic tool. A prospective non-randomized comparative study evaluating 59 children (6–13 years old) divided into three groups was conducted: a control group (CG) including 15 healthy emmetropic children; a group of 18 healthy children with oculomotor abnormalities (OAG); and a group of 26 children diagnosed with specific learning disorders (LDG). In all groups, besides a complete eye exam, oculomotricity was characterized with two clinical tests: Northeastern State University College of Optometry’s Oculomotor (NSUCO) and Developmental Eye Movement (DEM) tests. Concerning the NSUCO test, lower ability, precision, and head/body movement associated scorings were obtained for both smooth pursuits and saccades in OAG and LDG when compared to the CG (p < 0.001). Likewise, significantly longer time needed to read the horizontal sheet of the DEM test and a higher DEM ratio were found in OAG and LDG compared to CG (p ≤ 0.003). No differences between LDG and OAG were found in the performance with the two oculomotor tests (p ≥ 0.141). Oculomotor anomalies can be present in children with and without specific learning disorders, and therefore cannot be used as diagnostic criteria of these type of disorders.The author D.P.P. has been funded by the Ministry of Economy, Industry and Competitiveness of Spain within the program Ramón y Cajal, RYC-2016-20471
Stimuli Characteristics and Psychophysical Requirements for Visual Training in Amblyopia: A Narrative Review
No full textActive vision therapy using perceptual learning and/or dichoptic or binocular environments has shown its potential effectiveness in amblyopia, but some doubts remain about the type of stimuli and the mode and sequence of presentation that should be used. A search was performed in PubMed, obtaining 143 articles with information related to the stimuli used in amblyopia rehabilitation, as well as to the neural mechanisms implied in such therapeutic process. Visual deficits in amblyopia and their neural mechanisms associated are revised, including visual acuity loss, contrast sensitivity reduction and stereopsis impairment. Likewise, the most appropriate stimuli according to the literature that should be used for an efficient rehabilitation of the amblyopic eye are described in detail, including optotypes, Gabor’s patches, random-dot stimuli and Vernier’s stimuli. Finally, the properties of these stimuli that can be modified during the visual training are discussed, as well as the psychophysical method of their presentation and the type of environment used (perceptual learning, dichoptic stimulation or virtual reality). Vision therapy using all these revised concepts can be an effective option for treating amblyopia or accelerating the treatment period when combining with patching. It is essential to adapt the stimuli to the patient’s individual features in both monocular and binocular training.The authors C.J.H.-R., D.P.P., A.M.-M., D.d.F., L.L.-V., M.B.C.-M. have been funded by CDTI (Centro para el Desarrollo Tecnológico Industrial, Ministry of Economy and Competitiveness of Spain) and FEDER (Fondos Europeos de Desarrollo Regional) funds by means of the program PID (“Proyectos de Investigación y Desarrollo”) in the context of the Project NEIVATECH (“Neuroplasticity through virtual reality for amblyopia”, application number 111705). The author León Morales-Quezada is supported by funding from the Spaulding Research Catalyst award. The author David P Piñero has been also supported by the Ministry of Economy, Industry and Competitiveness of Spain within the program Ramón y Cajal, RYC-2016-20471
Machine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes
No full textThis research explores the use of machine learning to predict the mechanical properties of cementitious materials enhanced with carbon nanotubes (CNTs). Specifically, the study focuses on estimating the elastic modulus and flexural strength of these novel composite materials, with the potential to significantly impact the construction industry. Seven key variables were analyzed including water-to-cement ratio, sand-to-cement ratio, curing age, CNT aspect ratio, CNT content, surfactant-to-CNT ratio, and sonication time. Artificial neural network, support vector regression, and histogram gradient boosting, were used to predict these mechanical properties. Furthermore, a user-friendly formula was extracted from the neural network model. Each model performance was evaluated, revealing the neural network to be the most effective for predicting the elastic modulus. However, the histogram gradient boosting model outperformed all others in predicting flexural strength. These findings highlight the effectiveness of the employed techniques, in accurately predicting the properties of CNT-enhanced cementitious materials. Additionally, extracting formulas from the neural network provides valuable insights into the interplay between input parameters and mechanical properties. © 2024 The Author
