Paisajes del alma de Unamuno: propuesta de traducción

Programa de Doctorado en Humanidades por la Universidad Carlos III de MadridPresidente: José Luis Herrero Ingelmo.- Secretario: Eduardo Pérez-Rasilla Bayo.- Vocal: Chang Shir

Synthesis of Itaconic Acid Ester Analogues via Self-Aldol Condensation of Ethyl Pyruvate Catalyzed by Hafnium BEA Zeolites

Lewis acidic zeolites are used to synthesize unsaturated dicarboxylic acid esters via aldol condensation of keto esters. Hafnium-containing BEA (Hf-BEA) zeolites catalyze the condensation of ethyl pyruvate into diethyl 2-methyl-4-oxopent-2-enedioate and diethyl 2-methylene-4-oxopentanedioate (an itaconic acid ester analogue) with a selectivity of ca. 80% at ca. 60% conversion in a packed-bed reactor. The catalyst is stable for 132 h on stream, reaching a turnover number of 5110 mol[subscript EP] mol[subscript Hf]⁻¹. Analysis of the dynamic behavior of Hf-BEA under flow conditions and studies with Na-exchanged zeolites suggest that Hf(IV) open sites possess dual functionality for Lewis and Brønsted acid catalysis.United States. Department of Energy (DE-FG0212ER16352)National Science Foundation (U.S.) (122374

The Excitation of Guided-waves by Underground Point Source: an Investigation with Theoretical Seismograms

AbstractNear-Source scattering of Rg into S appears to be the primary contributor to the low-frequency Lg. The authors further suggest that the prominent low-frequency spectral null in Lg is due to Rg from a compensated linear vector dipole (CLVD) source, and the low-frequency null in Rg excitation is due to a zero-crossing of the horizontal displacement eigenfunctions. In this study, the mechanism of the excitation of Lg from explosions in layered earth structures are analyzed with theoretical seismograms. Our result shows that the CLVD source generates prominent Lg waves,and the null in the Lg spectra showing remarkably good agreement with those expected from Rg due to a CLVD source. We conclude that the derivative of displacement eigenfunction also takes a key role in the excitation of the null, only zero-crossing of the horizantall displacement eigenfunction can not fully explain it

Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation

Contemporary domain adaptation offers a practical solution for achieving cross-domain transfer of semantic segmentation between labeled source data and unlabeled target data. These solutions have gained significant popularity; however, they require the model to be retrained when the test environment changes. This can result in unbearable costs in certain applications due to the time-consuming training process and concerns regarding data privacy. One-shot domain adaptation methods attempt to overcome these challenges by transferring the pre-trained source model to the target domain using only one target data. Despite this, the referring style transfer module still faces issues with computation cost and over-fitting problems. To address this problem, we propose a novel framework called Informative Data Mining (IDM) that enables efficient one-shot domain adaptation for semantic segmentation. Specifically, IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. We then perform a model adaptation method using these selected samples, which includes patch-wise mixing and prototype-based information maximization to update the model. This approach effectively enhances adaptation and mitigates the overfitting problem. In general, we provide empirical evidence of the effectiveness and efficiency of IDM. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7\%/55.4\% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively. The code will be released at \url{https://github.com/yxiwang/IDM}.Comment: Accepted by ICCV 202

Low-molecular-weight heparin in addition to low-dose aspirin for preventing preeclampsia and its complications: A systematic review and meta-analysis

BackgroundIn this systematic review, we aimed to investigate the efficacy and safety of adding low-molecular-weight heparin (LMWH) or unfractionated heparin to low-dose aspirin (LDA) started ≤16 weeks'gestation in the prevention of preeclampsia (PE) in high-risk women.MethodsPubMed, Cochrane Library, Embase, and ClinicalTrials.gov databases were searched from their inception to April 2022 for randomized controlled trials (RCTs) that to determine whether the combined treatment of LMWH and LDA is better than single anticoagulant drugs in preventing PE and improving live birth rate of fetus in high-risk women with pregnancy ≤16 weeks. We also searched Embase, OVID MEDLINE and OVID MEDLINE in-process using the OVID platform.Results14 RCTs involving 1,966 women were found. The LMWH (or unfractionated heparin) and LDA groups included 1,165 wemen, and the LDA group included 960 women. The meta-analysis showed that the addition of LMWH to LDA reduced the risk of PE (RR: 0.59, 95% CI: 0.44-0.79, P < 0.05), small-for-gestational age (SGA, RR: 0.71, 95% CI: 0.52-0.97, P = 0.03), fetal and neonatal death (RR: 0.45, 95% CI: 0.23-0.88, P = 0.02) and gestational hypertension (RR: 0.47, 95% CI: 0.25-0.90, P = 0.02). It is worth emphasizing that LMWH (or unfractionated heparin) combined with LDA did not increase the risk of bleeding.ConclusionsLMWH combined with LDA can effectively improve the pregnancy outcome of women with high risk factors for PE and its complications. Although this study showed that combined medication also did not increase the risk of bleeding, but such results lack the support of large sample size studies. The clinical safety analysis of LMWH combined with LDA in patients with PE should be more carried out

High Relaxivity Gadolinium-Polydopamine Nanoparticles

AbstractThis study reports the preparation of a series of gadolinium‐polydopamine nanoparticles (GdPD‐NPs) with tunable metal loadings. GdPD‐NPs are analyzed by nuclear magnetic relaxation dispersion and with a 7‐tesla (T) magnetic resonance imaging (MRI) scanner. A relaxivity of 75 and 10.3 mM−1 s−1 at 1.4 and 7 T is observed, respectively. Furthermore, superconducting quantum interference device magnetometry is used to study intraparticle magnetic interactions and determine the GdPD‐NPs consist of isolated metal ions even at maximum metal loadings. From these data, it is concluded that the observed high relaxivities arise from a high hydration state of the Gd(III) at the particle surface, fast rate of water exchange, and negligible antiferromagnetic coupling between Gd(III) centers throughout the particles. This study highlights design parameters and a robust synthetic approach that aid in the development of this scaffold for T1‐weighted, high relaxivity MRI contrast agents

YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor

IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea