Este estudo verificou a capacidade de estimativa numérica de alunos de 3° e 4° anos do Ensino Fundamental de duas escolas públicas de Porto Alegre. No total, 143 crianças entre 8 e 11 anos de idade, ...foram avaliadas em duas tarefas de estimativa na reta numérica numa escala de 0 a 100: a número-posição e a posição-número. Os resultados indicaram uma representação linear das precisões dos participantes nos dois tipos de tarefa. Também mostraram que o desempenho do 4° ano foi superior ao do 3° ano e que a representação numérica apresenta um comportamento mais linear no 4° do que no 3° ano. De maneira geral, os estudantes apresentaram precisões mais aguçadas na tarefa posição-número, porém houve concordância entre as estimativas nos dois tipos de tarefa, indicando uma associação direta entre as duas tarefas de estimativa na reta numérica.
Drug-target binding affinity (DTA) prediction has drawn increasing interest due to its substantial position in the drug discovery process. The development of new drugs is costly, time-consuming, and ...often accompanied by safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. Therefore, it is of great significance to develop effective computational methods to predict DTAs. The attention mechanisms allow the computational method to focus on the most relevant parts of the input and have been proven to be useful for various tasks. In this study, we proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. For the representation of drugs, we use Bi-directional Gated Recurrent Units (BiGRU) to extract the SMILES representation from SMILES sequences, and graph neural networks to extract the graph representation of the molecular graphs. Then we utilize an attention mechanism to fuse the two representations of the drug. For the target/protein, we utilized an efficient transformer to learn the representation of the protein, which can capture the long-distance relationships in the sequence of amino acids. We conduct extensive experiments to compare our model with state-of-the-art models. Experimental results show that our model outperforms the current state-of-the-art methods on two independent datasets.
We proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. Experimental results show that our model outperforms the state-of-the-art methods on two independent datasets.
Mathematical representation is one of the mathematical recognition’s form. This study aimed to compare the ability of students' mathematical symbol recognition between learning using Student Activity ...Sheets characterized by mathematical representation and learning using group presentation assignment activities. The research method employed Posttest-Only Control Group Design, between the control class and the experimental class. The control class is a class with group presentation learning assignments while the experimental class is a class with learning that uses MFIs characterized by mathematical representations. The data were obtained by providing tests to measure students' mathematical recognition abilities. The data were analyzed by using the t-test. The results of calculations with the help of SPSS showed that data from both classes are normally distributed and homogeneous. The results of data analysis conducted by t-test showed sig. ≥0.05 so H1 is rejected and H0 is accepted which means that there was no difference in the ability to recognize mathematical symbols between the control class and the experimental class. There was no difference in recognition abilities between the control class and the experimental class because learning activities in both classes are student-centered learning.
We explore the representation of the Atlantic Meridional Overturning Circulation (AMOC) in 27 models from the CMIP6 multimodel ensemble. Comparison with RAPID and SAMBA observations suggests that the ...ensemble mean represents the AMOC strength and vertical profile reasonably well. Linear trends over the entire historical period (1850–2014) are generally neutral, but many models exhibit an AMOC peak around the 1980s. Ensemble mean AMOC decline in future (SSP) scenarios is stronger in CMIP6 than CMIP5 models. In fact, AMOC decline in CMIP6 is surprisingly insensitive to the scenario at least up to 2060. We find an emergent relationship among a majority of models between AMOC strength and 21st century AMOC decline. Constraining this relationship with RAPID observations suggests that the AMOC might decline between 6 and 8 Sv (34–45%) by 2100. A smaller group of models projects much less AMOC weakening of only up to 30%.
Plain Language Summary
The Atlantic Meridional Overturning Circulation (AMOC) is a circulation pattern in the Atlantic Ocean that is an important component of the climate system, due to its ability to redistribute and sequester heat and carbon. An accurate representation of the AMOC is a critical test for climate models and essential for building confidence in their projections. Here we investigate the AMOC in 27 climate models that contributed simulations to the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that many models reproduce the observed AMOC quite well, but there are still several models in which the AMOC is too weak or too strong. Most models suggest a slight upward trend in the AMOC from 1850 to the 1980s. Simulations representing different scenarios for future socioeconomic development suggest a stronger AMOC decline compared to previous assessments. Using direct measurements of the AMOC since 2004 and an emerging across‐model relationship between AMOC decline in the 21st century and their present‐day mean state, we find that the majority of CMIP6 models point to an end of century AMOC weakening of 34–45% of its present‐day strength. A smaller group of models projects much less weakening of only up to 30% of its present state.
Key Points
AMOC mean strength is well reproduced by the CMIP6 multimodel mean, but large model spread persists
Projected AMOC decline by the end of the 21st century shows weak dependence on the SSP scenarios
An emergent constraint between AMOC strength and projected decline suggests possible AMOC decline between 34% and 45% by 2100
The weight modules of the Lie algebra
are well known. In the first part of this paper we deal with a realization of weight modules of
in the space
, where
is the algebra of Laurent polynomials. In ...the second part, we consider the Hom-Lie algebra
of Jackson where
. The q-analogue of the above realization in the space
is considered. We obtain two kinds of q-modules. The regular q-modules which have limits the modules obtained in the classical realization when q goes to 1 . The other q-modules have no limits when q goes to 1 and they are called singular modules.
We show that all groups of a distinguished class of «large» topological groups, that of Roelcke precompact Polish groups, have Kazhdan’s Property (T). This answers a question of Tsankov and ...generalizes previous results by Bekka (for the infinite-dimensional unitary group) and by Evans and Tsankov (for oligomorphic groups). Further examples include the group
Aut
(
μ
)
of measure-preserving transformations of the unit interval and the group
Aut
∗
(
μ
)
of non-singular transformations of the unit interval.
More precisely, we prove that the smallest cocompact normal subgroup
G
∘
of any given non-compact Roelcke precompact Polish group
G
has a free subgroup
F
≤
G
∘
of rank two with the following property: every unitary representation of
G
∘
without invariant unit vectors restricts to a multiple of the left-regular representation of
F
. The proof is model-theoretic and does not rely on results of classification of unitary representations. Its main ingredient is the construction, for any
ℵ
0
-categorical metric structure, of an action of a free group on a system of elementary substructures with suitable independence conditions.
Introduction
Psychosocial risks increase the levels of not-integrated/ambivalent and restricted/disengaged representations during pregnancy, but no study has specifically analysed the impact of the ...COVID-19 pandemic on maternal representation styles.
Objectives
(1) to compare maternal representation styles in primiparous women who became pregnant before and during the COVID-19 pandemic and (2) to analyse the content of representation styles during the COVID-19 pandemic.
Methods
A total of 37 Italian pregnant women were recruited from 2019 to 2021. The sample was divided into two groups: the pre-COVID-19 group (22 women, mean age = 33.14 years; SD = 3.78) and the COVID-19 group (15 women, mean age = 35.9 years; SD = 4.6). Interviews on maternal representations during pregnancy were administered and analysed for style and content. Results: Women during the COVID-19 pandemic reported more restricted/disengaged and less integrated/balanced representation styles than women pre-COVID-19. Content analysis showed that the COVID-19 pandemic led women to focus more on concrete aspects of pregnancy in lieu of emotional aspects, thus leading them to develop more restricted/disengaged representation styles.
Conclusions for practice
In future pandemics pregnant women should be supported in focusing their attention to emotions, sensations and fantasies about themselves as mothers and their children.
Wi-Fi positioning technology has attracted considerable attention in recent decades due to its widespread deployment and cost-effectiveness. The multipath effect can lead to different local ...variations in Wi-Fi signals, diminishing both localization accuracy and robustness. In this article, we present an innovative localization framework that employs multiscale spatial and temporal features for localization, which takes the received signal strength (RSS) sequence as input. First, we propose a multiscale spatial feature extraction network to capture multiple local features by using different convolutional operations. Then, a deep temporal network based on the gated recurrent unit (GRU) is used to explore signal correlations at the temporal level. Finally, a channel-spatial (CS) attention mechanism is applied to discriminate the importance of multiscale spatial and temporal representations. Guided by the acquired attention values, multiple features are fused to generate more discriminative representations for localization. Extensive experiments are conducted to validate the effectiveness of our scheme, and the results demonstrate its superior localization accuracy and robustness compared to other localization approaches.
Accurate information on agricultural field boundaries is important for precision agriculture. Contour detection combining local cues presents a high performance on nature images. Image sparse ...representation describes an image is reconstructed by using as few basic functions as possible. The number of farmland parcel boundaries is small and unbalanced for the whole agricultural fields. It fits the application category of sparse representation. In this research, we investigate an approach based on contour detection and sparse representation for the extraction of farmland parcel boundaries. First, field boundaries have an obvious brightness contrast with the internal parts of the farmland parcels. We capture the cue to describe per pixel. Then, we use efficient sparse coding algorithm to represent every pixel for boundary determination. Experimental results show that the proposed method achieves a sensitivity, specificity, accuracy,
${F_1}$
F
1
and AUC of 0.6089, 0.9055, 0.8865, 0.4073 and 0.7552, respectively. The purpose of this paper is to demonstrate the potential of combining local features with sparse representation for a fast and accurate farmland parcel boundary extraction approach from remote sensing images. Comparison results with existing methods on two datasets demonstrate that the proposed method is able for accurate discrimination of the farmland parcel boundaries.
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