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We present a neural process model of visual analogical mapping that receives image inputs and responds by spatially selecting a matching object to a cued object. The relational structure of the base scene is stored in a way that specifies the arguments of each relation, allowing mappings based on structural correspondence to be represented as proposed by the structure-mapping theory. All the processes in the model emerge out of coupled integro-differential equations modeling neural population activation dynamics. The mapping can be influenced by both featural and relational similarities. The developmental shift in mappings in the presence of a featural distractor can be accounted for by manipulating how well the model can maintain attention to relevant feature/relation dimensions, consistent with a hypothesis suggesting inhibitory as a key factor explaining the shift.

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

The purpose of this study is to identify relationships of learners' achievement goal orientation, self-regulation, test-anxiety, self-efficacy, participation, satisfaction, and achievement in online learning environments in Korea. A total of 1,832 student responses from a Korean cyber university were used to find structural relationships of factors. Causal relationships among various variables are provided as results of this study. Achievement goal orientation--approach, self-regulated learning, test-anxiety, and self-efficacy, were positively related to participation; however, achievement goal orientation-avoidance was negatively related to participation. Test-anxiety was directly related to learning achievement and it was found that participation affected learning satisfaction and learning achievement. It was also revealed that learning satisfaction was related to learning achievement. Results of this study suggest that a comprehensive management of learners' psychological variables, such as achievement goal orientation, self-regulation, test-anxiety, self-efficacy for designing, and managing online learning environments is important to online learning organizations, instructors, and administrators for better learner support.

Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4g€¯d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at 10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

The H atom tunneling dissociation dynamics of the S1 state of meta- or para-cresol has been investigated by using the picosecond time-resolved pump-probe spectroscopy in a state-specific manner. The S1 state lifetime (mainly due to the H atom tunneling reaction) is found to be mode-dependent whereas it quickly converges and remains constant as the rapid intramolecular vibrational energy redistribution (IVR) starts to participate in the S1 state relaxation with the increase of the S1 internal energy (Eint). The IVR rate and its change with increasing Eint have been reflected in the parent ion transients taken by tuning the total energy (hνpump + hνprobe) just above the adiabatic ionization threshold (so that the dissipation of the initial mode-character could be monitored as a function of the reaction time), indicating that the mode randomization rate into the S1 isoenergetic manifolds exceeds the tunneling rate quite early in terms of Eint for m-cresol (≤∼1200 cm−1) or p-cresol (≤∼800 cm−1) compared to the case of phenol (≤∼1800 cm−1). Though the H atom tunneling dynamics of phenol (S1) seems to be little influenced by the methyl substitution on the either m- or p-position, the IVR rate has been found to be strongly accelerated due to the sharply-increasing (S1) density of states with increasing Eint due to the pivotal role of the low-frequency CH3 torsional mode. [ABSTRACT FROM AUTHOR]

We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements.

This study examines relationships of instructional environments, learner traits, and learning outcomes in the context of an online university course in Korea which has an advanced information technology background and rich e-learning experiences. However, the educational heritage of the country adheres to directive instruction with little interaction in the classroom. Based on the literature review, specific research variables are as follows: the environmental variables include learner-learner interaction, learner-instructor interaction, and content/system quality. Regarding learner traits, intrinsic/extrinsic motivation and computer/academic self-efficacy were investigated. Academic achievement and class satisfaction were identified as potential determinants of online learning outcomes. A total of 937 valid responses from online university students were used to establish structural relationships among the variables. Most of the structural associations among the factors were significantly positive, although some variables reflected Korean cultural and educational contexts specifically. The findings suggest a need for a synthetic approach towards e-learning and that further research should be conducted concerning context-specific variables.

This study investigates online students' acceptance of mobile learning and its influence on learning achievement using an information system success and extended technology acceptance model (TAM). Structural equation modeling was used to test the structure of individual, social, and systemic factors influencing mobile learning's acceptance, and how said acceptance influences learning satisfaction and achievement. Unlike earlier TAM-related research that did not provide a broad view of technological acceptance and its impact on learning activities, the present study's results highlight the relationship between behavioral intention/learning satisfaction and learning achievement. Additionally, this study tests the theoretical model of successful mobile learning by empirically accepting mobile learning management systems. The findings further imply that students at online universities have started to accept mobile technology as a new learning tool; consequently, its acceptance has influenced their learning achievement both directly and indirectly. These discoveries should facilitate a better understanding of students' usage of mobile learning systems in higher education, and provide timely guidance for its development and implementation.

Background: Machine perfusion is an organ preservation strategy used to improve function over simple storage in a cold environment. This article presents an updated systematic review and meta-analysis of machine perfusion in deceased donor kidneys. Methods: RCTs from November 2018 to July 2023 comparing machine perfusion versus static cold storage in kidney transplantation were evaluated for systematic review. The primary outcome in meta-analysis was delayed graft function. Results: A total 19 studies were included, and 16 comparing hypothermic machine perfusion with static cold storage were analysed. The risk of delayed graft function was lower with hypothermic machine perfusion (risk ratio (RR) 0.77, 95% c.i. 0.69 to 0.86), even in kidneys after circulatory death (RR 0.78, 0.68 to 0.90) or brain death (RR 0.73, 0.63 to 0.84). Full hypothermic machine perfusion decreased the risk of delayed graft function (RR 0.69, 0.60 to 0.79), whereas partial hypothermic machine perfusion did not (RR 0.92, 0.69 to 1.22). Normothermic machine perfusion or short-term oxygenated hypothermic machine perfusion preservation after static cold storage was equivalent to static cold storage in terms of delayed graft function and 1-year graft survival. Conclusion: Hypothermic machine perfusion reduces delayed graft function risks and normothermic approaches show promise. This study is an extension of the study of Tingle et al. (2019), confirming previous findings with a larger number of participants. In kidney transplantation, hypothermic machine perfusion (HMP) reduces the incidence of delayed graft function, irrespective of donor type, cold ischaemia time, or machine type. Further investigation is needed to understand the impact of short-term HMP. The evidence for a more practical and novel method of perfusing deceased donor kidneys is now being investigated in clinical trials. [ABSTRACT FROM AUTHOR]