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•Transcranial photobiomodulation (tPBM) has the potential to improve working memory in healthy older adults.•The mechanism of improving working memory by tPBM may be through alterations of the resting-state functional brain network properties.•The altered working memory were positively correlated with the changes of functional connectivity and nodal efficiency mainly in the left prefrontal cortex.•The tPBM may serve as an effective and non-invasive neurostimulation technique. Transcranial photobiomodulation (tPBM), as a novel non-invasive neurostimulation technique, has shown the compelling potential for improving cognitive function in aging population. However, the potential mechanism remains unclear. Neuroimaging studies have found that tPBM-induced physiological changes exist in both targeted and non-targeted brain areas, suggesting the necessity of understanding the modulation mechanism from the perspective of the whole brain level. This randomized, single-blind, sham-controlled crossover study aimed to investigate the hypothesis that tPBM improved working memory in healthy older adults through the mechanism of optimizing the properties of the resting-state functional brain networks. A total of 55 right-handed healthy older adults were randomly assigned to sham tPBM session group or active tPBM session group. After a washout interval, they were assigned to the opposite intervention session. Each session included the following: active or sham tPBM application with a 1064-nm laser to the left forehead; before and after, resting-state functional near-infrared spectroscopy (fNIRS) measurements; and the digital n-back task. Differences in accuracy and reaction time of the n-back task, and changes in functional connectivity and graph metrics of the brain networks were investigated and compared between the active and sham tPBM sessions. In addition, correlations between tPBM-induced changes in functional brain networks, and the n-back task were examined. The results showed that compared with the sham tPBM session, the accuracy and reaction time during 3-back task significantly improved in the active tPBM session. In addition, the global efficiency, local efficiency, nodal efficiency, and functional connectivity significantly increased in the active tPBM session, particularly in the frontoparietal areas. Importantly, the altered 3-back accuracy was positively correlated with the changes of functional connectivity and nodal efficiency mainly in left prefrontal cortex in those who had increased 3-back accuracy in the active tPBM session. This study suggests that tPBM may serve as an effective tool to improve working memory in older adults through the modulation of resting-state functional brain network properties. Investigations in large-scale samples are needed to further validate the findings of this study.

The Western Tibetan Vortex (WTV) is a large-scale circulation pattern identified from year-to-year circulation variability, which was used to understand the causal mechanisms for slowdown of the glacier melting over the western Tibetan Plateau (TP). A recent argument has suggested the WTV is the set of wind field anomalies resulting from variability in near-surface air temperatures over the western TP (above 1500 m), which, in turn, is likely driven by the surface net radiation. This study thereby evaluates the above putative thermal-direct mechanism. By conducting numerical sensitivity experiments using a global atmospheric circulation model, SAMIL, we find a WTV-like structure cannot be generated from a surface thermal forcing imposed on the western TP. A thermally-direct circulation generated by the surface or near surface heating is expect to cause upward motions and a baroclinic structure above it. In contrast, downward motions and a quasi-barotropic are observed in the vertical structure of the WTV. Besides, we find variability of the surface net radiation (sum of the surface shortwave and longwave net radiation) over the western TP can be traced back to the WTV variability based on ERA5 data. The anticyclonic (cyclonic) WTV reduces (increases) the cloudiness through the anomalous downward (upward) motions, causes more (less) input shortwave net radiation and thereby more (less) surface net radiations, resulting in the warmer (cooler) surface and near-surface air temperature over the western TP. The argument is constructive in encouraging examination of the radiative balance processes that complements previous studies.

Accurate measurements of the three-dimensional structure characteristics of urban buildings and their greenhouse effect are important for evaluating the impact of urbanization on the radiation energy budget and research on the urban heat island (UHI) effect. The decrease in evapotranspiration or the increase in sensible heat caused by urbanization is considered to be the main cause of the UHI effect, but little is known about the influence of the main factor “net radiant flux” of the urban surface heat balance. In this study, experimental observation and quantitative model simulation were used to find that with the increase of building surface area after urbanization, the direct solar radiation flux and net radiation flux on building surface areas changed significantly. In order to accurately quantify the relationship between the positive and negative effects, this study puts forward the equivalent calculation principle of “aggregation element”, which is composed of a building’s sunny face and its shadow face, and the algorithm of the contribution of the area to thermal effect. This research clarifies the greenhouse effect of a building with walls of glass windows. Research shows that when the difference between absorption rates of a concrete wall and grass is −0.21, the cooling effect is shown. In the case of concrete walls with glass windows, the difference between absorption rates of a building wall and grass is −0.11, which is also a cooling effect. The greenhouse effect value of a building with glass windows reduces the cooling effect value to 56% of the effect of a building with concrete walls. The simulation of changes in net radiant flux and flux density shows that the greenhouse effect of a 5-story building with windows yields 15.5% less cooling effect than one with concrete walls, and a 30-story building with windows reduces the cooling effect by 23.0%. The simulation results confirmed that the difference in the equivalent absorption rate of the aggregation element is the “director” of cooling and heating effects, and the area of the aggregation element is the “amplifier” of cooling and heating effects. At the same time, the simulation results prove the greenhouse effect of glass windows, which significantly reduces the cold effect of concrete wall buildings. The model reveals the real contribution of optimized urban design to mitigating UHI and building a comfortable environment where there is no atmospheric circulation.

What are the main findings? * This study developed an optimized deep learning framework for daily all-wave net radiation estimation over the Tibetan Plateau, with Xception achieving optimal balance between daily prediction accuracy (R[sup.2] > 0.94), computational efficiency, and physical interpretability under all-weather conditions. * The framework demonstrated superior performance in daily monitoring, outperforming established products particularly in complex terrain while effectively resolving fine-scale spatial heterogeneity. This study developed an optimized deep learning framework for daily all-wave net radiation estimation over the Tibetan Plateau, with Xception achieving optimal balance between daily prediction accuracy (R[sup.2] > 0.94), computational efficiency, and physical interpretability under all-weather conditions. The framework demonstrated superior performance in daily monitoring, outperforming established products particularly in complex terrain while effectively resolving fine-scale spatial heterogeneity. What is the implication of the main findings? * SHAP analysis confirms physical consistency with dominant astronomical/topographic drivers governing daily variations. * The study identified key pathways for operational daily radiation monitoring, emphasizing automated preprocessing of multi-source data, enhanced sub-diurnal dynamics capture, and physical constraint integration to advance reliable long-term daily product generation. SHAP analysis confirms physical consistency with dominant astronomical/topographic drivers governing daily variations. The study identified key pathways for operational daily radiation monitoring, emphasizing automated preprocessing of multi-source data, enhanced sub-diurnal dynamics capture, and physical constraint integration to advance reliable long-term daily product generation. Accurate daily surface net radiation (R[sub.n]) estimation over the Tibetan Plateau’s complex and highly heterogeneous terrain is essential for advancing the understanding of land–atmosphere exchanges and regional climate processes. This study developed an optimized deep learning framework that systematically evaluates 19 CNN architectures using a per-pixel multivariate regression design (1 × 1 × 21). The channel-rich representation incorporates engineered neighborhood descriptors to statistically embed spatial context while fully avoiding the mosaic and boundary artifacts common in patch-based approaches. Among all tested networks, Xception delivered the best combination of accuracy (R[sup.2] > 0.94), computational efficiency, and physical consistency. Its depthwise separable convolutions and skip connections enable hierarchical nonlinear cross-channel feature learning, effectively capturing the complex dependencies between surface variables and R[sub.n]. Independent validation confirmed stable performance under diverse weather conditions and substantially better skill than GLASS, especially across rugged terrain and high-albedo surfaces. SHAP analysis further highlights physically meaningful behavior, with astronomical and topographic factors contributing ~70% and surface properties ~25% to predictions. Remaining challenges include dependence on continuous high-quality multi-source inputs and scale effects from mixed pixels. Future work will enhance operational deployment through automated daily preprocessing, improved sub-diurnal characterization via multi-scale data fusion, and stronger physical constraints to increase reliability.

Surface all-wave net radiation (Rn) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of Rn from satellite data is gaining more and more attention. In order to obtain the daily Rn (Rnd) from the instantaneous satellite observations, a parameter Cd, which is defined as the ratio between the Rn at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for Cd estimation, namely New Model, was proposed based on the comprehensive clear-sky Rn measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate Cd at any moment during 9:30~14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the Rnd obtained by multiplying Cd from New Model had the best accuracy, yielding an overall R2 of 0.95, root mean square error (RMSE) of 14.07 Wm−2, and Bias of −0.21 Wm−2. Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model Cd estimate was applied with MODIS data to calculate Rnd. After validation, the Rnd computed from Cd was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with Rnd-validated R2 of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm−2, and Biases of −0.76 and 8.88 Wm−2. Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new Cd  model only works for clear-sky, it has the strong potential to be used in estimating Rnd from satellite data, especially for those having fine spatial resolution but low temporal resolution.

Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, although it can cause adverse effects. Recent data suggest that brain RT is associated with dose-dependent cortical atrophy, which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces were parcellated into 68 regions and correlation matrices were created for patients pre- and post-RT. Significant changes in graph network properties were tested using nonparametric permutation tests. Linear regressions were conducted to measure the association between dose and changes in nodal network connectivity. Increases in transitivity, modularity, and global efficiency (n = 54, p < 0.0001) were all observed in patients post-RT. Decreases in local efficiency (n = 54, p = 0.007) and clustering coefficient (n = 54, p = 0.005) were seen in regions receiving higher RT doses, including the inferior parietal lobule and rostral anterior cingulate. These findings demonstrate alterations in global and local network topology following RT, characterized by increased segregation of brain regions critical to cognition. These pathological network changes may contribute to the late delayed cognitive impairments observed in many patients following brain RT.

Surface shortwave net radiation (SSNR) flux is essential for the determination of the radiation energy balance between the atmosphere and the Earth’s surface. The satellite-derived intermediate SSNR data are strongly needed to bridge the gap between existing coarse-resolution SSNR products and point-based measurements. In this study, four different machine learning (ML) algorithms were tested to estimate the SSNR from the Landsat Thematic Mapper (TM)/ Enhanced Thematic Mapper Plus (ETM+) top-of-atmosphere (TOA) reflectance and other ancillary information (i.e., clearness index, water vapor) at instantaneous and daily scales under all sky conditions. The four ML algorithms include the multivariate adaptive regression splines (MARS), backpropagation neural network (BPNN), support vector regression (SVR), and gradient boosting regression tree (GBRT). Collected in-situ measurements were used to train the global model (using all data) and the conditional models (in which all data were divided into subsets and the models were fitted separately). The validation results indicated that the GBRT-based global model (GGM) performs the best at both the instantaneous and daily scales. For example, the GGM based on the TM data yielded a coefficient of determination value (R2) of 0.88 and 0.94, an average root mean square error (RMSE) of 73.23 W∙m-2 (15.09%) and 18.76 W·m-2 (11.2%), and a bias of 0.64 W·m-2 and –1.74 W·m-2 for instantaneous and daily SSNR, respectively. Compared to the Global LAnd Surface Satellite (GLASS) daily SSNR product, the daily TM-SSNR showed a very similar spatial distribution but with more details. Further analysis also demonstrated the robustness of the GGM for various land cover types, elevation, general atmospheric conditions, and seasons

The Global Land Surface Satellite (GLASS) product suite currently contains 12 products, including leaf area index, fraction of absorbed photosynthetically active radiation, fraction of green vegetation coverage, gross primary production, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, downward and upwelling thermal radiation, all-wave net radiation, and evapotranspiration. These products are generated from the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer satellite data. Their unique features include long-term temporal coverage (many from 1981 to the present), high spatial resolutions of the surface radiation products (1 km and 0.05°), spatial continuities without missing pixels, and high quality and accuracy based on extensive validation using in situ measurements and intercomparisons with other existing satellite products. Moreover, the GLASS products are based on robust algorithms that have been published in peer-reviewed literature. Herein, we provide an overview of the algorithm development, product characteristics, and some preliminary applications of these products. We also describe the next steps, such as improving the existing GLASS products, generating more climate data records (CDRs), broadening product dissemination, and fostering their wider utilization. The GLASS products are freely available to the public.

Surface longwave net radiation (LWNR) is a vital component in the surface radiation budget. Major progress has been made in the estimations of clear-sky LWNR. However, the estimation of cloudy-sky LWNR remains a significant challenge. In this paper, a linear model (LM) and a multivariate adaptive regression spline (MARS) model were developed to estimate the cloudy-sky LWNR from a satellite-derived surface shortwave net radiation product. Spatially and temporally matched satellite data and ground-measured LWNR, which was collected at 24 sites from four networks, were used to build and validate the linear and MARS models. The effects of land cover, climate type, and surface elevation on the estimate of LWNR were also analyzed. The MARS model, incorporating the normalized difference vegetation index (NDVI) and surface elevation (H) as the inputs, had the best performance. The determination coefficient, BIAS, and root mean square error (RMSE) were 0.51, 0.01 W/m2, and 26.10 W/m2, respectively. The developed model, when combined with freely distributed Global LAnd Surface Satellite (GLASS) products, showed promise for producing surface LWNR and all-sky surface net radiation.