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Solar energy plays an essential role in achieving carbon goals and mitigating climate change. Therefore, solar power plants are rapidly developing in the renewable energy sector. However, many reports of solar power plants are on land, and extremely limited observational research has been conducted on the impacts of fishery complementary photovoltaic power plants (FPVs) on near-surface meteorology and surface energy. This study selected two adjacent eddy covariance observational towers at the fishery complementary photovoltaic power plant (FPV) in Yangzhong, Jiangsu Province, China, to explore this topic. The results indicated that the percent frequency of east wind (<4 m s−1) at 2 m decreased by 25.3% in the FPV site compared with the reference site. The FPV array has not an obvious heating effect on the ambient environment. The average air temperature difference in the two sites at 2 m and 10 m was 0.3 K and 0.1 K, respectively, during the heating period. The net radiation increased by 47.8 W m−2 in the two sites. The sensible heat increased by 7.9 W m−2 due to the heating effect. The latent heat in the two sites is contrary to the sensible heat (−13.0 W m−2). The difference in the water storage heat is 32.18 W m−2, which implies that the water absorbs higher heat in the FPV site than in the reference site. This work illustrated the importance of observational experiments to animate process-based understanding combined with FPV systems and provides a scientific basis for establishing FPV energy balance models based on observations that may be used to reveal the impact of utility-scale FPV deployment on climatic effects. [ABSTRACT FROM AUTHOR]

The rapid development of photovoltaic plays an important role in achieving the carbon-neutral goal. How to improve the conversion efficiency and power generation of solar photovoltaic has always been a focus issue. However, more attention is paid to the impact of photovoltaic panel working temperature on the performance of photovoltaic power generation, and how air temperature affects photovoltaic power generation has been ignored. This paper compared and analyzed the impact of the difference in air temperature between lake and land on the revenue of photovoltaic power generation, and established the functional equation between photovoltaic power generation, air temperature and solar radiation, and revealed the relationship between air temperature, solar radiation, and photovoltaic power generation. [Display omitted] [ABSTRACT FROM AUTHOR]

There has been much debate on whether color categories affect how we perceive color. Recent theories have put emphasis on the role of top-down influence on color perception that the original continuous color space in the visual cortex may be transformed into categorical encoding due to top-down modulation. To test the influence of color categories on color perception, we adopted an RSVP paradigm, where color stimuli were presented at a fast speed of 100 ms per stimulus and were forward and backward masked by the preceding and following stimuli. Moreover, no explicit color naming or categorization was required. In theory, backward masking with such a short interval in a passive viewing task should constrain top-down influence from higher-level brain areas. To measure any potentially subtle differences in brain response elicited by different color categories, we embedded a sensitive frequency-tagging-based EEG paradigm within the RSVP stimuli stream where the oddball color stimuli were encoded with a different frequency from the base color stimuli. We showed that EEG responses to cross-category oddball colors at the frequency where the oddball stimuli were presented was significantly larger than the responses to within-category oddball colors. Our study suggested that the visual cortex can automatically and implicitly encode color categories when color stimuli are presented rapidly. [ABSTRACT FROM AUTHOR]

Rapid progress of solar photovoltaic (PV) technology has caused growing interest in understanding interactions between large scale PV plants and near-surface atmosphere. However few attempts have been made to quantify the impact of PV modules on surface radiative forcing and energy partitioning process. Here, this issue is explored experimentally and analytically in the two adjacent sites located at the PV plant and the natural barren field respectively in Wujiaqu in Xinjiang of China. The results showed that the physical effect of PV panels is not symmetrical in the whole day. During the daytime, compared with the reference site, net shortwave radiative forcing increases 8%, a warming effect on the integrated underlying surface (0.1 K) and a cooling effect (∼-2.6 K) on the ground surface were found in the PV plant. 9.2% of the net radiation (NR) was converted into electric energy (PE), sensible heat flux (H) increased by 30.6% hence resulted in the convection heating effects of 0.64 K and 0.32 K on the near-surface air temperature at the height of 2 m and 10 m respectively, while latent heat flux (LE) and ground surface heat flux (GS) decreased by 49% and 3% respectively related to the reference site. At night, PV panels produce a cooling effect of −0.2 K and −2.3 K on the ground and integrated underlying surface respectively, and less GS is released in the PV plant which contribute to the cooling effects of −0.24 K and −0.08 K on the air temperature at the height of 2 m and 10 m respectively related to the reference site. In the whole day, in the PV plant, H increased by 27.6%, LE and GS decreased by 47.4% and 6.7% respectively, air temperature increased by 0.16 K and 0.1 K at 2 m and 10 m respectively. • The physical effect of PV modules is not symmetrical in the whole day. • Net shortwave radiative forcing induced by PV modules increases 7.2%. • Sensible heat increases 34.4% and latent heat decreased 20.9% in PV plant related to reference site. • Ground surface heat decreases 7.4%. • The reasons of physical effect of PV modules were explained. [ABSTRACT FROM AUTHOR]

• PV plants in China's northwestern Gobi Deserts would favor lower evaporation and wind. • Local climate effects of PV plants are equivalent to or even greater than projected climate variability. • PV-induced climate effects could contribute to improving ecological conditions in Gobi Deserts. In this study, a promising photovoltaic (PV) deployment scenario is firstly designed to represent China's solar energy development in the context of its dual carbon target. Then, the potential climate effects of the designed PV plants under different emission scenarios are simulated using a newly developed weather research and forecasting (WRF)-PV regional climate model, which is initiated by the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. The modeling results indicate that the projected PV plants in China's Gobi Deserts could impact the local climate, causing positive change of 3.71 ± 0.03 % in the surface relative humidity, and negative changes of 1.60 ± 1.36 W/m2 in the latent heat flux and 0.39 ± 0.00 m/s in the 10-m wind speed in July by about 2060 under the intermediate emission scenario, and these PV-induced climate effects are approximately equivalent to or even greater than the projected climate variability, but almost confined to the local area. These findings suggest that the projected PV deployment in China will lead to low carbon emissions while causing favorable climate effects, such as lower evaporation and wind speed in Gobi Deserts, which will bring environmental and ecological benefits to the PV industry. [ABSTRACT FROM AUTHOR]

Tourette syndrome (TS) is a childhood-onset neurodevelopmental disorder characterized by the presence of multiple motor and vocal tics. Research using resting-state functional magnetic resonance imaging (rfMRI) have found aberrant static functional connectivity (FC) and its topological properties in the brain networks of TS. Our study is the first to investigate the dynamic functional connectivity (dFC) in the whole brain network of TS patients, focusing on the temporal properties of dFC states and the temporal variability of topological organization. The rfMRI data of 36 male children with TS and 27 matched healthy controls were collected and further analyzed by group spatial independent component analysis, sliding windows approach based dFC analysis, k-means clustering analysis, and graph theory analysis. The clustering analysis identified three dFC states. Of these states, state 2, characterized by increased inter-network connections in subcortical network (SCN), sensorimotor network (SMN), and default mode network (DMN), and decreased inter-network connections between salience network (SAN) and executive control network (ECN), was found to have higher fractional window and dwell time in TS, which was also positively correlated with tic severity. TS patients also exhibited higher temporal variability of whole-brain-network global efficiency and local efficiency, and higher temporal variability of nodal efficiency and local efficiency in SCN, DMN, ECN, SAN, and SMN. Additionally, temporal variability of the efficiency and local efficiency in insula was positively correlated with tic severity. Our findings revealed abnormal temporal property of dFC states and temporal variability of topological organization in TS, providing new insights into clinical diagnoses and neuropathology of TS. [ABSTRACT FROM AUTHOR]

Due to the rapid development of solar energy and photovoltaic industries in China, it is crucial to provide the reliable and accurate solar radiation predictions. In this work, three commonly used machine learning models for predicting global and diffuse solar radiation were assessed in eight Chinese cities, representing different geoclimatic and pollutant conditions. According to the results; regarding the nRMSE, nMAE, nMBE and R values, coastal locations (such as Shanghai, Guangzhou, etc.) obtained higher values than inland locations (such as Lanzhou and Wuhan). Moreover, the SVM (support vector machine) highly outperformed the other models in all locations, regardless of whether the study area was arid, semiarid, semihumid or humid, followed by GLMNET (generalized linear modeling) and RF (random forest). In addition, when assessing the SVM in different locations under different climatic and pollution conditions, it was indicated that the accuracy of solar radiation prediction was closely related to the weather and pollution condition levels. In general, the global solar radiation prediction error was in line with the weather condition levels. The prediction error increased as the weather level increased. However, the relationship between the pollution condition levels and the global solar radiation prediction showed a non-linear relationship. Moreover, for the prediction results of diffuse solar radiation, its variation law with different weather and pollution condition levels was almost different from that of global solar radiation. The maximum high error occurrence probability of global solar radiation and diffuse solar radiation appeared at pollution levels 5 and 1, respectively. Overall, the SVM model demonstrated its reliability in radiation prediction under slight pollution and stable weather conditions. This is crucial in locations with scarce meteorological data and can be used to optimize the selection of geographical locations for photovoltaic power station construction. [ABSTRACT FROM AUTHOR]

In order to identify impacts of photovoltaic (PV) power plant on surface radiation, this paper conducted a comparative study on the surface radiation and surface albedo characteristics between the PV site and reference site in the Gobi area in Xinjiang, China in the summer of 2020. The results show that, the upward long-wave radiation at the PV site was significantly lower than that at the reference site in the night, while the two were basically the same during the daytime. The average surface albedo at the PV site (0.14) was 39.1% lower than that at the reference site (0.23). Net radiation was higher at the PV site than that at the reference site throughout the whole day. The average net radiation of the underlying surface at the PV site was 30.7% more than that at the reference site, 75% of which was contributed by lower upward shortwave radiation and 25% was due to nocturnal lower upward longwave radiation at the PV site. • Average surface albedo at photovoltaic power site decreases 39.1%. • Upward long-wave radiation at photovoltaic power site decreases significantly. • Average net radiation at photovoltaic power site increases 30.7%. [ABSTRACT FROM AUTHOR]

• Microclimate and soil characteristics under Gobi PV arrays were first observed. • South-facing PV panels reduced wind speed with the prevailing northerly wind below. • PV panels had buffer effects on microclimate and soil thermal regime change. • Annual net radiation reduction was up to 92.68% since the shading of PV. • The significant increase in relative humidity during winter was caused by snow cover. The high demand for low-carbon energy sources to mitigate climate change has prompted a rapid increase in ground-mounted solar parks. The implementation of photovoltaic (PV) significantly impacted the local climate and ecosystem, which are both poorly understood. To investigate the effects of a typical solar park on the Gobi ecological system, local microclimate and soil thermal regimes were measured year-round under and between PV arrays, at an applied solar park sited in Xinjiang, China. Our results demonstrated their seasonal and diurnal changes. Under solar PV arrays, the mean annual net radiation and wind speed decreased by 92.68 % and 50.53 % respectively. In contrast, PV panels caused an increase of the rear sides air by 10.12 % with 0.87 °C. South-facing PV panels reduced wind speed with the prevailing northerly wind below. In addition, the relative humidity rapidly decreased when snow covered the ground, but slightly increased from April to September. We found the soil under PV panels was cooler and tended to be a sink of energy during spring and summer whereas was more often a source during autumn and winter compared with the soil between PV panels. Observed data developed the understanding of the energy processes of solar parks in Gobi ecosystems and provided evidence to support the sustainable management of the solar park. [ABSTRACT FROM AUTHOR]

The aim of this study is to identify the best practices and future research directions for driver lane-change intention (DLCI) prediction using eye-tracking technologies based on a systematic literature review. We searched five academic literature databases and then conducted an in-depth review, structured coding, and analysis of 40 relevant articles. The literature on DLCI prediction is summarized in terms of input features, feature extraction and prediction time windows, labeling methods, and machine learning algorithms. The results show that eye tracking data features along with other data sources can be useful inputs for the prediction of DLCI. Major challenges in this line of research include determining the optimal time window for feature extraction and developing and evaluating the appropriate machine learning algorithm. Suggestions for future research and practice for DLCI prediction in intelligent vehicles are discussed. [ABSTRACT FROM AUTHOR]