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Big data is a pivotal factor in propelling the digital economy forward and emerges as a novel driver in realizing the goals of carbon peaking and carbon neutrality. This study focuses on a quasi-natural experiment, namely national big data comprehensive pilot zones (NBD-CPZs), and employs a multi-period difference-in-differences (DID) model to identify the influence of big data on carbon emissions. The findings of this study are as follows. Overall, big data significantly reduces carbon emissions within the pilot zones. Mechanism analysis shows that big data reduces urban carbon emissions by promoting green innovation, optimizing energy structure, mitigating capital mismatch and improving public awareness of environmental protection. Heterogeneity analysis shows that the carbon reduction effect of big data are more pronounced in cities with high levels of digital economy, non-resource-based cities, cities with strong intellectual property rights protection and the Guizhou Province. Spatial effect analysis indicates that within a radius of 400–500 km, the NBD-CPZ increases urban carbon emissions, signifying a significant siphoning effect; within a radius of 500–900 km, the NBD-CPZ reduces urban carbon emissions, signifying a significant spillover effect, and beyond a distance of 900 km, the spatial effect of the NBD-CPZ is not significant. Based on the above conclusions, this study puts forward several policy recommendations to effectively exert the carbon emission reduction effect of big data. [ABSTRACT FROM AUTHOR]

Economic development often results in significant greenhouse gas emissions, contributing to global climate change, which demands immediate attention. Despite implementing various low-carbon policies to promote sustainable economic and environmental progress, current evaluations reveal limitations and deficiencies. Therefore, this study utilizes a dataset detailing policy intensity at a prefecture-level city in China to investigate the impacts of these policies on carbon emission reduction from 2007 to 2022 in 334 prefecture-level cities, employing a fixed-effects model. Additionally, it assesses the policies' efficacy. The findings indicate a significant negative correlation between China's low-carbon policies and carbon emissions, supported robustly by multiple tests. Specifically, a one-unit increase in China's policy intensity correlates with a 0.53-unit reduction in carbon emissions. Furthermore, the heterogeneity analysis shows that variations in urban agglomerations, environmental resource endowments, pollution levels, and low-carbon policy intensities influence the effectiveness of these policies in reducing carbon emissions. This analysis underscores that policy intensity achieves emission reductions through technological innovation, industrial transformation, welfare crowding out, and pollution transfer, with varying impacts across different environmental contexts, pollution levels, and policy intensities. Based on this analysis, we recommend several policies: formulating low-carbon strategies tailored to local conditions, enhancing regional low-carbon policies, establishing cross-regional coordination mechanisms, and so on. These recommendations not only offer valuable policy insights for China but also serve as useful references for the green and sustainable development of other developing countries. [ABSTRACT FROM AUTHOR]

Myocardial fibrosis is a pathological phenomenon associated with cardiovascular disease (CVD) that plays a crucial role in the development of heart diseases. Vitamin D deficiency can promote the development of CVD and exercise plays a role in the treatment of CVD. This study aimed to explore the effects of 12-week aerobic exercise training on myocardial fibrosis in vitamin D-deficient mice. A vitamin D-deficient mouse model was induced by a vitamin D-deficient (0 IU Vitamin D3/kg) diet. Twenty-four C57BL/6J male mice were randomly divided into three groups: a control sedentary group (CONS, n = 8), a vitamin D-deficient sedentary group (VDDS, n = 8), and a vitamin D-deficient exercise group (VDDE, n = 8) which was aerobically trained for 12 weeks. The results showed that the serum 25-hydroxyvitamin D [25(OH)D] levels of the VDDS group were <50 nmol/L, which was significantly lower than that of the CONS group. Compared with the CONS group, the VDDS group showed cardiac dysfunction and significant fibrosis, together with lower vitamin D receptor (VDR) mRNA and protein expression levels, higher mRNA expression levels of profibrotic and inflammatory factors, and higher transforming growth factor-β1 (TGF-β1) and phospho-Smad2/3 (P-Smad2/3) protein expression levels. Serum 25(OH)D levels in the VDDE group were significantly higher than those in the VDDS group. Compared with the VDDS group, the VDDE group showed improved cardiac function and alleviated myocardial fibrosis. Meanwhile, the VDDE group had significantly higher VDR mRNA and protein expression levels; lower mRNA expression levels of profibrotic and inflammatory factors; and lower TGF-β1 and P-Smad2/3 protein expression levels. In conclusion, aerobic exercise training remains a promising intervention for treating myocardial fibrosis in vitamin D deficiency. [ABSTRACT FROM AUTHOR]

The compressive strength of concrete can be predicted by machine learning. One thousand thirty samples of concrete compressive strength data were used as the dataset. Machine learning was applied to prediction of concrete compressive strength with seven machine learning algorithms. To improve data utilization and generalization ability of machine learning model, ten data sets were constructed by feature reorganization for data augmentation. Compared with other machine learning models, the XGBoost model based on Boosting tree algorithm had the highest prediction accuracy and the most robust generalization ability. With different multi-feature combination input conditions, the R2 score of the XGBoost algorithm was 0.9283, the MAE score was 3.4292, the MAPE score was 12.5656, and the RMSE score was 5.2813. The error accumulation curve of the XGBoost algorithm was analyzed. When the compressive strength of concrete is at 5–20MPa, the error contribution rate is higher. When the concrete compressive strength is at 20–40MPa, the prediction result error of the model drops sharply. When the strength reaches 40MPa, the error contribution rate of the model tends to converge and the error contribution rate is stable between 1 and 1.2, which indicates that the model has high prediction accuracy when the compressive strength is higher than 40 MPa. [ABSTRACT FROM AUTHOR]

The intelligent detection of distress in concrete is a research hotspot in structural health monitoring. In this study, Att-Unet, an improved attention-mechanism fully convolutional neural network model, was proposed to realize end-to-end pixel-level crack segmentation. Att-Unet consists of three parts: encoding module, decoding module, and AG (Attention Gate) module. The benefits associated with this module can effectively extract multi-scale features of cracks, focus on critical areas, and reconstruct semantics, to significantly improve the crack segmentation capability of the Att-Unet model. On the same data set, the mainstream semantic segmentation models (FCN and Unet) were trained simultaneously. Upon comparing and analyzing the calculated results of Att-Unet model with those of FCN and Unet, the results are as follows: for crack images under different conditions, Att-Unet achieved better results in accuracy, precision and F1-scores. Besides, Att-Unet showed higher feature extraction accuracy and better generalization ability in the crack segmentation task. [ABSTRACT FROM AUTHOR]

Insects may develop different behavioral phenotypes in response to heterogeneous environments (e.g., host plants), but the plasticity of their feeding behaviors has been rarely explored. In order to address the issue, clones of the English grain aphid, Sitobion avenae (Fabricius), were collected from wheat, and their probing behaviors were recorded on three plants. Our results demonstrated that S. avenae individuals on the alternative plants (i.e., barley and oat) tended to have higher frequency of non-probing (Np), increased duration of the pathway phase, increased phloem salivation, and decreased phloem ingestion (E2), compared to those on the source plant (i.e., wheat), showing the resistance of barley and oat to this aphid’s feeding. This aphid showed apparently high extents of plasticity for all test probing behaviors on barley or oat. Positive selection for higher extents of plasticity in E2 duration was identified on barley and oat. The factor ‘clone’ alone explained 30.6% to 70.1% of the total variance for each behavioral plasticity, suggesting that the divergence of probing behavior plasticity in S. avenae had a genetic basis. This aphid’s fitness correlated positively with the plasticity of Np frequency and E2 frequency. Some behaviors and their corresponding plasticities (e.g., the frequency of xylem ingestion and its plasticity) were found to be correlated characters, probably reflecting the limits for the evolution of higher extents of behavioral plasticity in this aphid. The differential probing behaviors and their plasticity in S. avenae can have significant implications for the adaptation and management of aphids on different plants. [ABSTRACT FROM AUTHOR]

To improve the properties of ground granulated blast furnace slag (GGBS) and utilize ground granulated blast furnace slag efficiently, this study investigates the effect of fineness on the hydration activity index (HAI) of ground granulated blast furnace slag. The hydration activity index of GGBS with six specific surface areas (SSAs) was characterized by the ratio of compressive strength of the prismatic mortar test block. The particle size distribution of GGBS with different grinding times was tested by laser particle size analyzer. The paste of different specific surface area GGBSs in different curing ages was investigated at the micro level by X-ray diffraction, scanning electron microscope, energy dispersive spectrometer, thermogravimetric scanning calorimeter, and differential scanning calorimeter. The effect of particle distribution of GGBS on the hydration activity index of different curing ages was studied by gray correlation analysis. The results indicated that the compressive strength and hydration activity index increases with the increase of a specific surface area of GGBS at different curing ages. The hydration activity index at different curing ages is almost a linear role for specific surface areas. With the increase in the specific surface area of GGBS, the content of Ca(OH)2 in paste decreases gradually. When GGBS was added into a mortar test block, the hydrate calcium silicate gel in paste changed from a high Ca/Si ratio to a low Ca/Si ratio. The 0–10 micron particles of GGBS particle distribution were highly correlated with the hydration activity index at different curing ages. [ABSTRACT FROM AUTHOR]

The apple buprestid beetle, Agrilus mali Matsumura (Coleoptera: Buprestidae), can respond to various volatiles, but the underlying mechanism of odorant perception for this insect is poorly understood. Here, we cloned A. mali's odorant-binding proteins 3 (AmalOBP3) and 8 (AmalOBP8) and characterized their expression patterns and binding profiles. Sequence and phylogenetic analyses showed that AmalOBP3 and AmalOBP8 were distributed in the classic and minus-C OBP subfamily, respectively. AmalOBP3 was specifically and abundantly expressed in antennae of both sexes. AmalOBP8 displayed high transcript levels in antennae of both sexes, abdomens of males, and wings of both sexes. Both AmalOBPs exhibited much higher expression in male antennae than in female antennae, suggesting that they could be important in perception of male-specific olfactory cues (e.g., some sex pheromones). Out of the 40 odorant ligands tested, AmalOBP3 and AmalOBP8 bound to 15 and 21 different odorants, respectively, indicating a distinct and selective binding profile for them. Both AmalOBPs seemed to have very strong binding affinity to aliphatic alcohols and aldehydes with 12 to 15 carbon atoms. Alcohols, esters, and terpenoids were more likely to be good ligands for both AmalOBPs than aldehydes and alkanes. Together with its broad expression in different tissues, strong binding with higher numbers of putative ligands for AmalOPB8 means that this protein can have more extensive functional roles in chemosensation of A. mali. Our results provide insights into the molecular basis of chemosensation in A. mali, as well as a basis for developing detection, monitoring, and management tools for this serious pest.

The apple buprestid beetle, Agrilus mali Matsumura, is an invasive pest causing significant damages to rare wild apple forests of Xinjiang. The morphology, abundance and distribution of antennal sensilla in both sexes of this pest were examined. We found that the antennae of A. mali females were longer than those of males. Five types of antennal sensilla were characterized, including trichodea (subtypes Tr.1, Tr.2, and Tr.3), chaetica (subtypes Sc.1, Sc.2, Sc.3, and Sc.4), basiconica (subtypes Ba. 1, Ba. 2, Ba. 3 and Ba.4), Böhm bristles (subtypes BB. 1, and BB. 2), and multiporous grooved sensilla. The most abundant sensilla of Ba.2 tended to occur mainly on flagellomeres 5-8 in both sexes. The last three flagellomeres tended to have the most abundant Tr.1 in both sexes. Overall, the abundance and distribution of these sensilla appeared to be highly conserved in both sexes, and their olfactory organs seemed to cluster on flagellomeres 6-8. However, some sex dimorphisms were also observed. Tr.3 and BB.2 were found only in females. Sensilla of Sc.2 were found on the pedicel and first two flagellomeres only in males. When compared with males, females showed a higher number of Sc.3, but a lower number of Sc.4 on the pedicel. These results indicate that contact cues could be important in intersexual communication in A. mali. The functional roles of these sensilla and their implications in A. mali behaviors are discussed, and further studies of identified chemosensitive sensilla can provide a foundation for developing semiochemical-based management strategies., (© The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America.)