Can artificial intelligence improve green economic growth? Evidence from China.

Not only has artificial intelligence changed the production methods of traditional industries; it has also presented a great opportunity for future industrial development to decouple from environmental degradation and the promotion of green economic growth. The article studies the influence of artificial intelligence on green economic growth and its mechanism. The research shows that (1) artificial intelligence can promote green economic growth in China. After accounting for spatial factors, it was found that artificial intelligence could promote local green economic growth, but had a siphon effect on neighboring green economic growth. From the perspective of dynamic effects, in the short term, artificial intelligence will not significantly dampen green economic growth in neighboring regions. In the long run, artificial intelligence will have a stronger role in promoting green economic growth, and the siphon effect on neighboring cities will be more significant. (2) As the level of human capital increases, the negative spillover effect of artificial intelligence will be significantly weakened. The promotion effect of artificial intelligence on green economic growth is relatively weak in resource-based cities. (3) Artificial intelligence has obvious attenuation characteristics on the spatial spillover effect of green economic growth, but significant influence is limited to within 200 km. (4) Artificial intelligence has the greatest impact on productivity, accounting for 30.59% in promoting green economic growth. The green innovation effect was 0.0181, accounting for 5.64%. The resource allocation effect is 0.0011, accounting for 3.44%. This paper provides policy enlightenment for promoting industrial intelligence and green economic growth.

Application of a nomogram to radiomics labels in the treatment prediction scheme for lumbar disc herniation.

OBJECTIVE: To investigate and verify the efficiency and effectiveness of a nomogram based on radiomics labels in predicting the treatment of lumbar disc herniation (LDH). METHODS: By reviewing medical records that were analysed over the past three years, clinical and imaging data of 200 lumbar disc patients at the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine were obtained. The collected cases were randomly divided into a training group (n = 140) and a testing group (n = 60) at a ratio of 7:3. Two radiologists with experience in reading orthopaedics images independently segmented the ROIs. The whole intervertebral disc with the most obvious protrusion in the sagittal plane T2WI lumbar MRI as a mask (ROI) is sketched. The LASSO (Least Absolute Shrinkage And Selection Operator) algorithm was used to filter the features after extracting the radiomics features. The multivariate logistic regression model was used to construct a quantitative imaging Rad­Score for the selected features with nonzero coefficients. The radiomics labels and nomogram were evaluated using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The calibration curve was used to evaluate the consistency between the nomogram prediction and the actual treatment plan. The DCA decision curve was used to evaluate the clinical applicability of the nomogram. RESULT: Following feature extraction, 11 radiomics features were used to construct the radiomics label for predicting the treatment plan of LDH. A nomogram was then constructed. The AUC was 0.93 (95% CI: 0.90-0.97), with a sensitivity of 89%, a specificity of 91%, a positive predictive value of 92.7%, a negative predictive value of 89.4%, and an accuracy of 91%. The calibration curve showed that there was good consistency between the prediction and the actual observation. The DCA decision curve analysis showed that the nomogram of the imaging group has great potential for clinical application when the risk threshold is between 5 and 72%. CONCLUSION: A nomogram based on radiomics labels has good predictive value for the treatment of LDH and can be used as a reference for clinical decision-making.

Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China.

Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises' performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises' energy efficiency.

[Static Migration and Release of Dibutyl-Phthalate in the Fluctuating Zone of Three Gorges Reservoir].

Phthalic acid esters (PAEs) have received increasing attention in recent years due to their widespread use and hazards to human health and fertility in the environment. In order to understand the migration and release processes of organic pollutants in huge fluctuating zone soil, Dibutyl-phthalate(DBP) was chosen as a typical substance, and its migration and release characteristics in the fluctuating zone of the Three Gorges Reservoir to overlying water and the impacts of DBP concentration in the soil, ionic strength and the concentration of organic mater in overlying water on the process were studied using static flooding method. The results showed that DBP migrated from the soil to the overlying water in the early days after flooding, and the release process of DBP was divided into two phases: one was quick release with a relatively short releasing time and a rapid releasing rate; the other was slow release with a relatively long releasing time and a slow releasing rate. The migration and release processes were well fitted by two-compartment first-order kinetics. After different concentrations DBP were added into soil, the rate of quick release increased with the increasing DBP concentrations in soil while the percent of quick release decreased with the increasing DBP concentrations. The results of rate of slow release and the percent of slow release were on the contrary. The water conditions of overlying water could impact the migration and releasing process of DBP when the soil in fluctuating zone was flooded. The amount of DBP released into the overlying water would increase when the ionic strength in the water increased. At the same time, when the ionic strength increased, in spite of the decreasing quick release rate, the percent of quick release increased. The higher concentration of organic matter in overlying water, the more the amount of DBP released into the overlying water. At the same time, all of the rates of quick release, slow release and the percent of quick release would increase with the increasing concentrations of organic matter, while there was almost no influence on the percent of slow release of DBP.

[Effect of external condition on the static migration and release of dibutylphthalate in the soil of the fluctuating zone of the Three Gorges Reservoir to the overlying water].

In order to understand the environmental behavior of the organic pollutants Dibutyl-phthalate (DBP) in fluctuating zone soil, the migration and release processes of DBP in the fluctuating zone of the Three Gorges Reservoir to the overlying water and the impacts of temperature, light, coexistence phthalate-bis (2-ethylhexyl)-ester (DEHP), microbial activity on the process were studied using static flooding method. The results showed that DBP migrated from the soil to the overlying water in the early days after flooding, and the release process of DBP was divided into two phases: one was the quick release with a relatively short releasing time and a rapid releasing rate; the other was the slow release with a relatively long releasing time and a slow releasing rate. The slow release was a major speed control step, which could be well fitted by two-compartment first-order kinetics. In the interim (12 d) after flooding, the capacity of release reached a maximum, the DBP released from the soil into the water migrated from the water to the soil again after continued flooding, and eventually the content of DBP in soil and water reached equilibrium in the later period after flooding. The intensity of DBP releasing into the overlying water and the rapid releasing rate increased, while the slow releasing rate decreased when the temperature increased. The concentrations of DBP released into the water were different with different light sources. The concentration of DBP in the overlying water with treatment of natural light was higher than those with treatment of ultraviolet light UVB, UVA. After the amount of DBP in the overlying water reached the maximum, the content of DBP in the overlying water decreased relatively faster under the ultraviolet light than under the natural light. The largest release content of DBP and the time reached the largest release content were different with different oxygen content in the overlying water. Overall, the higher oxygen content in the overlying water, the higher content of DBP in the overlying water. The time when the concentration of DBP in overlying water reached the maximum was on the 8th day after flooding in the high oxygen and low oxygen studies, while the time was on the 12th day in natural study. When the phthalate-bis (2-ethylhexyl)-ester(DEHP) co-existed in the soil, there would be some significant influence on the release of DBP. After DEHP addition in the soil, it could release more DBP than the control, and both the rapid releasing rate and slow releasing rate were bigger than those of the control. The microbial activity had some impacts on the process. However, the effect was not obvious. After adding microbial activity inhibitor, the content of migrated DBP was slightly lower than that of the control.