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To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours.

Hand gesturing is one of the most useful non-verbal behaviors in the classroom, and can help students activate multi-sensory channels to complement teachers’ verbal behaviors and ultimately enhance teaching effectiveness. The existing mainstream detection algorithms that can be used to recognize hand gestures suffered from low recognition accuracy under complex backgrounds and different backlight conditions. This study proposes an improved hand gesture recognition framework based on key point statistical transformation features. The proposed framework can effectively reduce the sensitivity of images to background and light conditions. We extracted key points of the image and establish a weak classifier to enhance the anti-interference ability of the algorithm in the case of noise and partial occlusion. Then, we used a deep convolutional neural network model with multi-scale feature fusion to recognize teachers’ hand gestures. A series of experiments were conducted on different human gesture datasets to verify the performance of the proposed framework. The results show that the framework proposed in this study has better detection and recognition rates compared to the you only look once (YOLO) algorithm, YOLOv3, and other counterpart algorithms. The proposed framework not only achieved 98.43%, measured by F1 score, for human gesture images in low-light conditions, but also has good robustness in complex lighting environments. We used the proposed framework to recognize teacher gestures in a case classroom setting, and found that the proposed framework outperformed YOLO and YOLOv3 algorithms on small gesture images with respect to recognition performance and robustness.

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 T 2 WI 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 Rad‑Score 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.

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.

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.

Unlike ordinary plate air preheater, the new dual-plate air preheater can generate temperature gradient through adding another plate in cold side to maintain certain space. And it can decrease the temperature of hot fluid in the outlet while increasing the temperature of plate surface to achieve the purpose of anti-corrosion.

Impaired aerobic exercise capacity and skeletal muscle dysfunction are associated with cardiometabolic diseases. Acute administration of capsaicin enhances exercise endurance in rodents, but the long-term effect of dietary capsaicin is unknown. The capsaicin receptor, the transient receptor potential vaniUoid 1 （TRPV1） cation channel has been detected in skeletal muscle, the role of which remains unclear. Here we report the function of TRPV1 in cultured C2C12 myocytes and the effect of TRPV1 activation by dietary capsaicin on energy metabolism and exercise endurance of skeletal muscles in mice. In vitro, capsaicin increased cytosolic free calcium and peroxisome proliferator-acti- vated receptor-γcoactivator-1α （PGC-1α） expression in C2C12 myotubes through activating TRPV1. In vivo, PGC-1α in skeletal muscle was upregulated by capsaicin-induced TRPV1 activation or genetic overexpression of TRPV1 in mice. TRPV1 activation increased the expression of genes involved in fatty acid oxidation and mitochondrial respiration, promoted mitochondrial biogenesis, increased oxidative fbers, enhanced exercise endurance and prevented high-fat diet-induced metabolic disorders. Importantly, these effects of capsaicin were absent in TRPVl-deficient mice. We conclude that TRPV1 activation by dietary capsaicin improves energy metabolism and exercise endurance by upregulating PGC-1α in skeletal muscles. The present results indicate a novel therapeutic strategy for managing metabolic diseases and improving exercise endurance.

Acetyl-CoA is a fundamental metabolite for all life on Earth, and is also a key starting point for the biosynthesis of a variety of industrial chemicals and natural products. Here we design and construct a Synthetic Acetyl-CoA (SACA) pathway by repurposing glycolaldehyde synthase and acetyl-phosphate synthase. First, we design and engineer glycolaldehyde synthase to improve catalytic activity more than 70-fold, to condense two molecules of formaldehyde into one glycolaldehyde. Second, we repurpose a phosphoketolase to convert glycolaldehyde into acetyl-phosphate. We demonstrated the feasibility of the SACA pathway in vitro, achieving a carbon yield ~50%, and confirmed the SACA pathway by 13 C-labeled metabolites. Finally, the SACA pathway was verified by cell growth using glycolaldehyde, formaldehyde and methanol as supplemental carbon source. The SACA pathway is proved to be the shortest, ATP-independent, carbon-conserving and oxygen-insensitive pathway for acetyl-CoA biosynthesis, opening possibilities for producing acetyl-CoA-derived chemicals from one-carbon resources in the future. The microbial synthesis of carbon-containing compounds from single carbon precursors is desirable, yet designed pathways to achieve this goal overlap with host metabolism. Here the authors design a de novo metabolic pathway to assimilate formaldehyde into acetyl-CoA that does not overlap with known metabolic networks.

The Tethyan metallogenic domain (TMD), as one of the three major domains in the world, extends over 10000 km from east to west, and has developed several world-class metallogenic belts, such as the Gangdese porphyry Cu belt, the Sanjiang metallogenic belt, the Iran porphyry Cu belt, the Pakistan porphyry Cu belt, the southeastern European epithermal gold deposit belt, and the Southeast Asian Sn belt. The formation and evolution of the TMD is mainly controlled by the multi-stage subduction of Tethys oceanic slabs, the opening and closing of several small ocean basins, and continent-continent collision. The Tethys oceans include the Proto-Tethys (Cambrian-Silurian), Paleo-Tethys (Carbonaceous-Triassic) and Neo-Tethys (Jurassic to Cretaceous), which in turn are formed by rifting from the Gondwana land at different times in different micro-continents. With a series of geological processes such as oceanic opening and closing, continental collision and post-collisional reworking with intraplate deformation, various types of ore deposits are developed in the TMD, including porphyry deposits, epithermal deposits, VMS deposits, chromite deposits, Sn deposits and orogenic gold deposits. The metallogenic processes of the TMD can be categorized into three stages. (1) Oceanic subduction: With the subduction of the oceanic slab and dehydration of basalt and sediments, the asthenospheric mantle was metasomatized with preliminary enrichment in metals under oxidized condition. (2) Continental subduction: Continental collision induced partial melting of the mantle wedge enriched the metals and water in mafic melts, which ascended from subarc depths to the lower crust, locally to the shallow crust for hydrothermal mineralization. (3) Post-collisional reworking: Partial melting of the mafic intrusives in the lower crust produced felsic melts under oxidized and water-rich conditions, which underwent crystal fractionation and transferred water and metals into hydrothermal fluids for mineralization. The large-scale porphyry mineralization in the TMD mainly occurs in the Miocene, which is an important scientific issue worthy of further study in the future. How is the metal enriched in the processes of gradual maturity of the crust, and how does large-scale mineralization occur in a collisional orogen where there is no subduction and dehydration of oceanic slabs anymore to supply S and Cl? These are still important questions in the study of porphyry mineralization in the Tethyan orogen. The application of hyperspectral and mineralogical studies of alteration assemblages is beneficial for prospecting and exploration in the TMD.

Partial substitution of chemical fertilizers by organic amendments is essential for improving the soil quality without yield loss. Fungi play an important role in soil quality because they decompose organic matter and cycle nutrients in the soil. However, there is limited information regarding the effect of different organic substitution rates (OSRs) on the soil quality and fungal community. This study investigated the relationship between the soil quality index and fungal community in a tea plantation under different OSRs of N, from a single application of synthetic fertilizer (NPK) to 100% N substitution with organic fertilizer (OM100). The OSRs were positively correlated with the soil physicochemical and biological soil quality index (SQI), but only the physicochemical SQI exhibited a significant relationship with tea production. The OSR also shifted the soil fungal community composition. Soil pH, soil organic C (SOC), microbial biomass C (MBC), and available potassium (AK) were the key characteristics that were significantly correlated with the variation of soil fungal community. Network analysis indicated that additional organic substitution can enhance the soil fungal network complexity, which also showed a positive correlation with the SQI. These results confirmed the feasibility of organic substitution for soil quality improvement, and implied that the soil fungal network complexity could be a new indicator for soil quality assessment.