RESUMEN
Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.
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Inteligencia Artificial , Desarrollo Económico , China , Políticas , Tecnología , EficienciaRESUMEN
Selecting suitable internal reference genes is an important prerequisite for the application of quantitative real-time PCR (qRT-PCR). However, no systematic studies have been conducted on reference genes in luffa. In this study, seven reference genes were selected, and their expression levels in luffa plants exposed to various simulated abiotic stresses [i.e., cold, drought, heat, salt, H2O2, and abscisic acid (ABA) treatments] were analyzed by qRT-PCR. The stability of the reference gene expression levels was validated using the geNorm, NormFinder, BestKeeper, and RefFinder algorithms. The results indicated that EF-1α was the most stably expressed and suitable reference gene overall and for the heat, cold, and ABA treatments. Additionally, UBQ expression was stable following the salt treatment, whereas TUB was identified as a suitable reference gene for H2O2 and drought treatments. The reliability of the selected reference genes was verified by analyzing the expression of copper/zinc superoxide dismutase (Cu/Zn-SOD) gene in luffa. When the most unstable reference genes were used for data normalizations, the resulting expression patterns had obvious biases when compared with the expression patterns for the most ideal reference genes used alone or combined. These results will be conducive to more accurate quantification of gene expression levels in luffa.
Asunto(s)
Adaptación Fisiológica/genética , Genes Esenciales , Luffa/genética , Factor 1 de Elongación Peptídica/genética , Proteínas de Plantas/genética , Reacción en Cadena en Tiempo Real de la Polimerasa/normas , Ácido Abscísico/farmacología , Frío , Sequías , Perfilación de la Expresión Génica , Regulación de la Expresión Génica de las Plantas , Calor , Peróxido de Hidrógeno/farmacología , Luffa/efectos de los fármacos , Luffa/metabolismo , Factor 1 de Elongación Peptídica/metabolismo , Proteínas de Plantas/metabolismo , Estándares de Referencia , Salinidad , Estrés Fisiológico/genética , Superóxido Dismutasa/genética , Superóxido Dismutasa/metabolismoRESUMEN
BACKGROUND: The robotic instrument of the da Vinci surgical system determines the accuracy of robotic-assisted surgery, However, the most effective cleaning method of robotic equipment is a challenge for healthcare professionals. This study compared three da Vinci robot-assisted surgery manipulators to detect the effect of "non-destructive" testing of the cleaning effect by two different methods. METHODS: The post-surgical cleaning of the da Vinci robotic instruments in the Sun Yat-sen University Cancer Center, The First Affiliated Hospital of Guangzhou Medical University and the Shenzhen Second People's Hospital was performed using two different processes from January 2019 to January 2020: manual joint automatic ultrasonic cleaning and automatic mechanical cleaning. The efficacy of visual estimation, the residual protein assay (quantitative) and adenosine triphosphate (ATP) biological biofluorescence detection of the cleanliness of the mechanical instrument's work (distal working end) surface and the shaft's inner chamber was compared. If the cleaning effect of any position on the surface or inner cavity of the manipulator did not qualify, the entire robotic instrument was judged as disqualified. RESULTS: A total of 198 cases of da Vinci robotic instrument postoperative cleanliness data were collected. The qualifying rates of automatic ultrasonic cleaning were 96.97% by visual estimation, 93.94% by residual protein assay and 60.61% by ATP biological fluorescence detection. The respective rates for automatic mechanical cleaning were 100% by visual estimation, 90.91% by residual protein assay and 66.67% by ATP biological fluorescence detection. CONCLUSIONS: The cleaning of the da Vinci robotic instrument detected by "non-destructive" residual protein assay or ATP biological fluorescence detection is more accurate than visual estimation.
RESUMEN
Halogenated tetraphenylethene derivatives show a unique anti-heavy-atom effect where introducing heavy halogens like bromine greatly improves the fluorescence quantum yield upon aggregation, contrary to the classic heavy-atom effect. The unique self-reversible mechanochromism of brominated TPE is attributed to re-generation of halogen-halogen bonding after its breakage.