Optimal synchronization with L2-gain performance: An adaptive dynamic programming approach.

This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain external disturbance. Under some mild assumptions, Hamilton-Jacobi-Isaacs equation is derived by the performance index function and system dynamics, which serves as an equivalent formulation. Distributed policy iteration adaptive dynamic programming is developed to obtain the numerical solution to the Hamilton-Jacobi-Isaacs equation. Three theoretical results are given about the proposed algorithm. First, the iterative variables is proved to converge to the solution to Hamilton-Jacobi-Isaacs equation. Second, the L2-gain performance of the closed loop system is achieved. As a special case, the origin of the nominal system is asymptotically stable. Third, the obtained control protocol constitutes an Nash equilibrium solution. Neural network-based implementation is designed following the main results. Finally, two numerical examples are provided to verify the effectiveness of the proposed method.

Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration.

Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.

Superexchange-stabilized long-distance Cu sites in rock-salt-ordered double perovskite oxides for CO2 electromethanation.

Cu-oxide-based catalysts are promising for CO2 electroreduction (CO2RR) to CH4, but suffer from inevitable reduction (to metallic Cu) and uncontrollable structural collapse. Here we report Cu-based rock-salt-ordered double perovskite oxides with superexchange-stabilized long-distance Cu sites for efficient and stable CO2-to-CH4 conversion. For the proof-of-concept catalyst of Sr2CuWO6, its corner-linked CuO6 and WO6 octahedral motifs alternate in all three crystallographic dimensions, creating sufficiently long Cu-Cu distances (at least 5.4 Å) and introducing marked superexchange interaction mainly manifested by O-anion-mediated electron transfer (from Cu to W sites). In CO2RR, the Sr2CuWO6 exhibits significant improvements (up to 14.1 folds) in activity and selectivity for CH4, together with well boosted stability, relative to a physical-mixture counterpart of CuO/WO3. Moreover, the Sr2CuWO6 is the most effective Cu-based-perovskite catalyst for CO2 methanation, achieving a remarkable selectivity of 73.1% at 400 mA cm-2 for CH4. Our experiments and theoretical calculations highlight the long Cu-Cu distances promoting *CO hydrogenation and the superexchange interaction stabilizing Cu sites as responsible for the superb performance.

Other's shoes also fit well: AI technologies contribute to China's blue skies as well as carbon reduction.

Artificial intelligence (AI) technology represents a disruptive innovation that has garnered significant interest among researchers for its potential applications in ecological and environmental management. While many studies have investigated the impact of AI on carbon emissions, relatively few have delved into its relationship with air pollution. This study sets out to explore the causal mechanisms and constraints linking AI technologies and air pollution, using provincial panel data collected from 2007 to 2020 in China. Furthermore, this study examines the distinct pathways through which AI technology can ameliorate air pollution and reduce carbon emissions. The findings reveal the following key insights: (1) AI technologies have the capacity to significantly reduce air pollution, particularly in terms of PM2.5 and SO2 levels. (2) AI technologies contribute to enhanced air quality by facilitating adjustments in energy structures, improving energy efficiency, and strengthening digital infrastructure. Nonetheless, it is important to note that adjusting the energy structure remains the most practical approach for reducing carbon emissions. (3) The efficacy of AI in controlling air pollution is influenced by geographical location, economic development level, level of information technology development, resource dependence, and public attention. In conclusion, this study proposes novel policy recommendations to offer fresh perspectives to countries interested in leveraging AI for the advancement of ecological and environmental governance.

Extraordinary Thermal Stability and Sinter Resistance of Sub-2 nm Platinum Nanoparticles Anchored to a Carbon Support by Selenium.

Nanoparticle sintering has long been a major challenge in developing catalytic systems for use at elevated temperatures. Here we report an in situ electron microscopy study of the extraordinary sinter resistance of a catalytic system comprised of sub-2 nm Pt nanoparticles on a Se-decorated carbon support. When heated to 700 °C, the average size of the Pt nanoparticles only increased from 1.6 to 2.2 nm, while the crystal structure, together with the {111} and {100} facets, of the Pt nanoparticles was well retained. Our electron microscopy analyses suggested that the superior resistance against sintering originated from the Pt-Se interaction. Confirmed by energy-dispersive X-ray elemental mapping and electron energy loss spectra, the Se atoms surrounding the Pt nanoparticles could survive the heating. This work not only offers an understanding of the physics behind the thermal behavior of this catalytic material but also sheds light on the future development of sinter-resistant catalytic systems.