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This paper introduces an optimization method for multi-robot automated control welding based on a Particle Swarm Genetic Algorithm (PSGA), aiming to address issues such as high costs, large footprint, and excessive production cycles in multi-robot welding production lines. The method first constructs a multi-axis robotic kinematic model to provide constraint conditions. Then, the PSO (particle swarm optimization) algorithm, which integrates penalty functions into the fitness evaluation, is used to determine the optimal welding path by simulating collective behavior within a group. The GA (genetic algorithm) encodes the position of the welding robot bases into chromosomes to find the optimal layout for coordinated control of multiple robots. The entire process is optimized according to welding standards and requirements. Additionally, a comprehensive production line performance estimation model was used to quantitatively analyze the new scheme. The results show that the optimized production line's balance rate increased by 10%, the balance loss rate decreased by 10%, the smoothness index increased by 37.8%, the space costs reduced by 44.4%, the equipment demand reduced by 41.1%, the labor demand reduced by 50%, the total costs reduced by 10%, and the average product cycle time was reduced by 5.07 s. Finally, we tested the algorithm in various complex scenarios and compared its performance against mainstream algorithms within the context of this study. The results demonstrated that the optimized production line significantly improved efficiency while maintaining safety standards. [ABSTRACT FROM AUTHOR]

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model's superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies. [ABSTRACT FROM AUTHOR]

Hydrogen (H2) production from water electrolysis provides a reliable route to accelerated decarbonization. Most of the studies in the past have focused on high-performance electrocatalysts, and there have been few innovations in water electrolytic systems. In conventional water electrolysis processes, the gaseous products mix to some extent inevitably, which poses an inherent challenge in terms of economics and operating costs. In this context, the use of redox mediators (RMs) for decoupled water electrolysis was first proposed in 2013 to completely separate the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in spatial and temporal terms. Among them, the realization of soluble RMs requires the introduction of a proton exchange membrane as well as a driving system, which raises technical issues and even limits the energy conversion efficiency. A solid-state RM decoupling system can overcome the above limitations and has received increasing attention. Herein, we review the recent progress of solid-state RMs for decoupled water electrolysis in different electrolytes. Specifically, we discuss the advantages and challenges of the current solid-state decoupled water electrolysis technologies and offer some perspectives on future developments. [ABSTRACT FROM AUTHOR]

Sharp eyespot, caused by the necrotrophic fungal pathogen Rhizoctonia cerealis , is a devastating disease of bread wheat (Triticum aestivum). However, the molecular mechanisms underlying wheat defense against R. cerealis are still largely unknown. In this study, by comparative transcriptomic analysis we identified a novel cysteine-rich receptor-like kinase (CRK)-encoding gene, designated as TaCRK3 , and investigated its role in defense against R. cerealis. TaCRK3 transcript abundance was significantly elevated by R. cerealis and exogenous ethylene treatments. Silencing of TaCRK3 significantly compromised resistance to R. cerealis and repressed expression of an ethylene biosynthesis enzyme-encoding gene, ACO2 , and a subset of defense-associated genes in wheat, whose transcript levels are up-regulated by ethylene stimulus. TaCRK3 protein was localized at the plasma membrane in wheat. Noticeably, both the heterologously expressed TaCRK3 protein and its partial peptide harboring two DUF26 (DOMAIN OF UNKNOWN FUNCTION 26) domains could inhibit growth of R. cerealis mycelia. These results suggest that TaCRK3 mediates wheat resistance to R. cerealis through direct antifungal activity and heightening the expression of defense-associated genes in the ethylene signaling pathway. Moreover, its DUF26 domains are required for the antifungal activity of TaCRK3. Our results reveal that TaCRK3 is a promising gene for breeding wheat varieties with resistance to R. cerealis. [ABSTRACT FROM AUTHOR]

Multifunctional plant growth-promoting rhizobacteria (PGPR) have garnered significant attention in agricultural applications; however, a few have applied them in crop rotation or intercropping fields. To identify PGPR with strong colonization ability and broad spectrum benefit, we screened strains from the local tobacco rhizosphere and evaluated their growth-promoting effects across various crops and farming systems. In this study, strain L8, identified as Bacillus thuringiensis , was selected as a multifunctional PGPR capable of producing indole-3-acetic acid (IAA), solubilizing potassium, and mobilizing both organic and inorganic phosphorus. Compared with the control group, the soil treated with strain L8 in tobacco pot experiments showed significantly higher levels of IAA, available potassium, and available phosphorus. Furthermore, tobacco plants inoculated with L8 exhibited significantly improved growth parameters compared with the uninoculated control. The results indicated that compared with the control, strain L8 increased tobacco fresh weight (by 83.18%), plant height (by 29.32%), relative chlorophyll content (by 14.33%), as well as plant phosphorus (by 23.78%) and potassium (by 30.81%). Interestingly, L8 not only enhanced tobacco growth but also improved tobacco root morphology, significantly increasing root length (1.55-fold), root surface area (1.78-fold), and root volume (2.05-fold) compared with the control. These findings provide valuable insights into utilizing plant growth-promoting bacteria for tobacco production. Moreover, the inoculation strain L8 enriched soil organic matter and nutrient content in both wheat ( Triticum aestivum )-maize ( Zea mays ) rotation and peanut ( Arachis hypogaea )-maize intercropping systems. Furthermore, within the intricate tillage systems of wheat-corn rotation and peanut-corn intercropping, multifunctional PGPR strain L8 can play a crucial role in crop growth promotion, yield increase, and higher soil nutrient availability.IMPORTANCEThese findings aim to study the application of plant growth-promoting rhizobacteria (PGPR) in diverse crops and complex planting systems, showing their adaptability and effectiveness in various agricultural contexts. The study suggests that this strain improves nutrient utilization in the soil, enhances soil health, and plays a significant role in plant growth through the secretion of substances like auxin (IAA). In parallel, we observed that applying this strain improved the production efficiency of wheat, corn, and peanuts within complex farming systems, indicating that this method holds the potential for enhancing both the yield and quality of various crops.

Cd x Zn 1- x S nanocrystals with sizes ranging from 3-11 nm were synthesized by a simple organic solution method. The nanocrystals possess a cubic zinc-blende structure and the bandgap blue-shifts from 2.1 eV to 3.4 eV by increasing the composition of Zn ions in the solid solutions. After a facile ligand exchange process, the photocatalytic activity for H 2 production of the Cd x Zn 1- x S nanocrystals was investigated under visible-light irradiation ( λ ≥ 420 nm) with Na 2 SO 3 /Na 2 S as the electron donor. It was found that the Cd 0.8 Zn 0.2 S had the highest photoactivity with H 2 evolution rate of 6.32 mmol g -1 h -1 . By in situ adding Pt precursors into the reaction solution, inhomogenous Pt-Cd x Zn 1- x S nanoheterostructures were formed, which accounted for a 30% enhancement for the H 2 evolution rate comparing with that of pure Cd 0.8 Zn 0.2 S nanocrystals. This work highlights the use of facile organic synthesis in combination with suitable surface modification to enhance the activity of the photocatalysts., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)