Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques.

Real-time flood forecasting is one of the most pivotal measures for flood management, and real-time error correction is a critical step to guarantee the reliability of forecasting results. However, it is still challenging to develop a robust error correction technique due to the limited cognitions of catchment mechanisms and multi-source errors across hydrological modeling. In this study, we proposed a hydrologic similarity-based correction (HSBC) framework, which hybridizes hydrological modeling and multiple machine learning algorithms to advance the error correction of real-time flood forecasting. This framework can quickly and accurately retrieve similar historical simulation errors for different types of real-time floods by integrating clustering, supervised classification, and similarity retrieval methods. The simulation errors "carried" by similar historical floods are extracted to update the real-time forecasting results. Here, combining the Xin'anjiang model-based forecasting platform with k-means, K-nearest neighbor (KNN), and embedding based subsequences matching (EBSM) method, we constructed the HSBC framework and applied it to China's Dufengkeng Basin. Three schemes, including "non-corrected" (scheme 1), "auto-regressive (AR) corrected" (scheme 2), and "HSBC corrected" (scheme 3), were built for comparison purpose. The results indicated the following: 1) the proposed framework can successfully retrieval similar simulation errors with a considerable retrieval accuracy (2.79) and time consumption (228.18 s). 2) four evaluation metrics indicated that the HSBC-based scheme 3 performed much better than the AR-based scheme 2 in terms of both the whole flood process and the peak discharge; 3) the proposed framework overcame the shortcoming of the AR model that have poor correction for the flood peaks and can provide more significant correction for the floods with bad forecasting performance. Overall, the HSBC framework demonstrates the advancement of benefiting the real-time error correction from hydrologic similarity theory and provides a novel methodological alternative for flood control and water management in wider areas.

Tunable Mechanics and Micromechanism in Close-Knit Silicide-in-SiO2 Core-Shell Nanowires.

Bending/tension mechanics is one of the core issues for nanowires in flexible free-standing transport and sensor applications, but it remains a challenge to tailor the mechanical performance beyond the inherent properties. Herein, based on structure engineering, silicon-based Mn5Si3@SiO2 nanocables are proposed and demonstrated as versatile nanosystems. Except for outstanding toughness, large ultimate strain, and great strength, they display diverse mechanical behaviors such as simplex elasticity, plasticity, and viscoelasticity under different external conditions. The tunable performances originate from synergetic effects between the core and shell components, like the atomic bonding transitional interface and space confinement, which induce optimizing internal stress distribution and the dislocation evolution mechanism in the core. The related mechanical performance is revealed carefully. The bending and tension dynamic picture, quantitative force curve, stress-strain dependence, and the corresponding lattice evolution are acquired by in/ex situ characterizations and measurements. These results contribute to nanowire mechanical design and also expand to strain-regulated three-dimensional multifunctional nanosystems.

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles.

The resolution of traffic congestion and personal safety issues holds paramount importance for human's life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm's backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

Additively manufactured bioceramic scaffolds based on triply periodic minimal surfaces for bone regeneration.

The study focused on the effects of a triply periodic minimal surface (TPMS) scaffolds, varying in porosity, on the repair of mandibular defects in New Zealand white rabbits. Four TPMS configurations (40%, 50%, 60%, and 70% porosity) were fabricated with ß-tricalcium phosphate bioceramic via additive manufacturing. Scaffold properties were assessed through scanning electron microscopy and mechanical testing. For proliferation and adhesion assays, mouse bone marrow stem cells (BMSCs) were cultured on these scaffolds. In vivo, the scaffolds were implanted into rabbit mandibular defects for 2 months. Histological staining evaluated osteogenic potential. Moreover, RNA-sequencing analysis and RT-qPCR revealed the significant involvement of angiogenesis-related factors and Hippo signaling pathway in influencing BMSCs behavior. Notably, the 70% porosity TPMS scaffold exhibited optimal compressive strength, superior cell proliferation, adhesion, and significantly enhanced osteogenesis and angiogenesis. These findings underscore the substantial potential of 70% porosity TPMS scaffolds in effectively promoting bone regeneration within mandibular defects.

α-Amylase-assisted extraction of polysaccharides from Panax ginseng.

In this paper, α-amylase-assisted extraction was used to isolate the polysaccharide that remained in hot water-extracted ginseng. The yield of the polysaccharide was 9.0%, almost equal to that of the hot water-extracted polysaccharide. Using anion exchange and gel permeation chromatography, the polysaccharide was fractionated into a neutral polysaccharide fraction and six pectic fractions. The neutral fraction accounted for 76% of the polysaccharide and contained both amylopectin and amylose. The pectic polysaccharide fractions were identified to be arabinogalactan, type-I rhamnogalacturonan and homogalacturonan-type pectin by high-performance liquid chromatography, Fourier transform-infrared and nuclear magnetic resonance analysis. Structural and lymphocyte proliferation activity results showed that these polysaccharides were different from those extracted by hot water, indicating that ginseng contains complex polysaccharides with diverse structures, which results in its diverse pharmacological activities. The α-amylase-assisted extraction is a novel method for preparing ginseng polysaccharides and could be applied toward the further study and exploration of ginseng. These findings provide technical and theoretical support for ginseng pharmacology.