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Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification., Author(s): Zheng-Yong Zhang [sup.1] , Xiao-Jing Shi [sup.1] , Ya-Ju Zhao [sup.2] , Yin-Sheng Zhang [sup.2] , Hai-Yan Wang [sup.2] Author Affiliations: (1) grid.440844.8, 0000 0000 8848 7239, School of [...]

In traditional body-centered cubic (bcc) metals, the core properties of screw dislocations play a critical role in plastic deformation at low temperatures. Recently, much attention has been focused on refractory high-entropy alloys (RHEAs), which also possess bcc crystal structures. However, unlike face-centered cubic high-entropy alloys (HEAs), there have been far fewer investigations into bcc HEAs, specifically on the possible effects of chemical short-range order (SRO) in these multiple principal element alloys on dislocation mobility. Here, using density functional theory, we investigate the distribution of dislocation core properties in MoNbTaW RHEAs alloys, and how they are influenced by SRO. The average values of the core energies in the RHEA are found to be larger than those in the corresponding pure constituent bcc metals, and are relatively insensitive to the degree of SRO. However, the presence of SRO is shown to have a large effect on narrowing the distribution of dislocation core energies and decreasing the spatial heterogeneity of dislocation core energies in the RHEA. It is argued that the consequences of the mechanical behavior of HEAs is a change in the energy landscape of the dislocations, which would likely heterogeneously inhibit their motion.

Bouligand structures are widely observed in natural materials; elasmoid fish scales and the exoskeleton of arthropods, such as lobsters, crabs, mantis shrimp and insects, are prime examples. In fish scales, such as those of the Arapaima gigas, the tough inner core beneath the harder surface of the scale displays a Bouligand structure comprising a layered arrangement of collagen fibrils with an orthogonal or twisted staircase (or plywood) architecture. A much rarer variation of this structure, the double-twisted Bouligand structure, has been discovered in the primitive elasmoid scales of the coelacanth fish; this architecture is quite distinct from “modern” elasmoid fish scales yet provides extraordinary resistance to deformation and fracture. Here we examine the toughening mechanisms created by the double-twisted Bouligand structure in comparison to those generated by the more common single Bouligand structures. Specifically, we have developed an orientation-dependent, hyperelastic, phase-field fracture mechanics method to computationally examine the relative fracture toughness of elasmoid fish scales comprising single vs. double-twisted Bouligand structures of fibrils. The model demonstrates the critical role played by the extra inter-bundle fibrils found in coelacanth fish scales in enhancing the toughness of Bouligand-type structures. Synthesis and fracture tests of 3-D printed Bouligand-type materials are presented to support the modeling and complement our understanding of the fracture mechanisms in Bouligand-type structures.

Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human–robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human–robot collaborative disassembly of EoL products. The adopted criteria for human–robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human–robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human–robot collaborative disassembly solutions. [ABSTRACT FROM AUTHOR]

Glycolysis is one of the key metabolic reprogramming characteristics of ovarian cancer. Ursolic Acid (UA), as a natural compound, exerts a beneficial regulatory effect on tumor metabolism. In this study, we have confirmed through RNA-seq analysis and a series of in vitro and in vivo functional experiments that UA significantly inhibits ovarian cancer cell proliferation, promotes tumor apoptosis, and reduces glycolysis levels. Additionally, it demonstrates synergistic therapeutic effects with cisplatin in both in vitro and in vivo experiments. Furthermore, at the molecular level, we found that UA inhibits glycolysis in ovarian cancer by binding to the transcription factor KLF5 and blocking the transcriptional expression of the downstream PI3K/AKT signaling pathway, thereby exerting its therapeutic effect. In conclusion, our research indicates that UA can inhibit the proliferation, apoptosis, and glycolysis levels of ovarian cancer cells through the KLF5/PI3K/AKT signaling axis. Our findings offer a new perspective on the therapeutic application of the natural compound UA in ovarian cancer and support its potential development as a candidate for chemotherapy. [ABSTRACT FROM AUTHOR]