Protein from Hylocereus polyrhizus protects MRC-5 cells against hydrogen peroxide (H2O2)-induced damage

Food Science and Biotechnology - The cytoprotective and potential molecular mechanisms of Hylocereus polyrhizus protein (RFPP) were investigated on the hydrogen peroxide (H2O2)-triggered damage in...     

石峡遗址玉琮形制与纹饰设计流变解读

通过梳理石峡遗址玉琮不同阶段形制与纹饰设计的变化,以及良渚文化和同时期其他地区玉琮的形制、纹饰和功能的分析,发现广东境内出土的玉琮与良渚文化关系极为密切,石峡遗址的多节玉琮更是直接来自于太湖地区,而那些纹饰设计出现颠倒或发生较大改变者应为本地仿制品。龙山时代,玉琮虽然传播和辐及的范围十分广泛,但是在大部分地区其功能都发生了变化,只有广东地区的玉琮仍最大程度地坚守着传统与信仰。石峡遗址玉琮形制与纹饰设计流变,所反映出的这种文化传播方式、接收程度与再创作,突出地体现了一种强势文化在对外传播和扩散的过程中,因地因时而制宜。这或许可以提示我们,利用传统文化元素进行再设计,欲意达到传统与现代的融合与契合,更理应如此。     

Integrated Inspection of QoM,QoP, and QoS for AOI Industries in Metaverses

With the rapid development of information technologies such as digital twin, extended reality, and blockchain, the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection (AutoQI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing (QoM), Quality of Products, Quality of Processes (QoP), Quality of Systems, and Quality of Services (QoS). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottom-up inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection, we propose Quality Transformers to complete detection task, while federated learning is integrated to regulate data sharing. In summary, we point out the development directions of quality inspection under metaverse tide.      

The influence of processing methods on the dielectric properties of BaTi1-xGdxO3-x/2 - Based materials

In this study, the intermediate rare-earth oxide Gd₂O₃ (Gd) was substituted in different amounts (x = 0.2–2 mol%) for the formulation of BaTi_1-xGd_xO_3-x/2 (BTG_x) dielectric materials. The effect of B-site substitution was confirmed by the additional Raman active A_1g octahedral peak at ~835cm^-1 strengthened at x ≥ 0.4 mol%. Additionally, properties of 0.9BTG_0.007-0.1BA dielectric ceramics were analysed based on the influence of various processing methods as a function of sintering temperature. The focal samples were labelled Method-A (direct-mix) and Method-B (indirect-mix). As the sintering temperature (1075–1200 °C) increased, the 1 kHz response of the ε–T curves of Method-A samples transformed from a single peak to broad-narrow double peaks of high dielectric loss tangent (tan δ). Nonetheless, samples of Method-B possessed a clearly defined transmission electron microscopy (TEM) core-shell structure, flattened double-peak ε-T curves, optimised dielectric properties (ε = ~1563–1851 and tan δ < 1.5% at room temperature), and a wide-ranging temperature behaviour that meets the X8R dielectric standards (ΔC/C_25°C < ±15%). The maximum dielectric breakdown strength of Method-B samples reached ~131 kVcm, while the energy storage density was ~0.726 J/cm³ at a maximum efficiency of ~80% at 1100 °C. Thus, exhibiting good potentials for balancing temperature stability with energy storage applications.     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

Photomemory and Pulse Monitoring Featured Solution-Processed Near-Infrared Graphene/Organic Phototransistor with Detectivity of 2.4 × 1013 Jones

Emerging graphene/organic phototransistors are eye-catching technologies owing to their unique merits including easy/low-cost fabrication, temperature independent, and achieving various functions. However, their development in the near-infrared (NIR) region is experiencing a bottleneck of inferior sensitivity due to low exciton dissociation efficiency and inefficient charge extraction rate. Here, a novel-design solution-processed graphene/organic NIR phototransistor is reported, that is, creatively introducing electron extraction layer of ZnO on graphene channel and employing organic ternary bulk heterojunction as photosensitive layer, successfully breaking that bottleneck. The phototransistor exhibits a high responsivity of 6.1 × 10⁶ A W⁻¹, a superior detectivity of 2.4 × 10¹³ Jones, and a remarkable minimum detection power of 1.75 nW cm⁻² under 850 nm radiation. Considering its excellent NIR detection performance, a noncontact transmission-type pulse monitoring is carried out with no external circuit support, from which human pulse signal and heart rate can be displayed in real time. The phototransistor, interestingly, can be switched into a photomemory function with a retention time of 1000 s in the atmosphere through a gate voltage of −20 V. The design takes the characteristics of graphene/organic phototransistors to a higher level, beyond the limit of sensitivity, and opens up a novel approach for developing multifunction devices.     

The effect of alkali metal oxide on the properties of borosilicate fireproof glass: Structure,thermal properties,viscosity, chemical stability

The purpose of the current work was to research the effect of alkali metal oxide on the structure, thermal properties, viscosity and chemical stability in the glass system (R₂O–CaO–B₂O₃–SiO₂) systematically. Because the glass would emulsify when Li₂O was added to the glass batch, this article did not discuss Li₂O. The results showed that when the amount of Na₂O was less than 4 mol.%, there was a higher interconnectivity of borate and silicate sub-networks in glass, as more mixed Si–O–B bonds were present in glass. The glass samples exhibited excellent thermal properties and chemical stabilities. As the amount of Na₂O exceeded 4 mol.%, the interconnectivity of borate and silicate sub-networks was weakened. The thermal properties and chemical stabilities of the glass samples were reduced. The connectivity of the silicate sub-network was weakened slightly as the Na/K ratio varied, and the coefficient of thermal expansion (CTE) of the glass samples gradually increased, and the resistance to thermal shock (RTS) value gradually decreased. Moreover, the viscosity of the glass samples decreased with the ratio of Na/Si and Na/K increased.