Using predictions for the sea surface temperature (SST) generated by a Flexible Global Ocean-AtmosphereLand System model of IAP/LASG (FGOALS-g), the season-dependent predictability of SST anomalies for El Nino/La Nina events is investigated by analyzing the forecast error growth in an imperfect model scenario. The results indicate that, for the predictions through the spring season in the growth phase of El Nino events, the prediction errors induced by both initial errors and model errors tend to have a prominent season-dependent evolution and yield a prominent spring predictability barrier (SPB). For the decay-phase predictions of El Nino events, a less prominent season-dependent evolution of prediction errors and then a less prominent SPB are observed. For the growth-and decay-phase predictions of La Nina events, the prediction errors do not exhibit a significant season-dependent evolution and yield a less prominent SPB phenomenon. These results indicate that the SPB phenomenon depends remarkably on the ENSO events themselves, particularly the phases of the El Nino/La Nina events. We also report that the initial SST errors that correspond to a significant SPB for El Nino events tend to have the dominant modes in a large-scale dipolar pattern with negative anomalies in the equatorial central-western Pacific and positive anomalies in the eastern Pacific, or vice versa. We further demonstrate that the error growth related to a significant SPB for El Nino prediction generated by the FGOALS-g model can result from two dynamical mechanisms: in one case, the prediction errors grow in a manner similar to El Niño; in the other, the prediction errors develop with a tendency opposite to El Niño.

Selective Laser Melting (SLM) is a powder bed layer-by-layer fusion technique mainly applied for additive manufacturing of 3D metallic components of complex geometry. However, the technology is currently limited to printing a single material across each layer. In many applications such as the manufacture of certain aero engine components, conformably cooled dies, medical implants and functional gradient structures, printing of multiple materials are desirable. This paper reports an investigation into the 3D printing of multiple metallic materials including 316L stainless steel, In718 nickel alloy and Cu10Sn copper alloy within a single build-up process using a specially designed multiple material SLM system combining powder-bed with point by point powder dispensing and selective material removal, for the first time. Material delivery system design, multiple material interactions, and component characteristics are described and the associated mechanisms are discussed. 3D printing, selective laser melting, multiple materials 2. Experimental materials and procedure 2.1. Materials Gas atomized spherical 316L stainless steel powder (LPW-718-AACF, 10-45 µm, LPW Technology Ltd., UK), In718 nickel alloy powder (LPW-316-AAHH, 10-45 µm LPW Technology Ltd., UK), Contents lists available at SciVerse ScienceDirect CIRP Annals Manufacturing Technology

Additive manufacturing (AM) is an emerging customized three-dimensional (3D) functional product fabrication technology. It provides a higher degree of design freedom, reduces manufacturing steps, cost and production cycles. However, existing metallic component 3D printing techniques are mainly for the manufacture of single material components. With the increasing commercial applications of AM technologies, the need for 3D printing of more than one type of dissimilar materials in a single component increases. Therefore, investigations on multi-material AM (MMAM) emerge over the past decade. Lasers are currently widely used for the AM of metallic components where high temperatures are involved. Here we report the progress and trend in laser-based macro- and micro-scale AM of multiple metallic components. The methods covered in this paper include laser powder bed fusion, laser powder directed energy deposition, and laser-induced forward transfer for MMAM applications. The principles and process/material characteristics are described. Potential applications and challenges are discussed. Finally, future research directions and prospects are proposed.

In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label‐free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep‐learning algorithm for classifying HCC differentiation to produce an innovative computer‐aided diagnostic method. Convolutional neural networks based on the VGG‐16 framework were trained using 217 combined two‐photon excitation fluorescence and second‐harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label‐free, automated methods for various tissues, diseases and other related classification problems.

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Multi-material additive manufacturing provides a new route for fabricating components with tailored physical properties. Laser-based powder bed fusion (L-PBF), also known as selective laser melting, is a powder bed-based additive manufacturing technology. This technology affords the advantage of manufacturing metallic and non-metallic materials with high geometrical resolution. An emerging field relevant to the foregoing is multi-material L-PBF. This paper reviews the latest progress in this field including multiple material powder deposition mechanisms, molten pool behaviour, process characteristics of printing metal-metal, metal-ceramic, and metal-polymer multiple material components, and potential applications. Finally, scientific and technological challenges are presented.

Production of functionally graded materials (FGMs, i.e., a gradual transition from one material to another) and components is challenging using conventional manufacturing techniques. Additive manufacturing (AM) provides a new opportunity for producing FGMs. However, current metal AM technologies including powder-bed fusion are limited to producing single material components or vertical FGM parts, i.e., a different material composition in different layers but not within the same layer, and in situ changing materials is challenging. In this paper, we demonstrate the fabrication of horizontal and 3D 316L/Cu10Sn components with FGM within the same layer and in different layers, via a proprietary multiple selective powder delivery array device incorporated into a selective laser melting system that allowed the deposition of up to six different materials point by point. The manufactured component macrostructure, microstructure, microhardness, and phases were examined. Smooth transition from one material to the other was realized. Also, an interesting phenomenon was found that the maximum hardness was at 50% 316L and 50% Cu10Sn. The work would open up a new opportunity for the manufacturing of true 3D functionally graded components using additive manufacturing and for the rapid development of new metal alloy systems.

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