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Since the first Bulk metallic glasses (BMGs) were synthesized in 1989, [1] striking progress has been made in the preparation of BMG. A great number of multicomponent metallic glass alloys with strong glass-forming ability (GFA) and high thermal stability have been discovered, including Ti, Zr, Fe, Pd, Mg, Cu, Ni, and other metallic glass alloy systems. [2][3][4] BMGs have attracted much attention due to their unique properties, for example, their superior strength and high hardness, excellent corrosion resistance and high wear resistance. Recently, many techniques have been used successfully to prepare Ti-based BMGs, but most of the research efforts and industrial interests are focused on different implementations of rapid solidification. [5] An alternative method uses solid-state amorphization processing, as in mechanical alloying (MA), to prepare amorphous powders that are suitable for further compaction and densification. Meanwhile, reinforced particles can be introduced easily into the glassy matrix. As previous investigations have demonstrated, MA has been used successfully to prepare amorphous Cu-and Zr-based composite powders. [6,7] Carbon nanotubes (CNTs) have been one of the most actively studied materials in contemporary research. These structures have good chemical and physical properties potential applications in many different fields. CNTs, with their superb electronic, mechanical, and structural characteristics, are strong candidate components for new functional devices, such as field emission displays, nanoscale transistors supercapacitors, [6][7][8] secondary batteries, and various composites. [8][9][10] Even though BMGs and CNTs have many excellent properties, a literature survey indicated that no report exists on the formation of a Ti-based BMG composite powder containing CNTs using the MA process. In this work, amorphous Ti 50 Cu 28 Ni 15 Sn 7 powders with and without CNT additions will be prepared using MA. Subsequent consolidation of asmilled powders will be performed and the mechanical property of compacts will be evaluated by a Vickers microhardness and compressive tests. ExperimentalA mixture of elemental metallic powders with a nominal composition of Ti 50 Cu 28 Ni 15 Sn 7 (in at. %) was mechanically alloyed with or without the addition of CNT powders. The milling was performed in a SPEX 8016 shaker ball mill under an Ar-filled atmosphere. The overall mechanical alloying process lasted 8 hrs and was interrupted every 15 min for the first hour and every 30 min thereafter. Each interruption was followed by an equal length of time (30 min) to cool down the vials, and then a suitable amount of the as-milled powders was extracted, to examine the progress of amorphization reaction. Specific details of the mechanical alloying process are described elsewhere. [6] The as-milled composite powders were consolidated in a vacuum hot pressing machine to prepare bulk samples with a 10 mm diameter and 2 mm thickness. The structures of the as-milled and bulk samples were analyzed using an X-ray diffracto...

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This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.

GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. Although much progress has been made in exploring the integration of AI and Geography, there is yet no clear definition of GeoAI, its scope of research, or a broad discussion of how it enables new ways of problem solving across social and environmental sciences. This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages over traditional methods. We organize this review of GeoAI research according to different kinds of image or structured data, including satellite and drone images, street views, and geo-scientific data, as well as their applications in a variety of image analysis and machine vision tasks. While different applications tend to use diverse types of data and models, we summarized six major strengths of GeoAI research, including (1) enablement of large-scale analytics; (2) automation; (3) high accuracy; (4) sensitivity in detecting subtle changes; (5) tolerance of noise in data; and (6) rapid technological advancement. As GeoAI remains a rapidly evolving field, we also describe current knowledge gaps and discuss future research directions.

The preparation of Ti50Cu28Ni15Sn7 metallic glass composite powders was accomplished by the mechanical alloying of a pure Ti, Cu, Ni, Sn and carbon nanotube (CNT) powder mixture after 8 h milling. In the ball-milled composites, the initial CNT particles were dissolved in the Ti-based alloy glassy matrix. The thermal stability of the amorphous matrix is affected by the presence of the CNT particles. Changes in Tg and Tx suggest deviations in the chemical composition of the glassy matrix due to a partial dissolution of the CNT species in the amorphous phase. The bulk metallic glass composite was successfully prepared by vacuum hot pressing the as-milled CNT/ Ti50Cu28Ni15Sn7 metallic glass composite powders. A significant hardness increase with the CNT additions was observed for the consolidated composite compacts.