Micro Thin Film Sensor Embedding in Metal Structures for Rapid Production of Miniature Smart Metal Tooling

In-situ monitoring and control of temperature and strain is important to improve product quality for numerous mesoscale manufacturing processes. However, it is difficult for conventional sensors to provide measurements with a high spatial and temporal resolution at critical locations. This paper studies the fabrication and calibration of micro thin film sensors embedded in metal structures for miniature tooling applications. Micro thin film sensors have been successfully fabricated on various metal substrates and advanced embedding techniques have been developed to ensure sensor function inside metal structures. Specifically, multilayer dielectric/metal thin film micro sensors were embedded into layered metal structures by ultrasonic welding (USW). These embedded sensors provided superior spatial and temporal resolutions. Smart tooling technique will improve safety and reliability significantly for manufacturing processes.Mechanical Engineerin

Learning context-aware outfit recommendation

With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching

Dynamic Graph Attention-Aware Networks for Session-Based Recommendation

Graph convolutional neural networks have attracted increasing attention in recommendation system fields because of their ability to represent the interactive relations between users and items. At present, there are many session-based methods based on graph neural networks. For example, SR-GNN establishes a user’s session graph based on the user’s sequential behavior to predict the user’s next click. Although these session-based recommendation methods modeling the user’s interaction with items as a graph, these methods have achieved good performance in improving the accuracy of the recommendation. However, most existing models ignore the items’ relationship among sessions. To efficiently learn the deep connections between graph-structured items, we devised a dynamic attention-aware network (DYAGNN) to model the user’s potential behavior sequence for the recommendation. Extensive experiments have been conducted on two real-world datasets, the experimental results demonstrate that our method achieves good results in capturing user attention perception

Rapid Fabrication of Smart Tooling with Embedded Sensors by Casting in Molds Made by Three Dimensional Printing

This paper is to investigate the feasibility of constructing “smart tooling” by embedding thin film sensors, specifically, thin film thermocouples (TFTC) in castings made by molds formed by 3 Dimensional Printing (3DP). This study investigates whether thin film sensors can effectively be cast into larger metal structures and if the sensors survive the casting process. The investigation includes making 3DP molds to produce cast lap joint test bars of aluminum A356 and electroplated nickel to characterize by mechanical testing to find the best process conditions to maximize bond strength between the embedded thin film sensors and the cast material. Lastly molds were made and embedded sensors were placed inside the mold for casting. Some of the embedded sensors survived the casting process. In-situ monitoring of casting process with the embedded sensors was accomplished.Mechanical Engineerin

Proving Secure Properties of Cryptographic Protocols with Knowledge Based Approach

Cryptographic protocols have been widely used to protect communications over insecure network environments. Existing cryptographic protocols usually contain flaws. To analyze these protocols and find potential flaws in them, the secure properties of them need be studied in depth. This paper attempts to provide a new framework to analyze and prove the secure properties in these protocols. A number of predicates and action functions are used to model the network communication environment. Domain rules are given to describe the transitions of principals\u27 knowledge and belief states. An example of public key authentication protocols has been studied and analysed

3-Methyl-4-nitro­phenol

In the title mol­ecule, C7H7NO3, the nitro group is oriented at 14.4 (3)° with respect to the plane of the benzene ring. The crystal structure is stabilized by O—H⋯O hydrogen bonds and further consolidated by C—H⋯O inter­actions