Experimental and analytical studies of a CO2 laser-based flexible fabrication method for dies and molds

Laser-based flexible fabrication (LBFF), a solid freeform fabrication (SFF) method based on laser-cladding process, was developed as an alternative to conventional machining methods for producing dies and molds. LBFF is similar to processes such as LENS with additional features including shaped beam profile, quasi-coaxial powder delivery, and functionally graded materials. It uses a high-power continuous wave (CW) CO2 laser to fabricate functional tooling, dies and molds, of low surface roughness and high dimensional uniformity. It offers flexibility in designing parts with tailored materials, and in producing parts of complicated geometry.;Functionally graded molds of TiC/Ni-alloy and TiC/Ni-alloy/H13, and functional dies of H13 steel were built up by use of LBFF. Test studies on mold relief ability, strength, and dimensional stability at elevated temperatures were conducted and compared with bench mark H13 mold in gravity casting, in injection molding, and in thermal fatigue environment, respectively. Dies were also tested in aluminum extrusion under laboratory conditions. Results showed that dies and molds fabricated by LBFF had nearly full density, smooth surface (Ra \u3c 25 mum), and improved performance; the functionally graded molds had gradual change in elemental compositions in the transitional regions between distinct layers. In addition to experiments, analytical and finite element modeling of temperature distributions was performed to justify the use of shaped beam profiles in LBFF

Improved lightweight identification of agricultural diseases based on MobileNetV3

At present, the identification of agricultural pests and diseases has the problem that the model is not lightweight enough and difficult to apply. Based on MobileNetV3, this paper introduces the Coordinate Attention block. The parameters of MobileNetV3-large are reduced by 22%, the model size is reduced by 19.7%, and the accuracy is improved by 0.92%. The parameters of MobileNetV3-small are reduced by 23.4%, the model size is reduced by 18.3%, and the accuracy is increased by 0.40%. In addition, the improved MobileNetV3-small was migrated to Jetson Nano for testing. The accuracy increased by 2.48% to 98.31%, and the inference speed increased by 7.5%. It provides a reference for deploying the agricultural pest identification model to embedded devices.Comment: Accepted by CAIBDA 202

Nanostructured hydroxyapatite coating for dental and orthopedic implants

\u27A high-strength coating for dental and orthopedic implants utilizing hydroxyapatite (HAp) nanoparticles provides for a high level of osseointegration through a range of surface pore sizes in the micro- to nanoscale. Zinc oxide (ZnO) nanoparticles may be incorporated with the HAp nanoparticles to form a composite coating material, with ZnO providing infection resistance due to its inherent antimicrobial properties. A textured surface, consisting of islands of roughly square coating structures measuring about 250 .mu.m on a side, with spacing of 50-100 .mu.m therebetween, may further promote the osseointegration and antimicrobial properties of the implant coating

Transition metal oxides for high performance sodium ion battery anodes

Sodium-ion batteries (SIBs) are attracting considerable attention with expectation of replacing lithium-ion batteries (LIBs) in large-scale energy storage systems (ESSs). To explore high performance anode materials for SIBs is highly desired subject to the current anode research mainly limited to carbonaceous materials. In this study, a series of transition metal oxides (TMOs) is successfully demonstrated as anodes for SIBs for the first time. The sodium uptake/extract is confirmed in the way of reversible conversion reaction. The pseudocapacitance-type behavior is also observed in the contribution of sodium capacity. For Fe2O3anode, a reversible capacity of 386 mAh g-1at 100 mA g-1 is achieved over 200 cycles; as high as 233 mAhg-1is sustained even cycling at a large current-density of 5 A g-1

Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.Comment: 11 pages, 4 figure

Behavior Analysis and Recognition of Hidden Populations in Online Social Network Based on Big Data Method

Hidden populations refer to the minority groups that not well-known to the public. Traditional statistical survey methods are difficult to apply in the study of hidden populations because of that the hidden populations individuals are very troublesome to be found and they are not willing to share the inner opinion with the others. On the other hand, with the development of the Web 2.0, the hidden populations gather and share their views in online social networks due to the openness and anonymity of the Internet. So, this paper analyzes the behavioral characteristics of the hidden populations based on their data in online social networks. This paper uses the lesbian population as an example and analyzes the behavioral characteristics of lesbian by analyzing the data of the lesbian population in Douban Group. First, the activity data on lesbian are collected from Douban Group. Second, behavior characteristics of lesbian are analysed, the regional characteristic, temporal characteristic and text characteristic are mined out by big data method. Third, a lesbian recognition model is proposed based on the above analytical characteristics, and the effectiveness of the recognition model is varified by experiment study. The research of this paper is helpful to understand the behavioral characteristics of hidden populations deeply, and provides decision-making basis of management and service for hidden populations

MORE THAN THE TONE: THE IMPACT OF SOCIAL MEDIA OPINIONS ON INNOVATION INVESTMENTS

Social media is a valuable knowledge source for firm innovation. Extending the literature of both social media and innovation management, we attempt to examine how the valence and volume of user-generated content (UGC) from social media influence firm organizational innovation behav-iours. In this research-in-progress study, we have reviewed the existing literatures and proposed three hypotheses. Firstly, we propose that valence of UGC from social media has a U-shaped rela-tion with firm innovation investments. In particular, compared with neutral UGC, both negative and positive contents are found to push firms to invest more in innovation. Secondly, we argued that such a curvilinear relation is mitigated with an increase in volume of UGC. Last but not least, we argued that firm investment in innovation improves firm performance. To validate our pro-posed hypotheses, we have designed an innovative framework of sentiment analysis and collected a large dataset including 5-year panel with 886 listed firms and their relevant 6.2 million micro-blogs. The preliminary results from applying sentiment analysis into the collected dataset are re-ported in this study. In the future, we will validate our hypotheses with more sophisticated estima-tion models and strict robustness check. The potential contribution to theory and practice is also discussed

Key technology progress of surface extraction project deployment based on coordinated development of CBM and coal

The surface extraction of CBM in coal mine area has the multiple effects of improving the efficiency of safe coal mining, using CBM resource cleanly and reducing gas emission. From the 11th five-year plan to the 13th five-year plan period, based on the national major oil and gas projects in coal mining areas, and based on the guiding principle of coordinated development of CBM and coal in coal mining areas, the characteristics and existing problems of surface CBM extraction and deployment in coal mining areas were studied, according to the influence of coal mining, the surface extraction of CBM in coal mining area is divided into two types: pre-extraction and mining extraction. Based on the study of the objects, time and space characteristics of the two kinds of extraction methods, the engineering deployment systems of ground pre-extraction and mining extraction are summarized respectively. Based on the analysis of the present situation of CBM development technology in non-coal mining areas, the technology system of CBM surface pre-extraction in coal mining areas is sorted out, and the characteristics of CBM extraction technology and the variation regularity of key reservoir parameters are studied based on the understanding of gas migration law of permeability control, the extraction simulation and prediction method of mining is formed. According to the safety effect of surface extraction in coal mining area, the evaluation index of indirect economic benefit of surface drainage in coal mining area is put forward, which is the difference between the cost of gas disaster treatment before surface extraction and the cost of gas disaster treatment after surface extraction, a comprehensive economic evaluation method for CBM surface extraction engineering is established. The surface extraction project deployment and related technology are the key means to predict the low-cost and high-return effect of surface CBM and the coordinated development of CBM and coal resources in coal mining area

GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces

The advent of neural 3D Gaussians has recently brought about a revolution in the field of neural rendering, facilitating the generation of high-quality renderings at real-time speeds. However, the explicit and discrete representation encounters challenges when applied to scenes featuring reflective surfaces. In this paper, we present GaussianShader, a novel method that applies a simplified shading function on 3D Gaussians to enhance the neural rendering in scenes with reflective surfaces while preserving the training and rendering efficiency. The main challenge in applying the shading function lies in the accurate normal estimation on discrete 3D Gaussians. Specifically, we proposed a novel normal estimation framework based on the shortest axis directions of 3D Gaussians with a delicately designed loss to make the consistency between the normals and the geometries of Gaussian spheres. Experiments show that GaussianShader strikes a commendable balance between efficiency and visual quality. Our method surpasses Gaussian Splatting in PSNR on specular object datasets, exhibiting an improvement of 1.57dB. When compared to prior works handling reflective surfaces, such as Ref-NeRF, our optimization time is significantly accelerated (23h vs. 0.58h). Please click on our project website to see more results.Comment: 13 pages, 11 figures, refrences adde