A novel method to fabricate complex three-dimensional microstructures

Conventional three-dimensional (3D) microstructures such as arcs or spiralities are generally fabricated using some complicated methods like LIGA or two-photon lithography. In this paper, a new approach of fabricating 3D microstructures is provided. The process is based on UV-LIGA technology yet including a novel reformation method in the post bake procedure. The fabrication process started with coating SU-8 as thick as 500 microns on the silicon substrate, and then it was followed by an exposure with patterned mask under UV light. Subsequently, a force on the exposed SU-8 photo resist was applied in the post-bake process. By adjusting the amount of force, the way in which the force was placed and the exposure dose, we directly fabricated some complicated three-dimensional structures on the SU-8 photo resist after development of the SU-8. We call this microfabrication method as Force-LIGA (F-LIGA). Firstly, orthogonal experiment method conducted to optimize the hot-press process is presented, and then we give some experiment examples using F-LIGA approach and discuss the relationships among the exposure time, pressure and the profile of microstructures. The fabrication process can be used widely in making useful three-dimensional devices.     

Degradation of Phenolic Compounds in Coal Gasification Wastewater by Biofilm Reactor with Isolated Klebsiella sp.

This study was conducted to evaluate the degradation of phenolic compounds by one strain isolated from coal gasification wastewater（ CGW）. 16S rRNA gene sequences homology and phylogenetic analysis showed that the isolate is belonged to the genus Klebsiella sp. The effect of different phenolic compounds on the isolate was investigated by determining OD600and phenoloxidase activity,of which the results showed that the isolate can utilize phenol,4-methyl phenol,3,5-dimethyl phenol and resorcinol as carbon resources. The biofilm reactor（ formed by the isolate） can resist the influent concentration of phenolic compounds as high as750 mg /L when fed with synthetic CGW and incubated at optimum conditions. The capacity of improving the biodegradability of CGW through degrading phenolic compounds was testified with fed the biofilm reactor with real CGW. Thus,it might be an effective strain for bioaugmentation of CGW treatment.     

Design of Two-Step Random Access Procedure for URLLC Applications

The idea of Smart City incorporates a few ideas being technology, economy, governance, people, management, and infrastructure. This implies a Smart City can have distinctive communication needs. Wireless technologies, for example, WiFi, Zig Bee, Bluetooth, WiMax, 4G or LTE have introduced themselves as a solution for the communication in Smart City activities. Nonetheless, as the majority of them utilize unlicensed interference, coexistence and bands issues are increasing. So to solve the problem IoT is used in smart cities. This paper addresses the issues of both resource allocation and routing to propose an energy efficient, congestion aware resource allocation and routing protocol (ECRR) for IoT network based on hybrid optimization techniques. The first contribution of proposed ECRR technique is to employ the data clustering and metaheuristic algorithm for allocate the large-scale devices and gateways of IoT to reduce the total congestion between them. The second contribution is to propose a queue based swarm optimization algorithm for select a better route for future route based on multiple constraints, which improves the route discovering mechanism. The proposed ECRR technique is implemented in Network Simulator (NS-2) tool and the simulation results are compared with the existing state-of-art techniques in terms of energy consumption, node lifetime, throughput, end-to-end delay, packet delivery ratio and packet overheads.

     

Elastic structural analysis based on graph neural network without labeled data

Artificial intelligence is gaining increasing popularity in structural analysis. However, at the structural system level, the appropriateness of data representation, the paucity of data, and the physical interpretability of results are rarely studied and remain profound challenges. To fill such gaps, a physics-informed model named StructGNN-E (i.e., structural analysis based on graph neural network [GNN]–elastic) based on the GNN architecture, which is capable of implementing the elastic analysis of structural systems without labeled data, is proposed in this study. The systems with structural topologies and member configurations are organized as graph data and later processed by a modified graph isomorphism network. Moreover, to avoid dependence on big data, a novel physics-informed paradigm is proposed to incorporate mechanics into deep learning (DL), ensuring the theoretical correctness of the results. Numerical experiments and ablation studies demonstrate the unique effectiveness of StructGNN-E against common DL models, with an average accuracy of 99% and excellent computational efficiency. Due to its differentiability, StructGNN-E is promising for bidirectionally linking structural parameters and analysis results, paving the way for a new end-to-end structural optimization method in the future.