Research on Lightweight of Docking Structure Based on ANSYS

The docking structure has broad application prospects in the construction of tower assembly. However, the docking structure has a relatively large quality, which has a greater impact on the operations of high-altitude workers. This paper studies the impact force on docking structure during the docking process. The finite element analysis (FEA) model of the docking structure and the tower section is established and the impact force of the docking structure under various wording conditions is calculated. Based on the calculation results of the impact force the lightweight research of the docking structure is carried out and the optimization constraint conditions are proposed. The simulation model is established by ANSYS and the optimization design of guiding tools and vertical limit tools are completed by adjusting the structural parameters. After the optimization design, the docking structure is trial-produced and tested. Through the method of FEA and experiment, a butting device that meets the requirements of strength and rigidity and is lighter in weight is obtained. Compared with the existing docking structure, the weight of the optimized docking structure is reduced by about 39%. The research results can provide a reference for the design of the docking structure.

Multi-attribute optimization-based system decomposition considering several value chain stakeholder perspectives

The final architecture of a complex system reflect preferences of several value chain stakeholders on system attributes, also called “ilities”. Owing to differences in their individual roles and responsibilities, different stakeholders prefer different approaches to architect and decompose a system to optimize their attributes of interest. However, owing to increasing complexity of modern engineering systems, optimizing multiple attributes of complex systems has become challenging; moreover, very few researches have been published in this regard. Thus, to address this gap in available literature, this paper presents a multi-attribute optimization framework for complex system decomposition. The proposed framework primarily optimizes two attributes—system robustness (to the perspective of the stakeholder), and modularity—while system maintainability is considered an optimization constraint. Feasibility of the proposed framework has been demonstrated through a case study, wherein system attributes of three different mechanical clock models having different architectures were optimized.

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNNs) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as their relationship to current developments in neural-symbolic computing.

Mutation Maker, An Open Source Oligo Design Platform for Protein Engineering

Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multi-site random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in-silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.

Joint Vehicle and Crew Routing and Scheduling

Traditional vehicle routing problems implicitly assume that only one crew operates a vehicle for the entirety of its journey. However, this assumption is violated in many applications arising in humanitarian and military logistics. This paper considers a joint vehicle and crew routing and scheduling problem in which crews are able to interchange vehicles, resulting in space and time interdependencies between vehicle routes and crew routes. The problem is formulated as a mixed integer programming (MIP) model and a constraint programming (CP) model that overlay crew routing constraints over a standard vehicle routing problem. The constraint program uses a novel optimization constraint to detect infeasibility and to bound crew objectives. This paper also explores methods using large neighborhood search over the MIP and CP models. Experimental results indicate that modeling the vehicle and crew routing problems jointly and supporting vehicle interchanges for crews may bring significant benefits in cost reduction compared with a method that sequentializes these decisions.

Fisher-discriminative regularized latent sparse transfer model

Top performing algorithms are trained on massive amounts of labeled data. Alternatively, domain adaptation (DA) provides an attractive way to address the few labeled tasks when the labeled data from a different but related domain are available. Motivated by Fisher criterion, we present the novel discriminative regularization term on the latent subspace which incorporates the latent sparse domain transfer (LSDT) model in a unified framework. The key underlying idea is to make samples from one class closer and farther away from different class samples. However, it is nontrivial to design the efficient optimization algorithm. Instead, we construct a convex surrogate relaxation optimization constraint to ease this issue by alternating direction method of multipliers (ADMM) algorithm. Subsequently, we generalize our model in the reproduced kernel Hilbert space (RKHS) for tracking the nonlinear domain shift. Empirical studies demonstrate the performance improvement on the benchmark vision dataset Caltech-4DA.