Intelligent optimal control considering dynamic posture compensation for a cantilever roadheader

The control of dynamic spatial posture for cantilever roadheader is one of the vital problems for intelligent mining, which directly affect the forming quality of cutting tunnel. Therefore, this paper proposed an intelligent optimal combination compensation strategy to adjust the real-time dynamic posture of cantilever roadheader. First, based on the topological structure analysis of cantilever roadheader, the structural loop compensation model for spatial posture deviation was established. Afterward, the principal component analysis (PCA) and multi-objective particle swarm optimization (MPSO) algorithm were applied to improve the analysis speed and accuracy of posture deviation. Finally, parallel dynamic cooperative optimization (PDCO) strategy was combined to achieve the accurate adjusting of posture deviation. The actual experimental and application results indicate that the intelligent optimal combination compensation strategy proposed in the paper can significantly improve the accuracy of the cutting tunnel. The intelligent optimal compensation strategy proposed in this paper transforms the transient spatial posture deviation into structural loop compensation, and implements by parallel cooperative strategy, finally to realize the fast analysis and efficient implementation of spatial dynamic posture deviation for cantilever roadheader during cutting process. The work of this paper provides an effective reference for intelligent deep and remote underground mining, and it can also be applied to effective control of dynamic spatial posture for intelligent engineering machinery products.

The Involvement of the hsa_circ_0088494-miR-876-3p-CTNNB1/CCND1 Axis in Carcinogenesis and Progression of Papillary Thyroid Carcinoma

Recently, growing studies have demonstrated that circular RNAs (circRNAs) function as critical players in multiple human tumors, including papillary thyroid carcinoma (PTC). However, the expression and underlying potential mechanism of circRNAs in PTC are still not fully elucidated. In this study, 14 candidate differentially expressed circRNAs (DECs) between normal thyroid tissues and benign thyroid tissues or PTC were first screened using the GSE93522 dataset by the GEO2R online tool. Then, the structural loop graphs of these 14 circRNAs were obtained through the CSCD database. After performing miRNA co-prediction by combination of CSCD and CRI databases, a potential circRNA-miRNA sub-network, consisting of 9 circRNAs and 21 miRNAs, was successfully constructed. Subsequently, the expression and prognostic values of these miRNAs were further determined by starBase, and two miRNAs, namely, miR-605-5p and miR-876-3p, were identified as key miRNAs in PTC. Then, their downstream target genes were predicted by the miRNet database. CTNNB1 and CCND1 were found to be two most potential targets of miR-876-3p by combination of multiple in silico analyses, including protein–protein interaction (PPI), hub gene screening, correlation analysis, and expression analysis. Conclusively, we established a key hsa_circ_0088494-miR-876-3p-CTNNB1/CCND1 axis linked to carcinogenesis and progression of PTC, which may provide promising therapeutic targets in treating PTC in the future.

Structural modeling of 2019-novel coronavirus (nCoV) spike protein reveals a proteolytically-sensitive activation loop as a distinguishing feature compared to SARS-CoV and related SARS-like coronaviruses

The 2019 novel coronavirus (2019-nCoV) is currently causing a widespread outbreak centered on Hubei province, China and is a major public health concern. Taxonomically 2019-nCoV is closely related to SARS-CoV and SARS-related bat coronaviruses, and it appears to share a common receptor with SARS-CoV (ACE-2). Here, we perform structural modeling of the 2019-nCoV spike glycoprotein. Our data provide support for the similar receptor utilization between 2019-nCoV and SARS-CoV, despite a relatively low amino acid similarity in the receptor binding module. Compared to SARS-CoV, we identify an extended structural loop containing basic amino acids at the interface of the receptor binding (S1) and fusion (S2) domains, which we predict to be proteolytically-sensitive. We suggest this loop confers fusion activation and entry properties more in line with MERS-CoV and other coronaviruses, and that the presence of this structural loop in 2019-nCoV may affect virus stability and transmission.

Modelling and characterisation of geometric errors on 5-axis machine-tool

This research work deals with the geometric modelling of 5-axis machine tool based on a standardised parameterisation of geometric errors with the aim to decrease the volumetric error in the workspace. The identification of the model’s parameters is based on the development of a new standard thermo-invariant material namely the Multi-Feature Bar. Thanks to its calibration and a European intercomparison, it now provides a direct metrological traceability to the SI metre for dimensional measurement on machine tool in a hostile environment. The identification of three intrinsic parameters of this standard, coupled with a measurement procedure ensures a complete and traceable identification of motion errors of linear axes. An identification procedure of location and orientation errors of axes is proposed by probing a datum sphere in the workspace and minimising the time drift of the structural loop and the effects of the previously identified motion errors. Finally, the developed model partially identified, allows the characterisation of 95% of the measured volumetric error. Therefore, the mean volumetric error not characterised by the model only amounts to 8 μm.

Simplifications of the volumetric error model because of the structural loop of machine tools

After many years of intensive work the international experts from ISO TC 39 published the technical report called ISO TR 16907 “Machine tools – numerical compensation of geometric errors”. This document defines the terminology, presents benefits and limitations of numerical compensation of machine tools’ and measuring machines’ errors. It gives machines manufacturers and users vital information about how to use numerical compensation. In the context of those types of compensation defined in ISO TR 16907, this article shows rules of selecting models of Volumetric Error for three-axis machine tools. What is more, this paper presents some principles of reduction of these proposed models because of the functional tasks for machine tools. One of the obtained results is an array of reduced models for three-axis machine tools. This array determines the degree of detail of the model and the experimental research program that needs to be carried out in order to determine the Volumetric Error distribution.

Relative Robots: Scaling Automated Assembly of Discrete Cellular Lattices

We propose metrics for evaluating the performance of robotically assembled discrete cellular lattice structures (referred to as digital materials) by defining a set of tools used to evaluate how the assembly system impacts the achievable performance objective of relative stiffness. We show that mass-specific stiffness can be described by the dependencies E*(γ, D(n, f, RA)), where E* is specific modulus, γ is lattice topology, and the allowable acceptance of the joint interface, D, is defined by an error budget analysis that incorporates the scale of the structure, and/or number of discrete components assembled, n, the type of robotic assembler, RA, and the static error contributions due to tolerance stack-up in the specified assembler structural loop, and the dynamic error limitations of the assembler operating at specified assembly rates, f. We refer to three primary physical robotic construction system topologies defined by the relationship between their configuration workspace, and the global configuration space: global robotic assembler (GR), mobile robotic assembler (MR), and relative robotic assemblers (RR), each exhibiting varying sensitivity to static, and dynamic error accumulation. Results of this analysis inform an iterative machine design process where final desired material performance is used to define robotic assembly system design parameters.