Uncertainty evaluation of multilateration-based geometric error measurement considering the repeatibility of positioning of the machine tool

The sequential multilateration principle is often adopted in geometric error measurement of CNC machine tools. To identify the geometric errors, a single laser tracker is placed at different positions to measure the length between the target point and the laser tracker. However, the measurement of each laser tracker position is not simultaneous and measurement accuracy is mainly subject to positioning repeatability of the machine tool. This paper attempts to evaluate the measurement uncertainty of geometric errors caused by the positioning repeatability of the machine tool and the laser tracker spatial length measurement error based on the Monte Carlo method. Firstly, a direct identification method for geometric errors of CNC machine tools based on geometric error evaluation constraints is introduced, combined with the geometric error model of a three-axis machine tool. Moreover, uncertainty contributors caused by the repeatability of positioning of numerically controlled axes of the machine tool and the laser length measurement error are analyzed. The measurement uncertainty of the geometric error and the volumetric positioning error is evaluated with the Monte Carlo method. Finally, geometric error measurement and verification experiments are conducted. The results show that the maximum volumetric positioning error of the machine tool is 84.1 μm and the expanded uncertainty is 5.8 μm (�� = 2). The correctness of the geometric error measurement and uncertainty evaluation method proposed in this paper is verified compared with the direct geometric error measurement methods

Adaptive finite-time control of multi-agent systems with partial state constraints and input saturation via event-triggered strategy

This paper focuses on the finite-time control problem of multi-agent systems with input saturation, unknown nonlinear dynamics, external disturbances and partial state constraints via output feedback. Fuzzy logic system and fuzzy state observer are introduced to approximate the uncertain nonlinearities and estimate the unmeasurable states, respectively. The partial state constraints are dealt with by using the barrier Lyapunov function, so that all states of the system do not exceed the preset boundary values. In order to reduce the computational complexity of the virtual controller and save communication resources, a first-order filter and an event-triggered mechanism are introduced, respectively. It is proved that the Zeno behavior does not occur via the proposed event-triggered controller. By stability analysis, the finite-time convergence of tracking error to a small neighborhood of the origin is proven. The effectiveness of the theoretical results is verified by examples.http://wileyonlinelibrary.com/iet-cthhj2023Electrical, Electronic and Computer Engineerin

Three-phase boost-stage coupled current source inverter concept and its space vector modulation

The current source inverter (CSI) is essentially a converter with inherent boost capability and has been preliminarily applied in the field of renewable energy generation systems. However, conventional CSIs are mostly operated independently. Several existing multilevel CSI topologies entirely rely on parallel combinations, which seems to be not very suitable for capacity expansion. To solve this issue, this paper proposes a concept of three-phase boost-stage coupled current source inverter (BSC-CSI) through the duality principle, which can output multi-level currents with a reduced number of switches as well as hardware costs. Compared with the state-of-the-art CSIs, the proposed BSC-CSI can notably simplify the implementation of the multi-level modulation scheme and meanwhile ensure the power devices switch under lower current stress. To further take full advantage of the modularity and scalability, the BSC-CSI can be constructed by hybrid using silicon-carbide (SiC) and silicon (Si) based semiconductor switches for improving efficiency. The experimental results have verified the theoretical findings

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability for recommendation. The promising performance is mainly resulting from their capability of capturing high-order proximity messages over the knowledge graphs. However, training KGNNs at scale is challenging due to the high memory usage. In the forward pass, the automatic differentiation engines (\textsl{e.g.}, TensorFlow/PyTorch) generally need to cache all intermediate activation maps in order to compute gradients in the backward pass, which leads to a large GPU memory footprint. Existing work solves this problem by utilizing multi-GPU distributed frameworks. Nonetheless, this poses a practical challenge when seeking to deploy KGNNs in memory-constrained environments, especially for industry-scale graphs. Here we present TinyKG, a memory-efficient GPU-based training framework for KGNNs for the tasks of recommendation. Specifically, TinyKG uses exact activations in the forward pass while storing a quantized version of activations in the GPU buffers. During the backward pass, these low-precision activations are dequantized back to full-precision tensors, in order to compute gradients. To reduce the quantization errors, TinyKG applies a simple yet effective quantization algorithm to compress the activations, which ensures unbiasedness with low variance. As such, the training memory footprint of KGNNs is largely reduced with negligible accuracy loss. To evaluate the performance of our TinyKG, we conduct comprehensive experiments on real-world datasets. We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices

New process development and process evaluation for capturing CO2 in flue gas from power plants using ionic liquid [emim][Tf2N]

Using the ionic liquid [emim][Tf2N] as a physical solvent, it was found by aspen plus simulation that it was possible to attempt to capture CO2 from the flue gas discharged from the coal-fired unit of the power plant. Using the combination of model calculation and experimental determination, the density, isostatic heat capacity, viscosity, vapor pressure, thermal conductivity, surface tension and solubility of [emim][Tf2N] were obtained. Based on the NRTL model, the Henry coefficient and NRTL binary interaction parameters of CO2 dissolved in [emim][Tf2N] were obtained by correlating [emim][Tf2N] with the gas–liquid equilibrium data of CO2. Firstly, the calculated relevant data is imported into Aspen plus, and the whole process model of the ionic liquid absorption process is established. Then the absorption process is optimized according to the temperature distribution in the absorption tower to obtain a new absorption process. Finally, the density, constant pressure heat capacity, surface tension, thermal conductivity, viscosity of [emim][Tf2N] were changed to investigate the effect of ionic liquid properties on process energy consumption, solvent circulation and heat exchanger design.The results showed that based on the composition of the inlet gas stream to the absorbers, CO2 with a capture rate of 90% and a mass purity higher than 99.5% was captured; These results indicate that the [emim][Tf2N] could be used as a physical solvent for CO2 capture from coal-fired units.In addition,The results will provide a theoretical basis for the design of new ionic liquids for CO2 capture

An Evil Backstage Manipulator: Psychological Factors Correlated with Health-Related Quality of Life in Chinese Patients with Crohn's Disease

Health-related quality of life (HRQoL) is recommended as one of essential parameters to evaluate treatment effect and clinical outcome in patients with Crohn's disease (CD). Recent studies reported that psychological factors might play a role in HRQoL in Western and American CD patients. Sufficient evidences in Chinese CD patients are still unavailable. This study is dedicated to investigate the correlation of various psychological factors with HRQoL in Chinese CD patients. We prospectively collected 40 active and 40 quiescent CD patients in China and found that psychological factors, especially neuroticism and anxiety, significantly correlate with and affect HRQoL in both active and quiescent CD groups. This is the first report revealing correlation between psychological factors and HRQoL in Chinese CD patients. Therefore, we assume that our results can contribute to a better understanding of etiology and tailoring of management in Chinese patients with Crohn's disease and are beneficial to our colleagues to compare the heterogeneous characteristics of Crohn's disease in different ethnic groups

COVID-19 Epidemic Peer Support and Crisis Intervention Via Social Media

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.This article describes a peer support project developed and carried out by a group of experienced mental health professionals, organized to offer peer psychological support from overseas to healthcare professionals on the frontline of the COVID-19 outbreak in Wuhan, China. This pandemic extremely challenged the existing health care systems and caused severe mental distress to frontline healthcare workers. The authors describe the infrastructure of the team and a novel model of peer support and crisis intervention that utilized a popular social media application on smartphone. Such a model for intervention that can be used elsewhere in the face of current global pandemic, or future disaster response

QTL Mapping for Grain Zinc and Iron Concentrations in Bread Wheat

Deficiency of micronutrient elements, such as zinc (Zn) and iron (Fe), is called “hidden hunger,” and bio-fortification is the most effective way to overcome the problem. In this study, a high-density Affymetrix 50K single-nucleotide polymorphism (SNP) array was used to map quantitative trait loci (QTL) for grain Zn (GZn) and grain Fe (GFe) concentrations in 254 recombinant inbred lines (RILs) from a cross Jingdong 8/Bainong AK58 in nine environments. There was a wide range of variation in GZn and GFe concentrations among the RILs, with the largest effect contributed by the line × environment interaction, followed by line and environmental effects. The broad sense heritabilities of GZn and GFe were 0.36 ± 0.03 and 0.39 ± 0.03, respectively. Seven QTL for GZn on chromosomes 1DS, 2AS, 3BS, 4DS, 6AS, 6DL, and 7BL accounted for 2.2–25.1% of the phenotypic variances, and four QTL for GFe on chromosomes 3BL, 4DS, 6AS, and 7BL explained 2.3–30.4% of the phenotypic variances. QTL on chromosomes 4DS, 6AS, and 7BL might have pleiotropic effects on both GZn and GFe that were validated on a germplasm panel. Closely linked SNP markers were converted to high-throughput KASP markers, providing valuable tools for selection of improved Zn and Fe bio-fortification in breeding

Global dispersal and adaptive evolution of domestic cattle: a genomic perspective

Domestic cattle have spread across the globe and inhabit variable and unpredictable environments. They have been exposed to a plethora of selective pressures and have adapted to a variety of local ecological and management conditions, including UV exposure, diseases, and stall-feeding systems. These selective pressures have resulted in unique and important phenotypic and genetic differences among modern cattle breeds/populations. Ongoing efforts to sequence the genomes of local and commercial cattle breeds/populations, along with the growing availability of ancient bovid DNA data, have significantly advanced our understanding of the genomic architecture, recent evolution of complex traits, common diseases, and local adaptation in cattle. Here, we review the origin and spread of domestic cattle and illustrate the environmental adaptations of local cattle breeds/populations

Structural variation and introgression from wild populations in East Asian cattle genomes confer adaptation to local environment

BACKGROUND: Structural variations (SVs) in individual genomes are major determinants of complex traits, including adaptability to environmental variables. The Mongolian and Hainan cattle breeds in East Asia are of taurine and indicine origins that have evolved to adapt to cold and hot environments, respectively. However, few studies have investigated SVs in East Asian cattle genomes and their roles in environmental adaptation, and little is known about adaptively introgressed SVs in East Asian cattle. RESULTS: In this study, we examine the roles of SVs in the climate adaptation of these two cattle lineages by generating highly contiguous chromosome-scale genome assemblies. Comparison of the two assemblies along with 18 Mongolian and Hainan cattle genomes obtained by long-read sequencing data provides a catalog of 123,898 nonredundant SVs. Several SVs detected from long reads are in exons of genes associated with epidermal differentiation, skin barrier, and bovine tuberculosis resistance. Functional investigations show that a 108-bp exonic insertion in SPN may affect the uptake of Mycobacterium tuberculosis by macrophages, which might contribute to the low susceptibility of Hainan cattle to bovine tuberculosis. Genotyping of 373 whole genomes from 39 breeds identifies 2610 SVs that are differentiated along a "north-south" gradient in China and overlap with 862 related genes that are enriched in pathways related to environmental adaptation. We identify 1457 Chinese indicine-stratified SVs that possibly originate from banteng and are frequent in Chinese indicine cattle. CONCLUSIONS: Our findings highlight the unique contribution of SVs in East Asian cattle to environmental adaptation and disease resistance