Pre-compensation of Friction for CNC Machine Tools through Constructing a Nonlinear Model Predictive Scheme

Nonlinear friction is a dominant factor afecting the control accuracy of CNC machine tools.This paper proposes a friction pre-compensation method for CNC machine tools through constructing a nonlinear model predictive scheme.The nonlinear friction-induced tracking error is frstly modeled and then utilized to establish the nonlinear model predictive scheme,which is subsequently used to optimize the compensation signal by treating the friction-induced tracking error as the optimization objective.During the optimization procedure,the derivative of compensation signal is constrained to avoid vibration of machine tools.In contrast to other existing approaches,the proposed method only needs the parameters of Stribeck friction model and an additional tuning parameter,while fnely identifying the parameters related to the pre-sliding phenomenon is not required.As a result,it greatly facilitates the practical applicability.Both air cutting and real cutting experiments conducted on an in-house developed open-architecture CNC machine tool prove that the proposed method can reduce the tracking errors by more than 56%,and reduce the contour errors by more than 50%.

Image Reconstruction for ECT under Compressed Sensing Framework Based on an Overcomplete Dictionary

Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed environments.In this paper,compressed sensing(CS)theory based on dictionary learning is introduced to the inverse problem of ECT,and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space.Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT,it is not necessary to rely on the sparsity of coefficient vector to solve the nonlinear mapping as most algorithms based on CS theory.Two-phase flow distribution in a cylindrical pipe was modeled and simulated,and three variations without sparse constraint based on Landweber,Tikhonov,and Newton-Raphson algorithms were used to rapidly reconstruct a 2-D image.

Visualization Detection of Solid-Liquid Two-Phase Flow in Filling Pipeline by Electrical Capacitance Tomography Technology

During mine filling,the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion.Therefore,the visualization of the innermine filling of the solid-liquid two-phase flow in the pipeline is important.This paper proposes a method based on capacitance tomography for the visualization of the solid-liquid distribution on the section of a filling pipe.A feedback network is used for electrical capacitance tomography reconstruction.This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.In the reconstruction process,the error in the linear back projection is removed;thus,the reconstruction problem becomes an accurate linear problem.The simulation results showthat the reconstruction accuracy of this algorithm is better than that of many traditional algorithms;furthermore,the reconstructed image artifacts are fewer,and the phase distribution boundary is clearer.This method can help determine the location and size of the caking and waste rock in the cross section of the pipeline more accurately and has great application prospects in the visualization of filling pipelines in mines.

Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms

Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards.However,few computational modeling studies for immunotoxicity were reported,with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity.In this study,we employed a data-driven quantitative structure–activity relationship(QSAR)modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity.To this end,a probe data set of 6,341 chemicals was obtained from a high-throughput screening(HTS)assay testing for the activation of the aryl hydrocarbon receptor(AhR)signaling pathway,a key event leading to immunotoxicity.Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds.100 assays were selected to develop QSAR models based on their correlations to AhR agonism.Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints.5-fold cross-validation of the resulting models showed good predictivity(average CCR=0.73).A total of 20 assays were further selected based on QSAR model performance,and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals.This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints,which have limited training data and complicated toxicity mechanisms.

The Complement System in Liver Diseases

The complement system plays an important role in mediating both acquired and innate responses to defend against microbial infection, and in disposing immunoglobins and apoptotic cells. The liver （mainly hepatocytes） is responsible for biosynthesis of about 80-90% of plasma complement components and expresses a variety of complement receptors. Recent evidence from several studies suggests that the complement system is also involved in the pathogenesis of a variety of liver disorders including liver injury and repair, fibrosis, viral hepatitis, alcoholic liver disease, and liver ischemia/reperfusion injury. In this review, we will discuss the potential role of the complement system in the pathogenesis of liver diseases. Cellular ＆ Molecular Immunology.

认知规律启发的物体分割评价标准及损失函数

物体分割技术是计算机视觉中的研究热点,在多个领域都有广泛的应用.本文从人类视觉系统对场景中的全局信息和局部细节非常敏感的角度出发,设计了一种新颖、高效且易于计算的增强匹配标准(Eξ)来评估物体分割模型的性能.Eξ将局部像素值与全局平均值有机结合,以便评估分割结果与标准结果在图像级和像素级的相似度.在国际主流的4个公开数据集上的大量实验表明,Eξ在多个方面,如应用关联度、随机偏好度、噪声偏好度、感知度上相比现有广泛采纳的评价标准(IoU和Fβ)均有大幅相对提升.通过利用加权二值交叉熵损失函数、本文的增强匹配损失函数以及加权交并比损失函数,本文进一步设计了一套组合损失函数(Hybrid-Eloss)来促进网络学习到像素级、对象级和图像级的分割特征.定性和定量的结果表明,在3个不同领域的分割任务中使用这一组合损失函数能够进一步提高物体分割的精度.

Application of a novel inhibitor of human CD59 for the enhancement of complement-dependent cytolysis on cancer cells

Many monoclonal antibodies(mAbs)have been extensively used in the clinic,such as rituximab to treat lymphoma.However,resistance and non-responsiveness to mAb treatment have been challenging for this line of therapy.Complement is one of the main mediators of antibody-based cancer therapy via the complement-dependent cytolysis(CDC)effect.CD59 plays a critical role in resistance to mAbs through the CDC effect.In this paper,we attempted to investigate whether the novel CD59 inhibitor,recombinant ILYd4,was effective in enhancing the rituximab-mediated CDC effect on rituximab-sensitive RL-7 lymphoma cells and rituximab-induced resistant RR51.2 cells.Meanwhile,the CDC effects,which were mediated by rituximab and anti-CD24 mAb,on the refractory multiple myeloma(MM)cell line ARH-77 and the solid tumor osteosarcoma cell line Saos-2,were respectively investigated.We found that rILYd4 rendered the refractory cells sensitive to the mAb-mediated CDC effect and that rILYd4 exhibited a synergistic effect with the mAb that resulted in tumor cells lysis.This effect on tumor cell lysis was apparent on both hematological tumors and solid tumors.Therefore,rILYd4 may serve as an adjuvant for mAb mediated-tumor immunotherapy.

The good and evil of complement activation in HIV-1 infection

The complement system,a key component of innate immunity,is a first-line defender against foreign pathogens such as HIV-1.The role of the complement system in HIV-1 pathogenesis appears to be multifaceted.Although the complement system plays critical roles in clearing and neutralizing HIV-1 virions,it also represents a critical factor for the spread and maintenance of the virus in the infected host.In addition,complement regulators such as human CD59 present in the envelope of HIV-1 prevent complement-mediated lysis of HIV-1.Some novel approaches are proposed to combat HIV-1 infection through the enhancement of antibody-dependent complement activity against HIV-1.In this paper,we will review these diverse roles of complement in HIV-1 infection.

Facial-sketch Synthesis:A New Challenge

This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of obtaining hand-drawn sketch datasets,there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade.We first introduce a high-quality dataset for FSS,named FS2K,which consists of 2104 image-sketch pairs spanning three types of sketch styles,image backgrounds,lighting conditions,skin colors,and facial attributes.FS2K differs from previous FSS datasets in difficulty,diversity,and scalability and should thus facilitate the progress of FSS research.Second,we present the largest-scale FSS investigation by reviewing 89 classic methods,including 25 handcrafted feature-based facial-sketch synthesis approaches,29 general translation methods,and 35 image-to-sketch approaches.In addition,we elaborate comprehensive experiments on the existing 19 cutting-edge models.Third,we present a simple baseline for FSS,named FSGAN.With only two straightforward components,i.e.,facialaware masking and style-vector expansion,our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin.Finally,we conclude with lessons learned over the past years and point out several unsolved challenges.Our code is available at https://github.com/DengPingFan/FSGAN.

Critical role of type I interferon-induced macrophage necroptosis during infection with Salmonella enterica serovar Typhimurium

Salmonella enteric strains are facultat- ive intracellular pathogens that canproduce both localized enteritis and dis- seminated systemic disease in humans and a variety of other vertebrates.1 Extensive evidence obtained from genetic and cell biology studies indicates that Salmonella has evolved specific virulence mechanisms to evade innate immune responses.1 However, the underlying molecular and cellular mechanisms have not been under- stood very well. A recent paper published by Robinson et al. in Nature Immunology2 reported that type I interferon （IFN）- induced macrophage necroptosis plays a critical role in evading host innate immune responses during infection with

New insights into IL-7 signaling pathways during early and late T cell development

IL-7, a cytokine secreted mainly by stromal cells in the bone marrow and thymus, has been recognized to be critical for the development of all lymphocytes.1 However, the underlying mechanisms through which IL-7-mediated signaling contributes to early T-cell development and to mature T-cell maintenance are not yet completely understood. Two recently published studies in Nature Immunology reported that a newly iden- tified IL-7 signaling pathway acts during early T-cell development and documented how IL-7 Signaling interacts with the T-cell antigen receptor （TCR） to influence mature T-cell survival.2＇3