Dynamic Parameter Identification of Tool-Spindle Interface Based on RCSA and Particle Swarm Optimization

In order to ensure the stability of machining processes, the tool point frequency response functions (FRFs) should be obtained initially. By the receptance coupling substructure analysis (RCSA), the tool point FRFs can be generated quickly for any combination of holder and tool without the need of repeated measurements. A major difficulty in the sub-structuring analysis is to determine the connection parameters at the tool-holder interface. This study proposed an identification method to recognize the connection parameters at the tool-holder interface by using RCSA and particle swarm optimization (PSO). In this paper, the XHK machining center is divided into two components, which are the tool and the spindle assembly firstly. After that, the end point FRFs of the tool are achieved by mode superposition method. The end receptances of the spindle assembly with complicated structure are obtained by impacting test method. Through translational and rotational springs and dampers, the tool point FRF of the machining center is obtained by coupling the two components. Finally, PSO is adopted to identify the connection parameters at the tool-holder interface by minimizing the difference between the predicted and the measured tool point FRFs. Comparison results between the predicted and measured tool point FRFs show a good agreement and demonstrate that the identification method is valid in the identification of connection parameters at the tool-holder interface

Interdecadal variation of ENSO predictability in multiple models

Abstract In this study, we performed ENSO (El Niño and the Southern Oscillation) retrospective forecasts for the 120 years from 1881-2000 using three realistic models that assimilate the historic dataset of sea surface temperature (SST). By examining these retrospective forecasts and corresponding observations, as well as the oceanic analyses from which forecasts were initialized, we have explored several important issues related to ENSO predictability including its interdecadal variability and the dominant factors that control the interdecadal variability. The prediction skill of the three models showed a very consistent interdecadal variation, with high skill in the late 19th century and in the middle-late 20th century, and low skill during the period from 1900-1960. The interdecadal variation in ENSO predictability is in good agreement with that in the signal of interannual variability and in the degree of asymmetry of ENSO system. A good relationship was also identified between the degree of asymmetry and the signal of interannual variability, and the former is highly related to the latter. Generally the high predictability is attained when ENSO signal strength and the degree of asymmetry are enhanced, and vice versa. The atmospheric noise generally degrades overall prediction skill, especially for the skill of mean square error, but is able to favor some individual prediction cases. The possible reasons why these factors control ENSO predictability were also discussed

Fault Detection and Fault-Tolerant Cooperative Control of Multi-UAVs under Actuator Faults, Sensor Faults, and Wind Disturbances

Fault detection (FD) and fault-tolerant cooperative control (FTCC) strategies are proposed in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) under actuator faults, sensor faults, and wind disturbances. Firstly, the faulty model is introduced while the effectiveness loss, deviation of thrust throttle setting, and pitot sensor faults are considered. Secondly, the faulty UAV model with wind disturbances is linearized and the system is then converted into two subsystems by using state and output transformations. Further, cooperative unknown input observers (UIOs) are developed to estimate the faults, disturbances, and states. By combining with the observers’ estimations, adaptive thresholds are designed to detect actuator and sensor faults in the system. Then, considering state constraints, a backstepping-based FTCC scheme is proposed for multiple UAVs (multi-UAVs) suffering from actuator faults, sensor faults, and wind disturbances. It is shown by Lyapunov analysis that the tracking errors are fixed-time convergent. Finally, the effectiveness of the FD and FTCC scheme is verified by numerical simulation

The Hypothetical Protein CT813 Is Localized in the Chlamydia trachomatis Inclusion Membrane and Is Immunogenic in Women Urogenitally Infected with C. trachomatis

Using antibodies raised with chlamydial fusion proteins, we have localized a protein encoded by hypothetical open reading frame CT813 in the inclusion membrane of Chlamydia trachomatis. The detection of the C. trachomatis inclusion membrane by an anti-CT813 antibody was blocked by the CT813 protein but not unrelated fusion proteins. The CT813 protein was detected as early as 12 h after chlamydial infection and was present in the inclusion membrane during the entire growth cycle. All tested serovars from C. trachomatis but not other chlamydial species expressed the CT813 protein. Exogenously expressed CT813 protein in HeLa cells displayed a cytoskeleton-like structure similar to but not overlapping with host cell intermediate filaments, suggesting that the CT813 protein is able to either polymerize or associate with host cell cytoskeletal structures. Finally, women with C. trachomatis urogenital infection developed high titers of antibodies to the CT813 protein, demonstrating that the CT813 protein is not only expressed but also immunogenic during chlamydial infection in humans. In all, the CT813 protein is an inclusion membrane protein unique to C. trachomatis species and has the potential to interact with host cells and induce host immune responses during natural infection. Thus, the CT813 protein may represent an important candidate for understanding C. trachomatis pathogenesis and developing intervention and prevention strategies for controlling C. trachomatis infection

α-Glucosidase and Bacterial β-Glucuronidase Inhibitors from the Stems of Schisandra sphaerandra Staph

α-Glucosidase (AGS) is a therapeutic target for Type 2 diabetes mellitus (T2DM) that tends to complicate with other diseases. Some medications for the treatment of T2DM complications have the risk of inducing severe adverse reactions such as diarrhea via the metabolism of intestinal bacterial β-glucuronidase (BGUS). The development of new AGS and/or BGUS inhibitors may improve the therapeutic effects of T2DM and its complications. The present work focused on the isolation and characterization of AGS and/or BGUS inhibitors from the medicinal plant Schisandra sphaerandra. A total of eight compounds were isolated and identified. Sphaerandralide A (1) was obtained as a previously undescribed triterpenoid, which may have chemotaxonomy significance in the authentication of the genus Schisandra and Kadsura. 2′-acetyl-4′,4-dimethoxybiphenyl-2-carbaldehyde (8) was obtained from a plant source for the first time, while compounds 2–7 were isolated from S. sphaerandra for the first time. In the in vitro assay, compounds 1–5 showed potent to moderate activity against AGS. Interestingly, compound 3 also exhibited significant BGUS inhibitory activity, demonstrating the potential of being developed as a bifunctional inhibitor that may find application in the therapy of T2DM and/or the diarrhea induced by medications for the treatment of T2DM complications

Correlation of photodynamic activity and fluorescence signaling for free and pegylated mTHPC in mesothelioma xenografts

Correlation of photodynamic activity (PDT) and fluorescence signaling for free and pegylated meta-tetrahydroxyphenylchlorin (mTHPC) in nude mice with mesothelioma xenografts

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach

Background and purpose: Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. Materials and methods: A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SNNM) boundaries and iron-based SN (SNQSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SNNM volume, SNQSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. Results: In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SNNM, and 0.87 and 1.05 for the SNQSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SNNM, and 0.87 and 0.96 for the SNQSM, respectively. The SNQSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SNQSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. Conclusion: In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically