CAN MANUFACTURING OUTPUT SERVITIZATION REDUCE CARBON EMISSIONS?

Carbon emissions from manufacturing have been a growing global concern in recent years. The growth in manufacturing firms’ service output and its carbon emission reduction effect have received less attention, though. Using data from 2008-2020 for listed companies in China, this study empirically analyzed the effects of manufacturing output servitization on carbon intensity. The results revealed a significant negative relationship between them. Heterogeneity analysis finds that the carbon emission reduction effect of manufacturing servitization is strongest in (i) private and relatively small-scale firms and (ii) developed regions and capital-intensive industries. The mediating effect study shows that green TFP and revenue growth rate are the transmission channels for the environmental impact of manufacturing servitization. This study verifies that servitization is a feasible path to coordinate high-quality economic development with resource and environmental constraints from different perspectives to provide a reference for the realistic development of diverse economies

Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder

Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions

Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network

(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling

M-estimate based normalized subband adaptive filter algorithm: Performance analysis and improvements

This article studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results of the algorithm are formulated analytically. These analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to the proportionate M-NSAF variant for sparse systems. Computer simulations on the system identification in impulsive noise and the acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.</p

An Origami Continuum Manipulator with Modularized Design and Hybrid Actuation: Accurate Kinematic Modeling and Experiments

Herein, this study contributes significantly to the advancement of continuum manipulators in two main aspects. First, a modularization concept and a hybrid actuation scheme to create a novel origami continuum manipulator with exceptional deformability are introduced. Second, an accurate model and framework for the forward and inverse kinematic analysis of origami manipulators are proposed. Specifically, each origami manipulator module can achieve axial extension and bending deformation by coordinated actuation of shape memory alloy (SMA) and pneumatic muscles, and the manipulator's end is equipped with a deformable gripper based on waterbomb origami and actuated by SMA. Through careful consideration of the self‐weight and torque balance, an accurate kinematic model based on the Denavit–Hartenberg method is established, which enables one to effectively predict the reachable extreme positions and spatial poses of the manipulator and solve the inverse kinematics using a genetic algorithm. Comprehensive experiments are conducted to validate the design's rationality and model's accuracy . In these tests, the rich spatial configurations are not only demonstrated that can be achieved by integrating hybrid actuators with origami modules but also the accuracy and reliability of the kinematic model are confirmed, opening up possibilities for the advancement and application of origami‐inspired robotics in various fields

Diagnostic performance of angiography-derived fractional flow reserve and CT-derived fractional flow reserve: A systematic review and Bayesian network meta-analysis

Accumulating evidence has demonstrated that fractional flow reserves (FFRs) derived from invasive coronary angiograms (CA-FFRs) and coronary computed tomography angiography-derived FFRs (CT-FFRs) are promising alternatives to wire-based FFRs. However, it remains unclear which method has better diagnostic performance. This systematic review and meta-analysis aimed to compare the diagnostic performances of the two approaches. The Cochrane Library, PubMed, Embase, Medline (Ovid), the Chinese China National Knowledge Infrastructure Database (CNKI), VIP, and WanFang Data databases were searched for relevant studies that included comparisons between CA-FFR and CT-FFR, from their respective database inceptions until January 1, 2023. Studies where both noninvasive FFR (including CA-FFR and CT-FFR) and invasive FFR (as a reference standard) were performed for the diagnosis of ischemic coronary artery disease and were designed as prospective, paired diagnostic studies, were pulled. The diagnostic test accuracy method and Bayesian hierarchical summary receiver operating characteristic (ROC) model for network meta-analysis (NMA) of diagnostic tests (HSROC-NMADT) were both used to perform a meta-analysis on the data. Twenty-six studies were included in this NMA. The results from both the diagnostic test accuracy and HSROC-NMADT methods revealed that the diagnostic accuracy of CA-FFR was higher than that of CT-FFR, in terms of sensitivity (Se; 0.86 vs. 0.84), specificity (Sp; 0.90 vs. 0.78), positive predictive value (PPV; 0.83 vs. 0.70), and negative predictive value (NPV; 0.91 vs. 0.89) for the detection of myocardial ischemia. A cumulative ranking curve analysis indicated that CA-FFR had a higher diagnostic accuracy than CT-FFR in the context of this study, with a higher area under the ROC curve (AUC; 0.94 vs. 0.87). Although both of these two commonly used virtual FFR methods showed high levels of diagnostic accuracy, we demonstrated that CA-FFR had a better Se, Sp, PPV, NPV, and AUC than CT-FFR. However, this study provided only indirect comparisions; therefore, larger studies are warranted to directly compare the diagnostic performances of these two approaches

Encrypted wide-field two-photon microscopy with single-pixel detection and compressed sensing

Ren Y-X, Kong C, He H, Zeng X, Tsia KK, Wong KKY. Encrypted wide-field two-photon microscopy with single-pixel detection and compressed sensing. APPLIED PHYSICS EXPRESS. 2020;13(3): 032007.We demonstrate a single-pixel two-photon microscopy using a compact femtosecond fiber laser by spatially tailoring the beam into orthogonal basis for patterned illumination. Such wide-field illumination excites a weak two-photon fluorescence signal that can be detected by a photomultiplier tube. An encrypted hybrid basis with random element sequence of Hadamard basis is adopted to illuminate the sample. The hybrid basis shares the same differential detection with Hadamard basis, and greatly reduces the number of measurements compared with random basis. The reduced number of measurements was demonstrated by using compressed sensing, which allows the minimum image collection and transfer bandwidth. (C) 2020 The Japan Society of Applied Physics

Transcriptome Analysis of Immune Responses and Metabolic Regulations of Chinese Soft-Shelled Turtle (Pelodiscus sinensis) against Edwardsiella tarda Infection

The Chinese soft-shelled turtle (Pelodiscus sinensis) is an important aquatic species in southern China that is threatened by many serious diseases. Edwardsiella tarda is one of the highly pathogenic bacteria that cause the white abdominal shell disease. Yet, little is known about the immune and metabolic responses of the Chinese soft-shelled turtle against E. tarda infection. In the paper, gene expression profiles in the turtle liver were obtained to study the immune responses and metabolic regulations induced by E. tarda infection using RNA sequencing. A total of 3908 differentially expressed unigenes between the experimental group and the control group were obtained by transcriptome analysis, among them, were the significantly upregulated unigenes and downregulated unigenes 2065 and 1922, respectively. Further annotation and analysis revealed that the DEGs were mainly enriched in complement and coagulation cascades, phagosome, and steroid hormone biosynthesis pathways, indicating that they were mainly associated with defense mechanisms in the turtle liver against E. tarda four days post infection. For the first time, we reported on the gene profile of anti-E. tarda response in the soft-shelled turtle, and our research might provide valuable data to support further study on anti-E. tarda defense mechanisms in turtles