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Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (α) has been introduced to seamlessly link open channel and pressurized flow dynamics, facilitating rapid adjustments in wave speed. On this basis, Fourier feature extraction and quadratic neural networks have been employed to capture complex dynamic processes featuring high-frequency. Validation through three classical cases using the Storm Water Management Model (SWMM) and comparisons with finite volume Harten-Lax-van Leer (HLL) solver reveal that the proposed model circumvents the constraints of spatiotemporal resolution, yielding accurate flow field predictions.

Abstract Objective Synovial inflammation, which precedes other pathological changes in osteoarthritis (OA), is primarily initiated by activation and M1 polarization of macrophages. While macrophages play a pivotal role in the inflammatory process of OA, the mechanisms underlying their activation and polarization remain incompletely elucidated. This study aims to investigate the role of NOD2 as a reciprocal modulator of HMGB1/TLR4 signaling in macrophage activation and polarization during OA pathogenesis. Design We examined NOD2 expression in the synovium and determined the impact of NOD2 on macrophage activation and polarization by knockdown and overexpression models in vitro. Paracrine effect of macrophages on fibroblast-like synoviocytes (FLS) and chondrocytes was evaluated under conditions of NOD2 overexpression. Additionally, the in vivo effect of NOD2 was assessed using collagenase VII induced OA model in mice. Results Expression of NOD2 was elevated in osteoarthritic synovium. In vitro experiments demonstrated that NOD2 serves as a negative regulator of HMGB1/TLR4 signaling pathway. Furthermore, NOD2 overexpression hampered the inflammatory paracrine effect of macrophages on FLS and chondrocytes. In vivo experiments revealed that NOD2 overexpression mitigated OA in mice. Conclusions Supported by convincing evidence on the inhibitory role of NOD2 in modulating the activation and M1 polarization of synovial macrophages, this study provided novel insights into the involvement of innate immunity in OA pathogenesis and highlighted NOD2 as a potential target for the prevention and treatment of OA.

Abstract Background An ever‐increasing number of efforts are focused on identifying effective biomarkers for immune checkpoint inhibitors (ICIs). Cytokines and chemokines are critical to tumor growth, metastasis, tumor angiogenesis, and the immune response against tumor cells. In the study here, we determined the correlation between circulating cytokines/chemokines and the clinical benefit of ICIs for non‐small cell lung cancer (NSCLC) patients. Methods Peripheral blood samples were collected before and during treatment (at 12th week). Plasma levels of cytokines/chemokines and specific stress response markers were measured using the Bio‐Plex Pro Human Cytokines Grp I Panel (27‐plex), an APEX1 detection kit, and a human LAP(TGF‐β1) immunoassay kit. A Mann–Whitney U‐test or Wilcoxon signed‐rank test and a Cox proportional hazards model were employed for statistical analysis. Results In the ICI monotherapy cohort, a high level of IL‐6 at pretreatment or an elevation of IL‐6, IL‐8, FGF2, CXCL10, CCR1, PDFGB, TNF, and APEX1 posttreatment was associated with poor progress‐free survival (PFS). A posttreatment elevation (defined herein as change rate) of CXCL10 was also associated with poor overall survival (OS). In the combinational therapy group, a high level of IL‐12, IL‐17A, FGF2, VEGF, and APEX1 at pretreatment and an elevation of CCL2 posttreatment were associated with poor PFS. A high level of IL‐9, FGF2, PDFGB, CCL4, TFGB, and APEX1 at pretreatment and an elevation of IL‐13, CSF2, and CCL2 at posttreatment were associated with poor OS of patients receiving combination therapy. Conclusions The study here suggests that circulating cytokines/chemokines are feasible, noninvasive biomarkers for predicting clinical benefit of ICI treatment for NSCLC. Distinct circulating factor profiles were observed in individuals receiving ICI monotherapy or combination therapy.

Introduction: Overhead transmission line vibration is detrimental to the normal operation of the power grid. It is necessary to remotely monitor overhead transmission lines with sensors in normal operation, and sensors require a constant source of energy. Harvesting energy from transmission line vibrations is an excellent solution to power these sensors.Methods: A low-frequency and low-amplitude vibration energy harvester is proposed, analyzed, produced and experimented in this study. A main constituent of the energy harvester is an outer support, an inner support, four one-way bearings, a bevel gear system and a DC generator. The harvester converts the linear reciprocating motion of the line into reciprocating swing at first and then converts it into fixed-direction rotation. Theoretical analyses are conducted to determine the harvester performance factors. Finally, the harvester is fabricated and tested.Results: The test results are in good accordance with the simulation results. At the vibrating speed as 0.48 m/s, the maximum output power and output voltage are 4.2 W and 24.7 V, respectively. The weather sensor and video recorder installed on the transmission line are powered by the harvester.Discussion: The energy harvester also effectively suppresses the vibration of transmission lines and has great potential in the constructions of smart grids. The harvester provides a feasible solution for harvesting line vibration energy and suppressing line breeze vibration simultaneously.

Objective Wear particles induce inflammation and the further osteolysis around the prosthesis, has been proven to be the main cause of aseptic hip joint loosening. In this research, we aimed to clarify whether human umbilical cord mesenchymal stem cells (HUCMSCs) could inhibit the titanium particles‐induced osteolysis and shed light upon its mechanism. Methods The expression of chemokine (C‐C motif) ligand 2 (CCL2), chemokine (C‐C motif) ligand 3 (CCL3) and chemokine (C‐C motif) ligand 5 (CCL5) were examinjed in clinical specimens of aseptic hip prosthesis loosening patients. Local injection of lentivirus that knocked down CCL2 or CCL3 in a cranial osteolysis mice model were used to exam the effect of CCL2 and CCL3 on titanium particles‐induced osteolysis in vivo. Transwell assay was used to examine the effect of CCL2 and CCL3 on titanium particles‐induced activation of macrophage in vitro. Furthermore, the therapeutic effect of HUCMSCs, and exosomes from HUCMSCs were also examed in vivo and vitro. Immunohistochemical and real‐time PCR were used to examine the expression of relative pathways. Analysis of variance (ANOVA) and Student–Newman–Keuls post hoc t test were used to analyze the results and determine the statistical significance of the differences. Results Results showed that titanium particles caused the osteolysis at the mice cranial in vivo and a large number of macrophages that migrated, while local injection of HUCMSCs and exosomes did inhibit the cranial osteolysis and migration. An exosome inhibitor GW4869 significantly increased the osteolysis area in the mice cranium osteolysis model, and increased the number of migrated macrophages. Immunohistochemical results suggested that the expression of CCL2, CCL3 and CD68 in the cranial in Titanium particles mice increased significantly, but was significantly reduced by HUCMSCs or exosomes. HUCMSC and exosomes down‐regulate the expression of CCL3 in vitro and in vivo. Conclusion HUCMSCs and HUCMSC‐derived exosomes could suppress the titanium particles‐induced osteolysis in mice through inhibiting chemokine (C‐C motif) ligand 2, chemokine (C‐C motif) ligand 3.

The accurate modeling and understanding of complex transient mixed pipe flows is crucial for the optimal design and safe and efficient operation in pipeline systems such as urban drainage systems. Currently, the predominant approach for modeling free-surface-pressurized flows relies on grid-based numerical schemes, with comparatively limited capability for exploring its complex phenomena. This study proposed a novel one-dimensional numerical model that integrates the smoothed particle hydrodynamics (SPH) method with the Preissmann slot method (PSM) to explore transient mixed flows in pipeline systems, with better potential capability for exploring more mixed flow phenomena. Empirical parameters of the proposed SPH-PSM model were optimized for improving the numerical accuracy and stability, and the applicable range for these empirical parameters was recommended. The performances of the proposed model were evaluated by different flow regimes, including one free surface case, one fully pressurized case, and two transient mixed-flow cases. The simulation results of different flow regimes demonstrated a high level of agreement with the reference data, indicating the good capability of the SPH-PSM model in simulating complex flow regimes in pipeline systems. Therefore, the proposed SPH-PSM model can be an alternative way for modeling, exploring, and understanding the complex transient mixed flows in pipeline systems.

Abstract As total joint replacement is widely applied for severe arthropathy, peri-prosthetic aseptic loosening as one of the main causes of implant failure has drawn wide attention. Wear particles such as titanium particles (TiPs) derived from prosthesis can initiate macrophages inflammation and sequentially activate osteoclasts, which results in bone resorption and osteolysis for long-term. Therefore, inhibiting wear particles induced macrophages inflammation is considered as a promising therapy for AL. In this research, we found that the inhibition of p110δ, a member of class IA PI3Ks family, could significantly dampen the TiPs-induced secretion of TNFα and IL-6. By the transfection of siRNA targeting p110δ, we confirmed that p110δ was responsible for TNFα and IL-6 trafficking out of Golgi complex without affecting their expression in TiPs-treated macrophages. As the upstream transcription-repressor of p110δ, Krüppel-like factor 4 (KLF4), targeted by miR-92a, could also attenuate TiPs-induced inflammation by mediating NF-κB pathway and M1/M2 polarization. To further ascertain the roles of KLF4/p110δ, TiPs-induced mice cranial osteolysis model was established and vivo experiments validated that KLF4-knockdown could exacerbate TiPs-induced osteolysis, which was strikingly ameliorated by knockdown of p110δ. In summary, our study suggests the key role of miR-92a/KLF4/p110δ signal in TiPs-induced macrophages inflammation and osteolysis.

Background: Recent studies have suggested association between the ABO blood group and inflammation, which was a crucial pathological process of primary knee osteoarthritis. The aim of this study was to investigate the association between the ABO blood group and primary knee osteoarthritis ​and the severity of primary knee osteoarthritis evaluated by the Kellgren/Lawrence score, as well as the histopathologic association in a subgroup of patients. Methods: We performed a retrospective review of patients with primary knee osteoarthritis that served as the case group ​and a random sampling of healthy blood donors that served as the control group. The severity of knee osteoarthritis at the first outpatient visit was evaluated by the Kellgren/Lawrence scoring system. Further study was performed to investigate the expression of blood group antigens in synovial tissue of the knee in both cases and controls. Results: A total of 1126 cases and 30299 controls were involved. The proportion of AB blood group was higher in the case group than in the control group (9.7% vs. 7.8%), and logistic regression revealed that the AB blood group was a risk factor of primary knee osteoarthritis (P ​= ​0.025 and 0.048 for univariate and multivariate analysis, respectively), independent of age (P ​= ​0.973) and sex (P ​= ​0.520). Patients of the blood group AB had a higher Kellgren/Lawrence score (P ​= ​0.017). The immunohistochemical study indicated association between LeY antigen and primary knee osteoarthritis (P ​= ​0.029). Conclusions: This study suggested that the blood group AB was associated with primary knee osteoarthritis, as well as its radiological severity. Further study indicated that LeY antigen, which was related to the blood group, was associated with primary knee osteoarthritis. Translational potential of this article: This study revealed that blood group AB and LeY antigen was associated with primary knee osteoarthritis, which shed new light on the nature of osteoarthritis, and the development of novel therapy for osteoarthritis. Keywords: Blood group, Kellgren/Lawrence, Knee, LeY antigen, Osteoarthritis

Previous studies have reported that, before or after occurrences of strong earthquakes, some low earth orbit satellites recorded ionospheric disturbances, including electromagnetic emissions and plasma fluctuations over the epicenter region or its conjugate point. Theoretically speaking, due to some electromagnetic coupling effect, electromagnetic emissions from the earthquake preparation zone could propagate from the lithosphere to the atmosphere, and could reach the ionosphere, even up to the inner magnetosphere. This paper introduces the electric field detector (EFD) onboard the ZhangHeng-1 satellite (ZH-1). The EFD is designed to measure electric field fluctuations within the broad frequency range of DC to 3.5 MHz, divided into 4 channels: ULF (DC–16 Hz), ELF (6 Hz–2.2 kHz), VLF (1.8 kHz–20 kHz) and HF (18 kHz–3.5 MHz). The sampling rates of the channels are 125 Hz, 5 kHz, 50 kHz and 10 MHz, respectively. The EFD includes 4 spherical probes mounted on a over 4.5 m boom and an electronic box inside the satellite module. The resolution of the EFD is 1 μV·m-1·Hz-1/2 at frequencies from DC to 16 Hz, and the sensitivity is 0.1 μV·m-1·Hz-1/2 at frequencies from 6 Hz to 2.2 kHz, 0.05 μV·m-1·Hz-1/2 in the band 1.8 kHz to 20 kHz, and 0.1μV·m-1·Hz-1/2 from 20 kHz to 3.5 MHz. The dynamic range from DC to 20 kHz is over 120 dB, and over 96 dB from 20 kHz to 3.5 MHz. The EFD has two observation modes: survey mode and burst mode. The survey mode concentrates primarily on electric field power density values; the burst mode provides high sampling rate waveform data. The detailed configuration of the EFD onboard the ZH-1 is also introduced in this paper. During the six months’ orbit test phase, the EFD recorded a number of natural electromagnetic emissions. Preliminary analysis of these data suggests that the EFD performs well onboard the ZH-1 and is meeting the requirements of the scientific objectives of ZH-1.

Given a finite group G, the coprime graph of G, denoted by Î(G), is defined as an undirected graph with the vertex set G, and for distinct x, y â G, x is adjacent to y if and only if (o(x), o(y)) = 1, where o(x) and o(y) are the orders of x and y, respectively. This paper classifies the finite groups with split, threshold and chordal coprime graphs, as well as gives a characterization of the finite groups whose coprime graph is a cograph. As some applications, the paper classifies the finite groups G such that Î(G) is a cograph if G is a nilpotent group, a dihedral group, a generalized quaternion group, a symmetric group, an alternating group, or a sporadic simple group.

Let G be a finite group with the identity element e. The proper order graph of G, denoted by â* (G), is an undirected graph with a vertex set G \ {e}, where two distinct vertices x and y are adjacent whenever o(x) | o(y) or o(y) | o(x), where o(x) and o(y) are the orders of x and y, respectively. This paper studies the perfect codes of â*(G). We characterize all connected components of a proper order graph and give a necessary and sufficient condition for a connected proper order graph. We also determine the perfect codes of the proper order graphs of a few classes of finite groups, including nilpotent groups, CP-groups, dihedral groups and generalized quaternion groups.

Abstract Although the acidic oxygen evolution reaction (OER) plays a crucial role in proton-exchange membrane water electrolysis (PEMWE) devices, challenges remain owing to the lack of efficient and acid-stable electrocatalysts. Herein, we present a low-iridium electrocatalyst in which tensile-strained iridium atoms are localized at manganese-oxide surface cation sites (TS-Ir/MnO2) for high and sustainable OER activity. In situ synchrotron characterizations reveal that the TS-Ir/MnO2 can trigger a continuous localized lattice oxygen-mediated (L-LOM) mechanism. In particular, the L-LOM process could substantially boost the adsorption and transformation of H2O molecules over the oxygen vacancies around the tensile-strained Ir sites and prevent further loss of lattice oxygen atoms in the inner MnO2 bulk to optimize the structural integrity of the catalyst. Importantly, the resultant PEMWE device fabricated using TS-Ir/MnO2 delivers a current density of 500 mA cm−2 and operates stably for 200 h.

This paper focuses on the analysis of the stress state of a large-span frame pier-continuous box girder bridge with pier crossbeams anchored by pier crossbeams on the main pier of the Guangfo-Zhao Expressway. The bridge is constructed by the cantilever method, and a refined finite element model of the entire bridge is established using the finite element software Midas/FEA to analyze the stress state of the frame pier during the cantilever construction process. It is found that under the possible combined action of an unbalanced load during construction, the torsional resistance of the frame pier crossbeam does not meet the requirements of the design code. In order to eliminate the torsion of the frame piers, counterweights were used to monitor the frame piers during the construction of the box girders. In this paper, the theoretical calculation formula of the inclination angle of the end section of the frame pier crossbeam with the change of unbalanced bending moment, the calculation formula of the relationship between the horizontal displacement of the frame pier and the unbalanced bending moment, and the calculation formula corresponding to the relationship with the water tank counterweight are derived using the structural mechanics method. Two monitoring methods for the frame pier are proposed. In the construction monitoring of the bridge, the numerical fitting formula obtained by finite element numerical analysis calculation is compared with the calculated formula obtained by substituting the design parameters of the frame pier into the theoretical formula. The basic constants in both formulas are basically equal, verifying the correctness of the monitoring calculation formula proposed in this paper for the torsional resistance of the frame pier crossbeam. The applicability of the two monitoring methods is also compared and analyzed. This paper takes the main pier of Chaoyang overpass’s mainline bridge as the engineering background, which adopts the framework pier with a large-span prestressed concrete continuous box girder bridge. It analyzes the torsional state of the beam of the framework pier during the bridge construction process and conducts research on the construction monitoring of the framework pier crossbeam, providing valuable references for the construction monitoring of framework pier crossbeams in the construction of large-span framework pier continuous bridges in the future. The research results of this paper can provide assistance for the construction monitoring of similar projects. This paper’s innovation primarily resides in employing structural mechanics methods to compute the torsion of frame piers. On this basis, a simplified beam torsion calculation formula is proposed to strengthen its practical application in construction monitoring. The findings of this paper can help in the construction monitoring of similar projects.

An automorphism group of a graph is said to be s-regular if it acts regularly on the set of s-arcs in the graph. A graph is s-regular if its full automorphism group is s-regular. In this paper, we classify all connected cubic symmetric graphs of order 52p2 for each prime p.

Feature extraction technology is an important part of bearing diagnosis, especially for early degradation detection. However, the traditional feature extraction technology can not effectively remove noise or is not sensitive to periodic weak faults, which leads to be inclined to raise false alarms and prediction delay for early degradation detection. In order to solve these two issues, a new feature extraction technique is presented based on Envelope Harmonic-to-noise Ratio (EHNR) and Adaptive Variational Mode Decomposition (AVMD). First of all, the minimum average envelope entropy is used as the objective function to search the optimal parameters of the Variational Modal Decomposition (VMD) adaptively by the Grey Wolf Optimization (GWO) algorithm. The problem of under-decomposition or over-decomposition caused by improper parameter setting is avoided. Then, a new index called Effective Weighted Sparseness Kurtosis (EWSK) is proposed. This index can separate the effective modal components and noise modal components only by the positive and negative results, so as to achieve the purpose of removing noise interference and retaining a large amount of fault information. Finally, the EHNR of the reconstructed signal is calculated, and its sensitivity to periodic fault shock is utilized to detect the early degradation starting point of the rolling bearing. Experimental results show that the proposed method outperforms several state-of-the-art detection methods in terms of early degradation point detection, false alarm rate and computational complexity. The superior performances of the presented AVMD-EHNR method can provide the basis for early fault diagnosis and remaining useful life prediction of rolling bearings.

Summary: The synthesis of biofilm exopolysaccharides and rhamnolipids (RLs) are two interrelated processes in Pseudomonas aeruginosa, but how bacteria coordinate these two processes remains unclear. We collected a P. aeruginosa KT1115 with rugose small colony variant (RSCV) phenotype from soil, and used it to study the dynamic regulation mechanism of biofilm polysaccharide and RLs synthesis. The results showed that the overproduction of biofilm exopolysaccharides at biofilm stage ultimately contributed the surge of RLs production at RLs stage. This phenomenon was further verified by comparing PAO1 with its engineered RSCV mutant, PAO1ΔwspF. Further genomic, transcriptomic analyses and gene deletion revealed that downregulation of c-di-GMP level was the key to switch biofilm exopolysaccharides accumulation to RLs surge, by transcriptionally upregulating a c-di-GMP phosphodiesterase NbdA. Overall, this study demonstrates the importance of c-di-GMP in coordinating biofilm exopolysaccharides and RLs synthesis, and provides an inspiration for enhancing RLs production through regulating c-di-GMP level.

Abstract Background The aim of this study was to construct a model based on the prognostic features associated with epithelial–mesenchymal transition (EMT) to explore the various mechanisms and therapeutic strategies available for the treatment of metastasis and invasion by hepatocellular carcinoma (HCC) cells. Methods EMT-associated genes were identified, and their molecular subtypes were determined by consistent clustering analysis. The differentially expressed genes (DEGs) among the molecular subtypes were ascertained using the limma package and they were subjected to functional enrichment analysis. The immune cell scores of the molecular subtypes were evaluated using ESTIMATE, MCPcounter, and GSCA packages of R. A multi-gene prognostic model was constructed using lasso regression, and the immunotherapeutic effects of the model were analyzed using the Imvigor210 cohort. In addition, immunohistochemical analysis was performed on a cohort of HCC tissue to validate gene expression. Results Based on the 59 EMT-associated genes identified, the 365—liver hepatocellular carcinoma (LIHC) samples were divided into two subtypes, C1 and C2. The C1 subtype mostly showed poor prognosis, had higher immune scores compared to the C2 subtype, and showed greater correlation with pathways of tumor progression. A four-gene signature construct was fabricated based on the 1130 DEGs among the subtypes. The construct was highly robust and showed stable predictive efficacy when validated using datasets from different platforms (HCCDB18 and GSE14520). Additionally, compared to currently existing models, our model demonstrated better performance. The results of the immunotherapy cohort showed that patients in the low-risk group have a better immune response, leading to a better patient’s prognosis. Immunohistochemical analysis revealed that the expression levels of the FTCD, PON1, and TMEM45A were significantly over-expressed in 41 normal samples compared to HCC samples, while that of the G6PD was significantly over-expressed in cancerous tissues. Conclusions The four-gene signature construct fabricated based on the EMT-associated genes provides valuable information to further study the pathogenesis and clinical management of HCC.

Abstract Hepatocellular carcinoma (HCC) is a common high-mortality cancer, mainly due to diagnostic difficulties during its early clinical stages. In this study, we aimed to identify genes that are important for HCC diagnosis and treatment, and we investigated the underlying mechanism of prognostic differences. Differentially expressed genes (DEGs) were identified by using the limma package, and receiver operating characteristic curve analysis was performed to identify diagnostic markers for HCC. Bioinformatics and clinical specimens were used to assess epithelial cell transforming 2 (ECT2) in terms of expression, prognostic value, pathways, and immune correlations. In vitro experiments were used to investigate the underlying mechanism and function of ECT2, and the results were confirmed through in vivo experiments. The integrated analysis revealed 53 upregulated DEGs, and one candidate biomarker for diagnosis (ECT2) was detected. High expression of ECT2 was found to be an independent prognostic risk factor for HCC. ECT2 expression showed a strong correlation with tumor-associated macrophages. We found that ECT2 overexpression increased the migration and proliferation of HCC cells. It also promoted the expression of PLK1, which subsequently interacted with PTEN and interfered with its nuclear translocation, ultimately enhancing aerobic glycolysis and promoting M2 macrophage polarization. M2 macrophages suppress the functions of NK cells and T cells, and this was confirmed in the in vivo experiments. Overall, ECT2 may promote the polarization of M2 macrophages by enhancing aerobic glycolysis and suppressing the functions of immune cells. ECT2 could serve as a candidate diagnostic and prognostic biomarker for HCC.

Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by default in the existing studies: 1) The training (source domain) and testing (target domain) data sets obey the same feature distribution; 2) Sufficient labeled data with fault information is available for model training. In real industrial scenarios, especially for different machines, these assumptions are mostly invalid, which makes it a huge challenge to build reliable diagnostic model. Motivated by transfer learning, we present a novel intelligent method named deep transfer network (DTN) with multi-kernel dynamic distribution adaptation (MDDA) to address the problem of cross-machine fault diagnosis. In the proposed approach, the DTN has wide first-layer convolutional kernel and several small convolutional layers, which is utilized to extract transferable features across different machines and suppress high frequency noise. Then, the MDDA method constructs a weighted mixed kernel function to map different transferable features to a unified feature space, and the relative importance of the marginal and conditional distributions are also evaluated dynamically. The proposed method is verified by three transfer learning tasks of bearings, in which the health states of wind turbine bearings in real scenario are identified by using diagnosis knowledge from two different bearings in laboratories. The results show that the proposed method can achieve higher diagnosis accuracy and better transfer performance even under different noisy environment conditions than many other state-of-the-art methods. The presented framework offers a promising approach for cross-machine fault diagnosis.

Abstract Background Rhamnolipids are the best known microbial-derived biosurfactants, which has attracted great interest as potential ‘‘green” alternative for synthetic surfactants. However, rhamnolipids are the major contributors to severe foam problems, which greatly inhibit the economics of industrial-scale production. In this study, a novel foam-control system was established for ex situ dealing with the massive overflowing foam. Based on the designed facility, foam reduction efficiency, rhamnolipids production by batch and repeated fed-batch fermentation were comprehensively investigated. Results An ex situ foam-control system was developed to control the massive overflowing foam and improve rhamnolipids production. It was found that the size of individual bubble in the early stage was much larger than that of late fermentation stage. The foam liquefaction efficiency decreased from 54.37% at the beginning to only 9.23% at the end of the fermentation. This difference of bubble stability directly resulted in higher foam reduction efficiency of 67.46% in the early stage, whereas the small uniform bubbles can only be reduced by 57.53% at the later fermentation stage. Moreover, reduction of secondary foam is very important for foam controlling. Two improved designs of the device in this study obtained about 20% improvement of foam reduction efficiency, respectively. The batch fermentation result showed that the average volume of the overflowing foam was reduced from 58–640 to 19–216 mL/min during the fermentation process, presenting a notable reduction efficiency ranging from 51.92 to 73.47%. Meanwhile, rhamnolipids production of batch fermentation reached 45.63 g/L, and the yield 0.76 g/g was significantly better than ever reported. Further, a repeated fed-batch fermentation based on the overall optimization was carried out. Total rhamnolipids concentration reached 48.67 g/L with the yield around of 0.67–0.83 g/g, which presented an improvement of 62% and 49% compared with conventional batch fermentation by using various kinds of defoamers, respectively. Conclusions The ex situ foam-control system presented a notable reduction efficiency, which helped greatly to easily solve the severe foaming problem without any defoamer addition. Moreover, rhamnolipids production and yield by repeated fed-batch fermentation obtained prominent improvement compared to conventional batch cultivation, which can further facilitate economical rhamnolipids production at large scales.

In industrial applications, the vibration and temperature measurements of rolling element bearings are known as two popular condition monitoring methods. The previously published method for remaining useful life (RUL) prediction has been limited to using the vibration signal. However, a single signal source cannot fully reflect the degradation trend of bearings, influencing the RUL prediction precision. In this paper, a novel general log-linear Weibull (GLL-Weibull) model is constructed by considering vibration and temperature condition monitoring signals to estimate the model parameters. During the feature extraction stage, the relative root mean square (RRMS) is derived from the monitored vibration signal, and the relative temperature trend value is extracted from the monitored temperature signal to eliminate individual differences in bearings and random signal fluctuations. Then, a fuzzy operator is introduced to describe the degree of an “overheated bearing” and “excessive bearing vibrations.” During the RUL prediction stage, both the extracted vibration and temperature characteristics are used to create the GLL-Weibull model. The best parameters are attained by employing the maximum likelihood estimation approach. The algorithm performance is checked with criteria like the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The effectiveness and superiority of the presented approach are validated by two real-life prognosis cases. According to the experimental results, the presented approach provides superior prediction precision and lower computational cost than other approaches for bearings under constant or variable operating conditions.

This paper investigates the station-keeping control of autonomous and remotely-operated vehicles (ARVs) for free-floating manipulation under model uncertainties and external disturbances. A modified adaptive generalized super-twisting algorithm (AGSTA) enhanced by adaptive tracking differentiator (ATD) and reduced-order extended state observer (RESO) is proposed. The ATD is used to obtain the smooth reference signal and its derivative. The RESO is used to estimate and compensate for the model uncertainties and external disturbances in real-time, which enhances the robustness of the controller. The modified AGSTA ensures the fast convergence of the system states and maintains them in a predefined neighborhood of origin without overestimating control gains. Besides, the proposed new variable gain strategy completely avoids the control gains vibrating near the set minimum value. Thanks to the RESO, the proposed controller is model-free and can be easily implemented in practice. The stability of the closed-loop system is analyzed based on Lyapunov’s direct method in the time domain. Finally, the proposed control scheme is applied to the station-keeping control of Haidou-1 ARV, and the simulation results confirm the superiority of the proposed control scheme over the original AGSTA.

Rhamnolipids (RLs) are important bioproducts that are regarded as promising biosurfactant for applications in oil exploitation, cosmetics, and food industry. In this study, the newly isolated Pseudomonas aeruginosa KT1115 showed high production of di-RLs. The highest yield of RLs by P. aeruginosa KT1115, reaching 44.39 g/L after 8 days of fermentation in a 5 L bioreactor, was obtained from rapeseed oil-nitrate medium after process optimization. Furthermore, we established a new separation process that achieved up to 91.82% RLs recovery with a purity of 89% and further obtained mono/di-rhamnolipids. Finally, ESI-MS analysis showed that the RLs produced by strain KT1115 have a high proportion of di-RLs (mono-RLs: di-RLs = 11.47: 88.53), which have a lower critical micelle-forming concentration (8 mN/m) and better emulsification ability with kerosene (52.1% EI24) than mono-RLs (167 mN/m and 41.4% EI24, respectively). These results demonstrated that P. aeruginosa KT1115 is a potential industrial producer of di-RLs, which have improved applicability and offer significant commercial benefits.