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The conventional diagnostic logics for hotbox warning/alarms are mainly based on predefined absolute temperature thresholds and temperature rising slope thresholds. They often fall short in effective analysis and judgments when encountering fluctuating measured temperature values. Abnormal jumps and missing of axle temperature measurements frequently occur on the hotbox monitoring system for high-speed trains due to external factors such as inadequate hardware circuit contact or electromagnetic interference, potentially triggering false warning/alarms. To solve these, this paper proposes a logic framework for removing abnormal temperature curves. Drawing insights from an extensive analysis of field data on axle temperature measurements, this approach enables extracting features that accurately depict variations in temperature curves, to display fluctuations as well as rising trends of temperature data, thereby offering a basis for identifying abnormal temperature variations. Simulation results showcased the effectiveness and viability of the proposed scheme in identifying abnormal jumps in temperature data. Furthermore, the methodology has been assessed through long-time track operation, with a notable reduction in the false warning rate from 78.3% to 26.5%.

The application of FPGA is becoming wider because of its advantages of reconfigurability, high performance, low power consumption, and strong real-time performance. However, its shortcomings such as high development threshold, low efficiency, and long development cycle become more prominent as the increase of system size and complexity. This paper uses model-based development methods to study the correlation algorithm of FPGA, including Simulink model building, test validation, code generation and simulation. The results of comparison with traditional manual coding show that the FPGA software development cycle using model-based design methods is only one fifth of the original method and the error in calculation results of the correlation coefficient is also under 0.3%. Although the length of generated code far exceeds that of handwritten code, the resource utilization of logic units, register, storage units, and multiplier units, etc. at runtime are less than those of handwritten code after code synthesis. It can be seen that model-based development methods not only meet the accuracy requirements (not exceeding ±5‰), but also greatly shorten the development cycle and lower the coding threshold.

The data-driven intelligent diagnosis of rolling bearing status suffers from low recognition rate due to the poor quality of learning samples in the process of identification model construction. To address this problem, a method is proposed to improve the recognition rate of the rolling bearing intelligent diagnosis model by selecting learning samples using the deep belief neural network. First, aiming at the time-varying modulation characteristics of the rolling bearing vibration signal, a finite number of intrinsic mode function components were decomposed using variational mode decomposition, which represents different components of the original signal. Secondly, according to the fault energy fluctuations and correlation of each component, the proportion of fault information in each intrinsic mode function components is quantitatively evaluated. On the basis of this, the vibration signal is screened and reconstructed to obtain learning samples. Finally, the obtained samples are used as the input of the deep belief network, and the fault identification model of the rolling bearing is constructed accordingly. Experimental results show that the proposed method is capable of screening out the intrinsic mode function components of the rolling bearing vibration signal, which contains the main components of the fault and realizes the construction of learning sample sets. Moreover, it improves the fault recognition rate of the rolling bearing state identification model based on vibration data.

In the transmission process, acceleration signal collected by the stability and instability detection device of EMU is easily interfered due to the long wiring and complex electromagnetic environment. If the interference signal is not fully suppressed, it is easy to produce aliasing phenomenon in the sampling process of analog conversion, resulting in false alarms or missing alarms, and affecting driving safety. The traditional anti-aliasing filter method uses analog high order (5~7 order) low pass filter to remove the interference signal outside the effective bandwidth. Although it meets the anti-aliasing requirement, it has some problems such as complex circuit, poor consistency and inconvenient parameter modification. Aiming at the problems, an improved digital-analog hybrid anti-aliasing filter scheme is proposed. Firstly, frequencies of aliasing points in the sampling process are improved by high-order over-sampling, so that the complexity of the analog filter circuit can be greatly reduced to the first-order one. Then, a digital FIR low-pass filter is used to remove the redundant signals brought by the over-sampling process. Test results show that the performance of the stability and instability detection device by the improved scheme is basically consistent with that of the one by pure hardware scheme, which meets the requirements of practical applications. Meanwhile, the problems of consistency and flexibility of high-order hardware filter circuit in traditional design are solved.

Nonlinear nanophotonic devices have brought about great advances in the fields of nano-optics, quantum science, biomedical engineering, etc. However, in order to push these nanophotonic devices out of laboratory, it is still highly necessary to improve their efficiency. Since obtaining novel nanomaterials with large nonlinearity is of crucial importance for improving the efficiency of nonlinear nanodevices, we propose the two-dimensional (2D) perovskites. Different from most previous studies which focused on the 2D perovskites in large scale (such as the bulk materials or the thick flakes), herein we studied the 2D perovskites nanosheets with thickness of ∼50 nm. The high-order nonlinear processes including multi-photon photoluminescence and third-harmonic generation (THG) have been systematically investigated, and it is found the THG process can have a high conversion efficiency up to ∼8 × 10−6. Also, it is observed that the nonlinear responses of 2D perovskites have large optical anisotropy, i.e., the polarization ratio for the incident polarization dependence of nonlinear response can be as high as ∼0.99, which is an impressive record in the perovskite systems. Our findings reveal the properties of high efficiency and huge optical anisotropy in the nonlinear processes of 2D perovskite nanosheets, shedding light on the design of advanced integrated nonlinear nanodevices in future.

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate. [ABSTRACT FROM AUTHOR]

Magnetic fluids are the suspensions composed of magnetic nanoparticles, surfactants, and non-magnetic carrier liquids. Magnetic fluids are widely used in various fields, especially in sealing, because of their excellent features, including rapid magnetic response, flexible flow ability, tunable magneto-viscous effect, and reliable self-repairing capability. Here, we provide an in-depth, comprehensive insight into the theoretical analyses and diverse applications of magnetic fluids in sealing from three categories: static sealing, rotary sealing, and reciprocating sealing. We summarize the magnetic fluid sealing mechanisms and the development of magnetic fluid seals from 1960s to the present, particularly focusing on the recent progress of magnetic fluid seals. Although magnetic fluid sealing technology has been commercialized and industrialized, many difficulties still exist in its applications. At the end of the review, the present challenges and future prospects in the progress of magnetic fluid seals are also outlined. [ABSTRACT FROM AUTHOR]

Magnetic fluid seals have long been thought to be a successful sealing form while sealing liquids are always a challenge. The instability of the liquid–liquid interface under the washout has become the key technical problem that hinders the realization of sealing liquid. This work mainly presents an experimental study about the influence of high viscosity and magnetoviscous effects on washout resistance. Three engine oil-based magnetic fluids of different viscosities were prepared with two kinds of surfactants. The magnetoviscous effects of the prepared magnetic fluids under different working conditions were found through rheological experiments. The viscosity of the three samples decreased at most by about 100 times with the shear rate increasing. An experimental platform was designed and built for the washout tests. The entire process of magnetic fluids being washed away was obtained experimentally. The magnetic fluid of higher viscosity can remain stationary with lower magnetic force. The quantitative results show that the viscosity of the magnetic fluid has a significant influence on washout resistance under a magnetic field. [ABSTRACT FROM AUTHOR]

The accuracy of the intelligent diagnosis of rolling bearings depends on the quality of its vibration data and the accuracy of the state identification model constructed accordingly. Aiming at the problem of "poor quality" of data and "difficult to select" structural parameters of the identification model, a method is proposed to integrate data cleaning in order to select effective learning samples and optimize the selection of the structural parameters of the deep belief network (DBN) model. First, by calculating the relative energy fluctuation value of the finite number of intrinsic function components using the variational modal decomposition of the rolling bearing vibration data, the proportion of each component containing the fault component is characterized. Then, high-quality learning samples are obtained through screening and reconstruction to achieve the effective cleaning of vibration data. Second, the improved particle swarm algorithm (IPSO) is used to optimize the number of nodes in each hidden layer of the DBN model in order to obtain the optimal structural parameters of the intelligent diagnosis model. Finally, the high-quality learning samples obtained from data cleaning are used as input to construct an intelligent identification model for rolling bearing faults. The results showed that the proposed method not only screens out the intrinsic mode function components that contain the fault effective components in the rolling bearing vibration data, but also finds the optimal solution for the number of nodes in the DBN hidden layer, which improves bearing state identification accuracy by 3%. [ABSTRACT FROM AUTHOR]

Magnetorheological fluid seal and brush seal are successful sealing forms. To address problems such as pole pieces colliding with spindles, precise sealing clearance in magnetic fluid seals, and inevitable leakage in brush seals, this article combines the advantages of magnetic fluid seal and brush seal and proposes a magnetorheological fluid seal with flexible pole pieces. The magnetic field of the seal was estimated using the finite-element method and the seal capacity was studied in theory. Single structural parameters of the seal that affect sealing capacity such as the relative permeability and thickness were discussed. The combined effects of various structural parameters were analyzed. The results show that an increase in the relative permeability and length of the flexible pole pieces leads to the enhancement of the seal capacity. The thickness of the flexible magnetic pole piece and the magnetic part of the flap and coating are inversely proportional to the sealing pressure value. The experimentally obtained seal capacity agrees with the value calculated through the simulation. This article presents a structure of the magnetorheological fluid seal and comparative results of the seal capacity obtained from both experimental studies and numerical simulation calculations. [ABSTRACT FROM AUTHOR]

The presumed DNA helicase encoded by ORF44 of Kaposi's sarcoma-associated herpesvirus (KSHV) plays a crucial role in unwinding viral double-stranded DNA and initiating DNA replication during lytic reactivation. However, the regulatory mechanism of KSHV ORF44 has not been fully elucidated. In a previous study, we identified that N-Myc downstream regulated gene 1 (NDRG1), a host scaffold protein, facilitates viral genome replication by interacting with proliferating cell nuclear antigen (PCNA) and the latent viral protein latency-associated nuclear antigen (LANA) during viral latency. In the present study, we further demonstrated that NDRG1 can interact with KSHV ORF44 during viral lytic replication. We also found that the mRNA and protein levels of NDRG1 were significantly increased by KSHV ORF50-encoded replication and transcription activator (RTA). Remarkably, knockdown of NDRG1 greatly decreased the protein level of ORF44 and impaired viral lytic replication. Interestingly, NDRG1 enhanced the stability of ORF44 and inhibited its ubiquitin-proteasome-mediated degradation by reducing the polyubiquitination of the lysine residues at positions 79 and 368 in ORF44. In summary, NDRG1 is a novel binding partner of ORF44 and facilitates viral lytic replication by maintaining the stability of ORF44. This study provides new insight into the mechanisms underlying KSHV lytic replication. Author summary: During lytic replication, KSHV ORF44 unwinds viral DNA and initiates DNA replication. Here, we report that the host protein NDRG1, a novel ORF44 binding partner, is significantly upregulated during viral lytic replication and facilitates this process. Mechanistically, NDRG1 can increase the stability of ORF44, impairing the polyubiquitination of the lysine residues at positions 79 and 368 in ORF44, thus inhibiting ubiquitin-proteasome-mediated degradation of ORF44. Our study demonstrates that NDRG1 plays an important role in KSHV lytic replication and may thus constitute a promising therapeutic target for KSHV infection. [ABSTRACT FROM AUTHOR]

Group B Streptococcus (GBS) is an important etiological agent of maternal and neonatal infections as well as postpartum women and individuals with impaired immunity. We developed and evaluated a rapid classification method for sequence types (STs) of GBS based on statistic models with Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF/MS). Whole-cell lysates MALDI-TOF/MS analysis was performed on 235 well-characterized GBS isolates from neonatal invasive infections in a multi-center study in China between 2015 and 2017. Mass spectra belonging to major STs (ST10, ST12, ST17, ST19, ST23) were selected for model generation and validation. Recognition and cross validation values were calculated by Genetic Algorithm-K Nearest Neighbor (GA-KNN), Supervised Neural Network (SNN), QuickClassifier (QC) to select models with the best performance for validation of diagnostic efficiency. Informative peaks were further screened through peak statistical analysis, ST subtyping MSP peak data and mass spectrum visualization. For major STs, the ML models generated by GA-KNN algorithms attained highest cross validation values in comparison to SNN and QC algorithms. GA-KNN models of ST10, ST17, and ST12/ST19 had good diagnostic efficiency, with high sensitivity (95–100%), specificity (91.46%–99.23%), accuracy (92.79–99.29%), positive prediction value (PPV, 80%–92.68%), negative prediction value (NPV, 94.32%–99.23%). Peak markers were firstly identified for ST10 (m/z 6250, 3125, 6891) and ST17 strains (m/z 2956, 5912, 7735, 5218). Statistical models for rapid GBS ST subtyping using MALDI-TOF/MS spectrometry contributes to easier epidemical molecular monitoring of GBS infection diseases. [ABSTRACT FROM AUTHOR]

This paper introduces an Unmanned Aerial Vehicle (UAV) image stitching method, based on the optimal seam algorithm and half-projective warp, that can effectively retain the original information of the image and obtain the ideal stitching effect. The existing seam stitching algorithms can eliminate the ghosting and blurring problems on the stitched images, but the deformation and angle distortion caused by image registration will remain in the stitching results. To overcome this situation, we propose a stitching strategy based on optimal seam and half-projective warp. Firstly, we define a new difference matrix in the overlapping region of the aligned image, which includes the color, structural and line difference information. Then, we constrain the search range of the seam by the minimum energy, and propose a seam search algorithm based on the global minimum energy to obtain the seam. Finally, combined with the seam position and half-projective warp, the shape of the stitched image is rectified to keep more regions in their original shape. The experimental results of several groups of UAV images show that our method has a superior stitching effect. [ABSTRACT FROM AUTHOR]