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The working environment of coal mine mechanical equipment is harsh, the background noise is strong, and the early fault characteristic information of the bearing is weak. Therefore, it is difficult to extract the information reflecting the fault state from the vibration signal measured by the sensor. Moreover, the coal mine mechanical equipment work in high speed, shock and other working conditions, which are typical non-stationary working conditions. The unstable excitation and complex working conditions directly lead to the difficulty of extracting the bearing fault characteristic signal. In order to solve the above problems, a rolling bearing fault diagnosis method based on computed order analysis and adaptive stochastic resonance is proposed in the background of the working conditions of mine hoisting equipment. Firstly, the method simulates the typical variable speed working conditions in the operation of mine hoist, constructs the fault simulation signals and collects the experimental signals of bearing vibration. Secondly, by collecting synchronous time-domain key-phase signal at equal angles, the non-stationary vibration signal of the bearing is resampled into a stationary signal by using computed order analysis. Thirdly, the stationary signal is decomposed into a number of intrinsic mode function (IMF) components by using the variational mode decomposition (VMD) method, and the bearing fault type is judged by the bearing fault order. Finally, the adaptive stochastic resonance method is used to enhance the bearing fault characteristic order so as to achieve the extraction and enhancement of fault characteristics for fault diagnosis. The simulation and experimental results prove the effectiveness of the method. And the method is compared with the maximum correlation kurtosis deconvolution (MCKD) method. The results show that although the MCKD method can also observe the fault characteristic order, but the characteristic order is only 0.001 96 higher than the amplitude of the surrounding interference order, which is lower than the results of the proposed method, indicating the superiority of the proposed method.

Purpose. This study aimed to investigate nurses' perceptions of spirituality and spiritual care and perceived professional benefits in China and reveal the relationship between them. Design and Methods. In this study, 372 nurses (response rate: 93.47%) from a number of hospitals across China were surveyed using convenience sampling. The data were collected through online questionnaires, including a sociodemographic characteristics form, the Chinese version of the Spiritual Care-Giving Scale (C-SCGS), and the Nurses' Perceived Professional Benefits Questionnaire (NPPBQ). Findings. The total score of the NPPBQ was 136.88±20.13. A statistically significant relationship was found between the total score and subscale score of the C-SCGS and the total score and subscale score of the NPPBQ (R=0.217-0.475, P, Author(s): Xiaoying Zeng [1]; Jianhua Yang [1]; Yanjia Li [1]; Min Chen [2]; Jue Wu [3]; Yanli Hu [4]; Weinan Lu [1]; Jing Liu [1]; Qian Yang [1]; Wenqiong Guo [...]

In the past three decades, stepping-stone intrusion has become a professional and primary way used by intruders to launch their attacks since they can be protected behind a long TCP connection chain. Many different algorithms have been proposed to detect stepping-stone intrusion since 1995. But most algorithms cannot resist intruders' session manipulation. In this paper, we propose a novel approach using the distribution of round-trip time (RTT) of network traffic to detect stepping-stone intrusion. This approach can resist intruders' chaff-perturbation since the round-trip time of network packets can fairly be affected by chaffed packets. The ratio between the standard deviation of the RTTs between Send and Echo packets and the standard deviation of the RTTs between Send and Ack packets can be used to predict if a stepping-stone intrusion exists. The closer to 0 the ratio, the more suspicious a stepping-stone intrusion. [ABSTRACT FROM AUTHOR]

In this study, we introduce a distributed algorithm that is specifically designed to address optimization problems featuring a decomposable objective function and equality constraints. To minimize the amount of communication required, we incorporate an event-triggered mechanism that enables information exchange only when variable values exceed predefined thresholds. Importantly, our proposed algorithm possesses a distinctive characteristic where the determination of step size is solely based on the properties of the objective function, regardless of the structure of the communication network. Even in situations where changes occur in the network structure, our algorithm remains valid without necessitating any updates to its step size. Assuming strong convexity and smoothness in local objective functions, along with appropriate event-triggered thresholds, our algorithm achieves a convergence rate that is linear. Several numerical experiments provide evidence supporting the effectiveness and superiority of our proposed approach. [ABSTRACT FROM AUTHOR]

Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry technology offers a reliable means of calculating building heights with high precision. Here, we initially calculated building heights along satellite orbits based on building-rooftop contour vector datasets and ICESat-2 ATL03 photon data from 2019 to 2022. By integrating multi-source passive remote sensing observation data, we used the inferred building height results as reference data to train a random forest model, regressing building heights at a 10 m scale. Compared with ground-measured heights, building height samples constructed from ICESat-2 photon data outperformed methods that indirectly infer building heights using total building floor number. Moreover, the simulated building heights strongly correlated with actual observations at a single-city scale. Finally, using several years of inferred results, we analyzed building height changes in Tianjin from 2019 to 2022. Combined with the random forest model, the proposed model enables large-scale, high-precision inference of building heights with frequent updates, which has significant implications for global dynamic observation of urban three-dimensional features. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy of the sound quality of vehicle interior noise, a novel sound quality prediction model was proposed based on the physiological response predicted metrics, i.e., loudness, sharpness, and roughness. First, a human-ear sound transmission model was constructed by combining the outer and middle ear finite element model with the cochlear transmission line model. This model converted external input noise into cochlear basilar membrane response. Second, the physiological perception models of loudness, sharpness, and roughness were constructed by transforming the basilar membrane response into sound perception related to neuronal firing. Finally, taking the calculated loudness, sharpness, and roughness of the physiological model and the subjective evaluation values of vehicle interior noise as the parameters, a sound quality prediction model was constructed by TabNet model. The results demonstrate that the loudness, sharpness, and roughness computed by the human-ear physiological model exhibit a stronger correlation with the subjective evaluation of sound quality annoyance compared to traditional psychoacoustic parameters. Furthermore, the average error percentage of sound quality prediction based on the physiological model is only 3.81%, which is lower than that based on traditional psychoacoustic parameters. [ABSTRACT FROM AUTHOR]

Flexible ammonia (NH3) gas sensors have gained increasing attention for their potential in medical diagnostics and health monitoring, as they serve as a biomarker for kidney disease. Utilizing the pre-designable and porous properties of covalent organic frameworks (COFs) is an innovative way to address the demand for high-performance NH3 sensing. However, COF particles frequently encounter aggregation, low conductivity, and mechanical rigidity, reducing the effectiveness of portable NH3 detection. To overcome these challenges, we propose a practical approach using polyvinyl alcohol-carrageenan (κPVA) as a template for in the situ growth of two-dimensional COF film and particles to produce a flexible hydrogel gas sensor (COF/κPVA). The synergistic effect of COF and κPVA enhances the gas sensing, water retention, and mechanical properties. The COF/κPVA hydrogel shows a 54.4% response to 1 ppm NH3 with a root mean square error of less than 5% and full recovery compared to the low response and no recovery of bare κPVA. Owing to the dual effects of the COF film and the particles anchoring the water molecules, the COF/κPVA hydrogel remained stable after 70 h in atmospheric conditions, in contrast, the bare κPVA hydrogel was completely dehydrated. Our work might pave the way for highly sensitive hydrogel gas sensors, which have intriguing applications in flexible electronic devices for gas sensing. [ABSTRACT FROM AUTHOR]

Numerous studies have suggested that intra‐articular administration of antibiotics following primary revision surgery may be one of the methods for treating prosthetic joint infection (PJI). Vancomycin and meropenem are the two most commonly used antibiotics for local application. Determining the concentrations of vancomycin and meropenem in the serum and synovial fluid of patients with PJI plays a significant role in further optimizing local medication schemes and effectively eradicating biofilm infections. This study aimed to establish a rapid, sensitive, and accurate ultra‐performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) method for determining the concentrations of vancomycin and meropenem in human serum and synovial fluid. Serum samples were processed using acetonitrile precipitation of proteins and dichloromethane extraction, while synovial fluid samples were diluted before analysis. Chromatographic separation was achieved in 6 min on a Waters Acquity UPLC BEH C18 column, with the mobile phase consisting of 0.1% formic acid in water (solvent A) and acetonitrile (solvent B). Quantification was carried out using a Waters XEVO TQD triple quadrupole mass spectrometer with an electrospray ionization (ESI) source in positive ion mode. The multiple reaction monitoring (MRM) mode was employed to detect the following quantifier ion transitions: 717.95–99.97 (norvancomycin), 725.90–100.04 (vancomycin), 384.16–67.99 (meropenem). The method validation conformed to the guidelines of the FDA and the Chinese Pharmacopoeia. The method demonstrated good linearity within the range of 0.5–50 μg/ml for serum and 0.5–100 μg/ml for synovial fluid. Selectivity, intra‐day and inter‐day precision and accuracy, extraction recovery, matrix effect, and stability validation results all met the required standards. This method has been successfully applied in the pharmacokinetic/pharmacodynamic (PK/PD) studies of patients with PJI. [ABSTRACT FROM AUTHOR]