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In this paper, we explore the iterated Crank–Nicolson (ICN) algorithm for the one-dimensional peridynamic model. The peridynamic equation of motion is an integro-differential equation that governs structural deformations such as fractures. The ICN method was originally developed for hyperbolic advection equations. In peridynamics, we apply the ICN algorithm for temporal discretization and the midpoint quadrature method for spatial integration. Several numerical tests are carried out to evaluate the performance of the ICN method. In general, the ICN method demonstrates second-order accuracy, consistent with the Störmer–Verlet (SV) method. When the weight is 1/3, the ICN method behaves as a third-order Runge–Kutta method and maintains strong stability-preserving (SSP) properties for linear problems. Regarding energy conservation, the ICN algorithm maintains at least second-order accuracy, making it superior to the SV method, which converges linearly. Furthermore, selecting a weight of 0.25 results in fourth-order superconvergent energy variation for the ICN method. In this case, the ICN method exhibits energy variation similar to that of the fourth-order Runge–Kutta method but operates approximately 20% faster. Higher-order convergence for energy can also be achieved by increasing the number of iterations in the ICN method.

Abstract Conductive grease has been considered as a potential solution in addressing electric erosion issue inside motor bearings. Understanding the conductive grease performance, especially the electric and tribological behaviour is crucial for its application. This paper combines bulk grease characterization and contact film study considering both electrical and tribological loading conditions. The results show that the electric performance for the selected conductive grease sample is electric field dependent, including both frequency and voltage amplitude. Its film impedance inside the contact area is closely related to the external electric field, running condition, and lubrication regime. The following film impedance calculation tells that not only bulk grease electric parameters, but the contact parameter, lubricant film contour and lubrication condition should be considered when performing the overall bearing electrical modelling.

The pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valuable alternative. This article intends to use the BP neural network to predict the safe operation status of pump stations and optimize the initial threshold and weight information of the BP network using the sparrow search algorithm (SSA) to improve the accuracy and generalization ability of the model. In addition, to more accurately reflect the correlation between various influencing factors and the safe operation status of the pumping station, the entropy weight method and the analytic hierarchy process were used to obtain the comprehensive weights of each main influencing factor. The experimental results show that the SSA-BP model can accurately predict the safe operation status of pumping stations, and compared with other traditional models, the SSA-BP model has better convergence and higher accuracy. This model provides a new approach for predicting the safe operation of pumping stations and has particular reference significance for predicting the safe operation of other pumping stations.

A novel temporal subgridding technique is proposed for the finite-difference time-domain (FDTD) method to solve two-dimensional Maxwell’s equations of electrodynamics in the TEz mode. Based on the subgridding FDTD algorithm with a separated spatial and temporal interface, our method focuses on the temporal subgridding region, as it is the main source of late-time instability. Different from other subgridding algorithms that work on the interpolation between coarse and fine meshes, our method stabilizes the solution by using iterative updating equations on the temporal coarse–fine mesh interface. This new method presents an alternative approach aimed at improving the stability of the subgridding technique without modifying the interpolation formulas. We numerically study the stability of the proposed algorithm via eigenvalue tests and by performing long-term simulations. We employ a refinement ratio of 2:1 in our study. Our findings indicate the stability of the conventional temporal subgridding FDTD algorithm with a magnetic field (Hz) interpolation. However, when electric fields (Ex and Ey) are utilized in interpolation, late-time instability occurs. In contrast, the proposed iteration-based method with an electric field interpolation appears to be stable. We further employ our method as the forward problem solver in the Through-the-Wall Radar (TWR) imaging application.

As the advantages of course-based undergraduate research experiences (CUREs) become widely accepted as a mainstream high-impact practice for undergraduate STEM education, it is paramount for instructors to be trained in effective practices for CURE development and implementation. The Mentoring the Integration of Research Into the Classroom (MIRIC) Network is a community of practice dedicated to providing current and future instructors with long-term support in implementing CUREs tailored to specific academic environments. Through the use of virtual synchronous all-network discussions as well as participant-selected affinity groups, MIRIC provides instructors with a dynamic and diverse community of educators in which to engage in long-term conversation in best practices in CURE development and implementation. Survey data on MIRIC participants reveals both success in providing instructors with the tools they need to succeed in CURE development and inspiration for evolving network focus and direction in the future.

A new diphenyl ether derivative, namely barceloneic lactone D (1), along with five known compounds (2-6) were isolated from the solid cultures of an endolichenic fungus, Preussia africana. The structure of new compound was elucidated by HR-ESI-MS, 1D and 2D NMR spectroscopy. All the isolates were evaluated for their antifungal activities. All compounds showed definite inhibitory activities with inhibition rate ranging from 9.2% to 79.2%. [ABSTRACT FROM AUTHOR]

A diagnosis of chronic kidney disease (CKD) may increase the risk for depression. The network perspective focuses on dynamic relationships among individual symptoms, which could advance our understanding of the development of depression during the transition to a diagnosis of CKD. The aim of this study was to use network analysis to examine the longitudinal associations of depressive symptoms from before to after a diagnosis of CKD. Method: The analytic sample included 1,386 participants from the Chinese Health and Retirement Longitudinal Study. Participants were aged 45 years or older and reported a doctor's diagnosis of CKD in any wave of interviews between 2011 and 2018. Depressive symptoms were measured by the 10-item version of the Center for Epidemiological Studies Depression. Cross-lagged panel network analysis was conducted to examine relationships between symptoms at three time points: prediagnosis; onset of diagnosis, and postdiagnosis). Results: After controlling for other symptoms and covariates, feeling unable to get going and less happiness at prediagnosis were the most predictive of other symptoms at the diagnosis of CKD. Feeling effortful to do everything and depressed mood at the diagnosis of CKD were the most predictive of other symptoms at postdiagnosis. Conclusions: Fatigue (i.e., feeling unable to get going, feeling effortful to do everything), less happiness, and depressed mood were central symptoms during the transition to a diagnosis of CKD. These findings highlight the benefits of identifying and managing these central symptoms to reduce the risk of activating other depressive symptoms. [ABSTRACT FROM AUTHOR]

In this study, 20 fungal strains isolated from Liupao tea were used for solid-state fermentation of sterilized raw Liupao tea from Cangwu Quntizhong. The fermentation performance of these strains was explored by sensory evaluation and biochemical analysis combined with principal component analysis (PCA) and strains that are able to significantly improve the quality of Liupao tea or impart new flavor to the tea were selected. The results showed that nine fungi with excellent fermentation performance were obtained, namely, Aspergillus tubingensis, A. niger, Penicillius manginii, A. keratitidis, A. ruber, A. intermedius, P. commune, A. amstelodami and Trichomonascus ciferrii. Moreover, A. tubingensis, A. niger, P. commune and A. amstelodami could significantly change the contents of 18 chemical components in tea samples. P. commune and A. amstelodami were identified as excellent starter cultures for Liupao tea fermentation. This study could provide a theoretical basis for the improvement of the fermentation process of Liupao tea and the development, innovation and quality improvement of tea products.