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Abstract The connectivity of modern vehicles allows for the monitoring and analysis of a large amount of sensor data from vehicles during their normal operations. In recent years, there has been a growing interest in utilizing this data for the purposes of predictive maintenance. In this paper, a multi-label transfer learning approach is proposed using 14 different pretrained convolutional neural networks retrained with engine simulation data to predict the failure conditions of a selected set of engine components. The retrained classifier networks are designed such that concurrent failure modes of an exhaust gas recirculation, compressor, intercooler, and fuel injectors of a four-cylinder diesel engine can be identified. Time-series simulation data of various failure conditions, which include performance degradation, are generated to retrain the classifier networks to predict which components are failing at any given time. The test results of the retrained classifier networks show that the overall classification performance is good, with the normalized value of mean average precision varying from 0.6 to 0.65 for most of the retrained networks. To the best of the authors’ knowledge, this work represents the first attempt to characterize such time-series data utilizing a multi-label deep learning approach. 

AC/DC hybrid microgrids are becoming potentially more attractive due to the proliferation of renewable energy sources, such as photovoltaic generation, battery energy storage systems, and wind turbines. The collaboration of AC sub-microgrids and DC sub-microgrids improves operational efficiency when multiple types of power generators and loads coexist at the power distribution level. However, the voltage stability analysis and software validation of AC/DC hybrid microgrids is a critical concern, especially with the increasing adoption of power electronic devices and various types of power generation. In this manuscript, we investigate the modeling of AC/DC hybrid microgrids with grid-forming and grid-following power converters. We propose a rapid simulation technique to reduce the simulation runtime with acceptable errors. Moreover, we discuss the stability of hybrid microgrids with different types of faults and power mismatches. In particular, we examine the voltage nadir to evaluate the transient stability of the hybrid microgrid. We also design a droop controller to regulate the power flow and alleviate voltage instability. During our study, we establish a Simulink-based simulation platform for operational analysis of the microgrid. 

The rostral migratory stream (RMS) facilitates neuroblast migration from the subventricular zone to the olfactory bulb throughout adulthood. Brain lesions attract neuroblast migration out of the RMS, but resultant regeneration is insufficient. Increasing neuroblast migration into lesions has improved recovery in rodent studies. We previously developed techniques for fabricating an astrocyte-based Tissue-Engineered RMS (TE-RMS) intended to redirect endogenous neuroblasts into distal brain lesions for sustained neuronal replacement. Here, we demonstrate that astrocyte-like-cells can be derived from adult human gingiva mesenchymal stem cells and used for TE-RMS fabrication. We report that key proteins enriched in the RMS are enriched in TE-RMSs. Furthermore, the human TE-RMS facilitates directed migration of immature neurons in vitro. Finally, human TE-RMSs implanted in athymic rat brains redirect migration of neuroblasts out of the endogenous RMS. By emulating the brain’s most efficient means for directing neuroblast migration, the TE-RMS offers a promising new approach to neuroregenerative medicine.

 

We examine upper mantle anisotropy across the Antarctic continent using 102 new shear wave splitting measurements obtained from teleseismic SKS, SKKS, and PKS phases combined with 107 previously published results. For the new measurements, an eigenvalue technique is used to estimate the fast polarization direction and delay time for each phase arrival, and high‐quality measurements are stacked to determine the best‐fit splitting parameters at each seismic station. The ensemble of splitting measurements shows largely NE‐SW‐oriented fast polarization directions across Antarctica, with a broadly clockwise rotation in polarization directions evident moving from west to east across the continent. Although the first‐order pattern of NE‐SW‐oriented polarization directions is suggestive of a single plate‐wide source of anisotropy, we argue the observed pattern of anisotropy more likely arises from regionally variable contributions of both lithospheric and sub‐lithospheric mantle sources. Anisotropy observed in the interior of East Antarctica, a region underlain by thick lithosphere, can be attributed to relict fabrics associated with Precambrian tectonism. In contrast, anisotropy observed in coastal East Antarctica, the Transantarctic Mountains (TAM), and across much of West Antarctica likely reflects both lithospheric and sub‐lithospheric mantle fabrics. While sub‐lithospheric mantle fabrics are best associated with either plate motion‐induced asthenospheric flow or small‐scale convection, lithospheric mantle fabrics in coastal East Antarctica, the TAM, and West Antarctica generally reflect Jurassic—Cenozoic tectonic activity.

 

Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long‐term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER) network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in 50 or 100 yr based on long‐term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the “state change” theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo (tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds (the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea‐level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate‐induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across a diverse range of ecosystems and highlight the need for long‐term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.