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Electronic information and optical properties coupled with the Quantum Theory of Atoms in Molecules (QTAIM) and Electron Localization Function (ELF) analyses are used to elucidate the erbium (Er+3) and praseodymium (Pr+3) intraband f–f transitions in the lithium tantalate (LiTaO3) doped and co-doped configurations and the metal-oxygen bonding. The generalized gradient approximation calculations show that the Er+3- and Pr+3-4f bands appear closer to the conduction band bottom for Er+3 and Pr+3 at the Li sites and to the valance band top for Er+3 at the Ta sites. However, the corresponding hybrid functional calculations for the dopants at the Li site show that the Er+3 and Pr+3-4f bands spread in energy, which agrees with the observed intraband f–f transitions from the optical properties calculations. QTAIM shows that Ta-, Er+3-, and Pr+3-O bonding is incipient covalent for all configurations of this work. The absence of ELF in the metal-O regions aligns with QTAIM on the lack of strong covalent bonding in these compounds. This complementary insight highlights how weakly interacting metal-O atoms lead to delocalized electron density, a feature that influences the physical, electronic, and chemical behavior of the LiTaO3. 

Abstract Expanding on the insights from our initial investigation into railway accident patterns, this paper delves deeper into the predictive capabilities of machine learning to forecast potential accident trends in railway crossings. Focusing on critical factors such as “Highway User Position” and “Equipment Involved,” we integrate Kernel Ridge Regression (KRR) models tailored to distinct clusters, as well as a global model for the entire dataset. These models, trained on historical data, discern patterns and correlations that might elude traditional statistical methods. Our findings are compelling: certain clusters, despite limited data points, showcase remarkably Root Mean Squared Error (RMSE) values between predictions and real data, indicating superior model performance. However, certain clusters hint at potential overfitting, given the disparities between model predictions and actual data. Conversely, clusters with vast datasets underperform compared to the global model, suggesting intricate interactions within the data that might challenge the model’s capabilities. The performance nuances across clusters emphasize the value of specialized, cluster-specific models in capturing the intricacies of each dataset segment. This study underscores the efficacy of KRR in predicting future railway crossing incidents, fostering the implementation of data-driven strategies in public safety. 

Abstract From 2013 to 2022, 1671 derailments have been reported by the Federal Railroad Administration (FRA), 8.2% of which were due to journal bearing defects. The University Transportation Center for Railway Safety (UTCRS) designed an onboard monitoring system that tracks vibration waveforms over time to assess bearing health through three analysis levels. However, the speed of the bearing, a fundamental parameter for these analyses, is often acquired from Global Positioning System (GPS) data, which is typically not available at the sensor location. To solve this issue, this paper proposes to employ Machine Learning (ML) algorithms to extract the speed and other essential features from existing vibration data, eliminating the need for additional speed sensors. Specifically, the proposed method tries to extract the speed information from the signatures that are embedded in the Power Spectral Density (PSD) plot, which enables rapid real-time analysis of bearings while the train is in motion. The rapid extraction of data could be sent to a cloud accessible by train dispatchers and railcar owners for assessment of bearings and scheduling of replacements before defects reach a dangerous size. Eventually, the developed algorithm will reduce derailments and unplanned field replacements and afford rail stakeholders more cost-effective preventive maintenance. 

Abstract This study employs graph mining and spectral clustering to analyze patterns in railway crossing accidents, utilizing a comprehensive dataset from the US Department of Transportation. By constructing a graph of implicit relationships between railway companies based on shared accident localities, we apply spectral clustering to identify distinct clusters of companies with similar accident patterns. This offers nuanced insight into the underlying structure of these incidents. Our results indicate that “Highway User Position” and “Equipment Involved” play pivotal roles in accident clustering, while temporal elements like “Date” and “Time” exert a diminished impact. This research not only sheds light on potential accident causation factors but also sets the stage for subsequent predictive safety analyses. It aims to serve as a cornerstone for future studies that aspire to leverage advanced data-driven techniques for improving railway crossing safety protocols. 

A thermoelectric energy harvesting device is evaluated to power a bearing health monitoring system. Unlike wayside equipment, the new system is an onboard wireless solution utilizing accelerometer and temperature sensors to assess the bearing condition continuously. The harvesting system consists of two thermoelectric generator modules with aluminium heat sinks, a switching boost converter, a battery management circuit, and a lithium rechargeable battery. The performance of the harvester is validated on an AAR class bearing mounted on a laboratory tester, with load and speed simulating common freight routes of up to 896 miles. The energy harvested varies with operating conditions, and data is presented showing the effect of load and speed. Over a realistic route, the net energy harvested is more than double that needed to indefinitely power a Bluetooth Low Energy sensor. The critical design parameters are the ratio of open-circuit voltage to the temperature difference for the thermoelectric module, and the cold start voltage of the boost converter.