General Motors (United States)

Problem definition: We collaborate with an industrial partner whose supply chain uses multiple tiers, locations, and shipping speeds to efficiently serve customers. In practice, our partner also faces the possibility of upstream disruptions, which limit inventory availability. We model these key features of our partner’s network as a multiechelon distribution system (central warehouse and retailers) with expediting and disruptions. Methodology/results: We prove a novel stochastic program lower bound on optimal cost in this model and use this program to develop a heuristic base-stock policy. Our analysis demonstrates that there is a pronounced benefit from centralized inventory (i.e., holding inventory at the central warehouse) in distribution systems with expediting and disruptions as it can be used to both clear backlogs through expediting and hedge against future disruptions. Further, in the disrupted mode, we provide a simple criterion to determine when decentralization (i.e., holding inventory at the retailers) is preferred over complete centralization. Then, we validate our policies using data from our partner’s nationwide distribution network in the United States. Managerial implications: We provide novel inventory policies for managing a distribution system with expediting and disruptions that are understandable and implementable in practice. Our analysis provides the insight that facilitating the right level of central warehouse inventory is a critical hedge for improving performance in these systems. Finally, our industrial partner’s data suggest that our policies can provide significant cost savings in practice. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0249 .

Nickel-rich layered oxide cathodes are promising for next-generation lithium-ion batteries due to their high energy density and lower cost compared to the lithium cobalt oxide (LCO) cathode. However, their practical application is often limited by their thermal instability at high nickel content, which is associated with oxygen evolution and the risk of thermal runaway. In this study, discrete cerium oxide (CeO2) nanoparticles were coated on Li[Ni0.91Co0.05Mn0.03Al0.01]O2 (NCMA) cathodes via the coprecipitation method in water. CeO2 with the low redox potential of Ce3+/Ce4+ couple serves as an oxygen storage material on the surface of the NCMA cathode. The CeO2-coated NCMA (C-NCMA) shows 30% less total heat release and a higher onset temperature of cathode decomposition than uncoated NCMA (P-NCMA) due to 3.6% less oxygen release and robust barriers between the NCMA cathode surface and the electrolyte. Furthermore, long-term cycling stability at 25 °C and enhanced cycling performance at 45 °C with a low cutoff voltage (4.2 V) were observed for C-NCMA due to the suppression and delay of the H2–H3 phase transition peak, despite the kinetic hindrance from the relatively thick CeO2 nanoparticles. At a higher cutoff voltage (4.5 V) under severe cycling conditions, C-NCMA shows a capacity retention of 96.4% after 60 cycles compared to 84.4% for P-NCMA. To obtain both enhanced thermal stability and improved cycling performance, the cutoff voltage should be pushed to a higher value for these discrete CeO2 nanoparticle coating morphologies. This study presents an outlook on the rationale for applying discrete nanoparticle coatings to nickel-rich cathode materials for future battery development.

Lithium- and manganese-rich layered oxides (LMR) are promising high-capacity cathodes for next-generation lithium-ion batteries, yet their practical use is hindered by severe surface degradation and irreversible oxygen release under high-voltage operation. These surface instabilities not only accelerate voltage decay but also limit the structural integrity and cycle life. Here, we employ synchrotron-based scanning transmission X-ray microscopy (STXM), complemented by X-ray absorption fine structure (XAFS), to directly visualize shell-core-specific (surface-bulk) electronic reconstruction in LMR cathodes and establish its correlation with local atomic structures. We show that surface Al incorporation generates a controlled density of oxygen vacancies in the shell, which weakens 3d transition metal (TM)-O covalency while activating Ni t2g-O 2p-Mn t2g π-type superexchange (SE) interactions. This orbital reconfiguration stabilizes Ni in a high-spin Ni4+ configuration even at 4.6 V, allowing Ni t2g orbitals to buffer oxygen oxidation charges and thereby mitigate excessive oxygen anionic redox (OAR). As a result, irreversible O2 evolution and lattice collapse are noticeably suppressed, while XAFS analysis confirms shortened Ni-O bonds and reduced coordination loss in Al-modified LMR. Importantly, the Mn valence and 3d configuration remain unchanged, highlighting the fact that the stabilization is dominantly Ni-O driven. These findings demonstrate how nanoscale orbital engineering at the particle shell can suppress the level of OAR driven surface degradation, offering a practical pathway to improve the cycling durability and high-voltage stability of advanced LMR cathodes.

Prediction of solute clustering kinetics in aged multicomponent alloys requires a quantitative understanding of complex vacancy-cluster interactions across multiple scales. Here, we develop an integrated computational framework combining on-lattice kinetic Monte Carlo (KMC) simulations, absorbing Markov chain models, and mesoscale cluster dynamics (CD) to investigate these interactions in Al-Mg-Zn alloys. The Markov chain model yields vacancy escape times from solute clusters and identifies a two-stage behavior of the vacancy-cluster binding energy. These binding energies are used to estimate residual vacancy concentrations in the Al matrix after quenching, which serve as critical inputs to CD simulations to predict long-term cluster evolution kinetics during natural aging. Our results quantitatively demonstrate the significant impact of quench rate on natural aging kinetics. Results provide insights to guide alloy chemistry, quench rates, and aging time at finite temperatures to control the evolution of solute clusters and eventual precipitates in aged multicomponent alloys.

A religious lifestyle can be a powerful factor in predicting and promoting healthy functioning following trauma exposure. Previous research has demonstrated that higher levels of religiosity significantly predict posttraumatic growth following exposure to trauma. Firefighters are a population associated with routine exposure to potentially traumatic events, as well as high levels of occupational stress. While the relationship between religiosity and posttraumatic growth is well documented, the interaction of religious belief, occupational stress, and posttraumatic growth is less clear. The objective of this secondary analysis was to determine if religious belief moderates the relationship between occupational stress and posttraumatic growth in a sample of firefighters. The sample for this analysis was comprised of 109 firefighters who were assessed at various time points before fire service and throughout their first 3 years of fire service. A religious lifestyle did moderate the relationship between occupational stress and overall posttraumatic growth. However, a religious lifestyle and occupational stress were not significantly related. A religious lifestyle did moderate the relationship between occupational stress and the subdomains of spiritual enhancement and appreciation of life. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

This paper introduces a novel method for real-time rainfall rate determination in a given region, leveraging signals from connected vehicles as primary data sources. Traditional methods suffer from low temporal resolution and accessibility issues, prompting the need for an alternative approach. Our method utilizes vehicle sensor data, including wiper speed, temperature, humidity, and pressure, alongside ground truth data from Next Generation Weather Radars (NEXRAD). Through a two-stage process, a model is developed to predict the upper bound of rainfall rate based on aggregated vehicle signals. Validation of the model demonstrates its reliability and accuracy, with implications for enhancing advanced driver-assistance systems (ADAS) and ensuring safe driving in adverse weather conditions. This paper outlines the methodology, validation process, and potential applications of the proposed method, offering a practical solution for real-time rainfall prediction using existing connected vehicle infrastructure.

Increasing the number of lobes on a resolver can enhance several aspects of motor control performance, including resolution, signal quality, high-speed operation, commutation precision, and torque ripple reduction. However, selecting the optimal number of resolver lobes for a given machine pole pair is not straightforward. It requires balancing multiple performance trade-offs while also considering design complexity, manufacturing cost, and computational demands. This paper presents clear, structured guidelines to navigate these trade-offs and identify the optimal resolver configuration based on specific application requirements. All results are validated using a Hardware-in-the-Loop (HIL) testing platform to ensure practical applicability.