Indiana University – Purdue University Columbus

Moisture recycling is an integral component of the hydrological cycle, enhancing regional water availability by returning evaporated moisture as precipitation either locally or downwind. In China, characterized by uneven water resource distribution and regional water scarcity, understanding moisture recycling dynamics is crucial for sustainable water management. Our study quantified the contribution of moisture recycling to precipitation and water availability across China and its sub‐regions during 2000–2022 using an atmospheric moisture tracking model. Results revealed that 59% of China's evapotranspiration (ET) was recycled within the region, contributing 299 mm/year to precipitation and 98 mm/year to water availability. Distinct spatial patterns emerged, with the highest ET contribution to precipitation and water availability in subtropical Central and South China (477 mm/year precipitation; 210 mm/year water availability) and the Qinghai‐Tibet Plateau (355 mm/year precipitation; 121 mm/year water availability). Water availability exchanges through moisture transport between sub‐regions were quantified, with Northeast China and subtropical Central and South China being net water availability recipients of 31 and 67 billion m 3 /year, respectively. The remaining sub‐regions were net exporters, with tropical South China and Northwest China experiencing the largest net exports, amounting to −40 and −30 billion m 3 /year, respectively. The study not only advances our understanding of the hydrological dynamics in China but also offers critical insights for sustainable water management practices aimed at mitigating challenges posed by water scarcity and climate change. It highlights the significance of integrating moisture recycling considerations into water resource management strategies to enhance resilience against hydroclimatic variabilities.

The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and, therefore, the areal extent of crop residue cover (CRC) could provide a rapid measure of the sustainability of agricultural production systems in a region. Recognizing the limitations of traditional CRC methods, a new method is proposed for estimating the spatial and temporal distribution of maize residue cover (MRC) in the Jilin Province, NE China. The method used random forest (RF) algorithms, 13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data. The tillage indices with the best predictive performance were STI and NDTI (R2 of 0.85 and 0.84, respectively). Among the texture features, the best-fitting was Band8AMean-5∗5 (R2 of 0.56 and 0.54 for the line-transect and photographic methods, respectively). Based on MSE and InNodePurity, the optimal combination of RF algorithm for the line-transect method was STI, NDTI, NDI7, NDRI5, SRNDI, NDRI6, NDRI7 and Band3Mean-3∗3. Likewise, the optimal combination of RF algorithm for the photographic method was STI, NDTI, NDI7, SRNDI, NDRI6, NDRI5, NDRI9 and Band3Mean-3∗3. Regional distribution of MRC in the Jilin Province, estimated using the RF prediction model, was higher in the central and southeast sections than in the northwest. That distribution was in line with the spatial heterogeneity of maize yield in the region. These findings showed that the RF algorithm can be used to map regional MRC and, therefore, represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.

Lane changing is common in driving. Thus, the possibility of traffic accidents occurring during lane changes is high given the complexity of this process. One of the primary objectives of intelligent driving is to increase a vehicle's behavior, making it more similar to that of a real driver. This study proposes a decision-making framework based on deep reinforcement learning (DRL) in a lane-changing scenario, which seeks to find a driving strategy that simultaneously considers the expected lane-changing risks and gains. First, a fuzzy logic lane-changing controller is designed. It outputs the corresponding safety and lane-change gain weights by inputting relevant driving parameters. Second, the obtained weights are brought into the constructed reward function of DRL. The model parameters are designed and trained on the basis of lane-changing behavior. Finally, we conducted experiments in a simulator to evaluate the performance of our developed algorithm in urban scenarios. To visualize and validate the estimated driving intentions, lane-changing strategies were tested under four scenarios. The results show that the average improvement in travel efficiency in the four scenarios is 19%. In addition, the average accident rate in the four scenarios increased by only 4%. We combine fuzzy logic and DRL reward functions to personify the lane-changing behavior of intelligent driving. Compared with conservative strategies that prioritize only safety, this method can considerably improve the number of lane changes and travel efficiency for autonomous vehicles (AVs) on the premise of ensuring safety. The approach provides an effective and explainable method designed for facilitating intelligent driving lane-changing behavior.

The trapped residual magnetic flux during the cool-down due to the incomplete Meissner state is a significant source of radio frequency losses in superconducting radio frequency cavities. Here, we clearly correlate the niobium microstructure in elliptical cavity geometry and flux expulsion behavior. In particular, a traditionally fabricated Nb cavity half-cell from an annealed poly-crystalline Nb sheet after an 800 degrees C heat treatment leads to a bi-modal microstructure that ties in with flux trapping and inefficient flux expulsion. This non-uniform microstructure is related to varying strain profiles along the cavity shape. A novel approach to prevent this non-uniform microstructure is presented by fabricating a 1.3 GHz single cell Nb cavity with a cold-worked sheet and subsequent heat treatment leading to better flux expulsion after 800 degrees C/3 h. Microstructural evolution by electron backscattered diffraction-orientation imaging microscopy on cavity cutouts, and flux pinning behavior by dc-magnetization on coupon samples confirms a reduction in flux pinning centers with increased heat treatment temperature. The heat treatment temperature-dependent mechanical properties and thermal conductivity are reported. The significant impact of cold work in this study demonstrates clear evidence for the importance of the microstructure required for high-performance superconducting cavities with reduced losses caused by magnetic flux trapping.

The well-known Berwald square metric is a positively complete and projectively flat Finsler metric with vanishing flag curvature. In this paper, we study a positively complete square metric on a manifold. We show a rigidity result that if the Ricci curvature is constant, then it must be isometric to the Berwald square metric. This is not true without assumption on the completeness of the metric.