Global explicit particle-in-cell simulations of the nonstationary bow shock and magnetosphere

We carry out two-dimensional global particle-in-cell simulations of the interaction between the solar wind and a dipole field to study the formation of the bow shock and magnetosphere. A self-reforming bow shock ahead of a dipole field is presented by using relatively high temporal-spatial resolutions. We find that (1) the bow shock and the magnetosphere are formed and reach a quasi-stable state after several ion cyclotron periods, and (2) under the Bz southward solar wind condition the bow shock undergoes a self-reformation for low \b{eta}i and high MA. Simultaneously, a magnetic reconnection in the magnetotail is found. For high \b{eta}i and low MA, the shock becomes quasi-stationary, and the magnetotail reconnection disappears. In addition, (3) the magnetopause deflects the magnetosheath plasmas. The sheath particles injected at the quasi-perpendicular region of the bow shock can be convected to downstream of an oblique shock region. A fraction of these sheath particles can leak out from the magnetosheath at the wings of the bow shock. Hence, the downstream situation is more complicated than that for a planar shock produced in local simulations.Comment: in ApJS, 201

Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scale, due to a lack of observations at local and seasonal time scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5 degree) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that global total water withdrawal has increased significantly during 1971–2010, mainly driven by the increase of irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of annual total irrigation water withdrawal in mid and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual scales. The reconstructed gridded water withdrawal dataset is open-access, and can be used for examining issues related to water withdrawals at fine spatial, temporal and sectoral scales

Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents

This empirical study serves as a primer for interested service providers to determine if and how Large Language Models (LLMs) technology will be integrated for their practitioners and the broader community. We investigate the mutual learning journey of non-AI experts and AI through CoAGent, a service co-creation tool with LLM-based agents. Engaging in a three-stage participatory design processes, we work with with 23 domain experts from public libraries across the U.S., uncovering their fundamental challenges of integrating AI into human workflows. Our findings provide 23 actionable "heuristics for service co-creation with AI", highlighting the nuanced shared responsibilities between humans and AI. We further exemplar 9 foundational agency aspects for AI, emphasizing essentials like ownership, fair treatment, and freedom of expression. Our innovative approach enriches the participatory design model by incorporating AI as crucial stakeholders and utilizing AI-AI interaction to identify blind spots. Collectively, these insights pave the way for synergistic and ethical human-AI co-creation in service contexts, preparing for workforce ecosystems where AI coexists.Comment: V1.0 on Oct 25th, 202

A Review of Surface Treatments for Sliding Bearings Used at Different Temperature

The boundary lubrication and dry friction of plain bearings at different work temperature are unable to be avoided under the start and stop condition. The poor lubrication is one reason of bearing broken. In order to improve the tribological properties and select the best treatment for different bearings used at different temperature, the studies of different treatment technologies are reviewed in this paper. The review shows that the shortages of bonding fiber woven materials, inlaying solid lubricating materials, electro plating and magnetron sputtering are poor temperature resistance, low load capacity, environment pollution and low production efficiencies respectively. Based on the analyses and summaries, the liquid dope spraying and thermal powder spraying are suggested to deposit coating on the surface of bearing which working temperature is lower than 200 and above 800°C respectively. However, the technology processes, the mechanisms of spraying and self-lubrication materials should be studied further and deeply

Spatial-temporal pattern and driving mechanism of urban land use eco-efficiency in mountainous counties based on multi-source data: a case study of Zhejiang province, China

Improving urban land use eco-efficiency (ULUEE) is of great significance for promoting high-quality economic development and promoting the modernization of harmonious coexistence between humans and nature. In this study, the super efficiency SBM model with undesirable output was used to measure the level of ULUEE at the county scale in Zhejiang province from 2006 to 2022. Based on this, the spatial-temporal evolution and spatial agglomeration characteristics were analyzed by using spatial analysis techniques, kernel density analysis, and spatial autocorrelation model. Finally, the driving mechanisms were revealed by using the geographical detector model and GWR model. The results were as follows: (1) From 2006 to 2022, the ULUEE of Zhejiang province rose from 0.34 to 0.73, with an average annual growth rate of 2.44%. The degree of efficiency differences between counties gradually converged. (2) The ULUEE at the county level exhibited a significant spatial positive correlation, with Moran’s I index increasing from 0.3219 to 0.3715. On the local scale, the cold spot significant area was mainly distributed in the north and south of Zhejiang province, and significant spatial and temporal variations were observed within the hot spot significant area. (3) The results of factor detection showed that altitude (X1), topographic relief (X2), and forest cover (X3) always played a strong role in affecting ULUEE. Among the socioeconomic factors, foreign trade (X8) had the strongest explanatory power in the early period, and GDP per capita (X5) and industrial structure (X6) played the strongest role in the later period. The explanatory power of all influencing factors decreased over time. (4) At the local scale, GDP per capita (X5), industrial structure (X6), and fiscal expenditure scale (X7) presented positive effects on ULUEE, and development vitality (X9) presented a negative effect. Future endeavors should encompass a multifaceted approach, which includes the facilitation of industrial modernization and the enhancement of external economic engagement. Concurrently, it is imperative to capitalize on the region’s inherent economic strengths and to foster a low-carbon, environmentally sustainable economic model