Velocity distribution characteristics and parametric sensitivity analysis of liquid nitrogen jet

Liquid nitrogen is expected to be used as a jet medium in petroleum engineering because of its cryogenic and non-polluting characteristics. To identify the velocity distribution characteristics of liquid nitrogen jet, a computational fluid dynamics model was built by coupling the equations for nitrogen properties. The velocity and pressure distributions of liquid nitrogen jet were analyzed by comparing them with water jet ones. Meanwhile, the influences of relevant parameters on the centerline velocity distributions of liquid nitrogen jet were researched as well. The simulation results showed that the liquid nitrogen jet not only displayed higher velocity but also presented fewer kinetic energy losses than the water jet during jetting process. The nozzle outlet velocity of liquid nitrogen jet was increased by increasing the nozzle pressure drop, and was slightly influenced by confining pressure and nozzle diameter. In the external space of the nozzle, the attenuation amplitude of centerline velocity was decreased with the growth of nozzle diameter, and was slightly influenced by nozzle pressure drop and confining pressure. This study is expected to provide a theoretical guide for parametric design of liquid nitrogen jet

Labor Costs of Implementing New Accounting Standards

While much research focuses on the informational benefits of new accounting standards, the costs of implementing new standards remain unclear. We examine the adoption of two new major standards: lease accounting and revenue recognition. We find increase in the number of accounting job postings, related to those standards, in standards’ issuance years. Firms most affected by new standards, measured by accounting complexity and early adoption behavior, post higher number of accounting jobs. We estimate incremental labor costs at about 30 percent of median audit fees for each standard for the most affected firms. These costs, as a percentage of their total employee cost, are higher for smaller firms, indicating greater regulatory-compliance burden. We provide large-sample evidence on the direct labor costs, and thus on the lower bound of implementation costs associated with new accounting standards. Our findings should interest standard setters as they evaluate cost-benefit tradeoffs before issuing new standards

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

Velocity distribution characteristics and parametric sensitivity analysis of liquid nitrogen jet

Liquid nitrogen is expected to be used as a jet medium in petroleum engineering because of its cryogenic and non-polluting characteristics. To identify the velocity distribution characteristics of liquid nitrogen jet, a computational fluid dynamics model was built by coupling the equations for nitrogen properties. The velocity and pressure distributions of liquid nitrogen jet were analyzed by comparing them with water jet ones. Meanwhile, the influences of relevant parameters on the centerline velocity distributions of liquid nitrogen jet were researched as well. The simulation results showed that the liquid nitrogen jet not only displayed higher velocity but also presented fewer kinetic energy losses than the water jet during jetting process. The nozzle outlet velocity of liquid nitrogen jet was increased by increasing the nozzle pressure drop, and was slightly influenced by confining pressure and nozzle diameter. In the external space of the nozzle, the attenuation amplitude of centerline velocity was decreased with the growth of nozzle diameter, and was slightly influenced by nozzle pressure drop and confining pressure. This study is expected to provide a theoretical guide for parametric design of liquid nitrogen jet

Large Variation of Mercury Isotope Composition During a Single Precipitation Event at Lhasa City, Tibetan Plateau, China

AbstractThis study examined for the first time the Hg isotope composition in rain samples from a single precipitation event at Lhasa City (China) on the Tibetan Plateau, the “world's third pole”. Large variations of both mass-dependent fractionation (MDF, δ202Hg from -0.80‰ to -0.42‰) and mass-independent fractionation (MIF, Δ199Hg from 0.38‰ to 0.76‰) were observed, with the latter increasing with time. Our results demonstrated that the large variation of Hg isotope ratios likely resulted from mixing of locally emitted Hg and long-term transported Hg, which were characterized by different Hg isotope signatures and mainly leached by below-cloud scavenging and in-cloud scavenging processes, respectively. Our findings demonstrated that Hg isotopes are a powerful tool for investigating the dynamics of precipitation events and emphasized the importance of systematic monitoring studies of the chemical and isotope variability of Hg and other elements during rainfall events