A Comparative Analysis of the Diagnostic Value of Shoulder MRI Plain Scan and MR Shoulder Arthrography for Rotator Cuff Injury

Objective: To analyze the diagnostic value of MRI plain scan and MR shoulder arthrography on rotator cuff injury. Methods: Sixty patients with suspected rotator cuff injury admitted to Yunnan South Central Hospital in Yunnan Province from October 2022 to March 2023 were selected, and all patients were scanned with MR conventional serial scan and MR arthrography, and the arthroscopic findings were used as the gold standard to compare the diagnostic results of the two methods on rotator cuff injury, the diagnostic value, the diagnostic value of different tear types, and the diagnostic value in oblique sagittal position for different parts of the display rate. Results: Arthroscopic findings were positive in 45 cases and negative in 15 cases; MR arthrography scans were positive in 42 cases and negative in 18 cases; MR conventional serial scans were positive in 39 cases and negative in 21 cases ; MR arthrography sensitivity was 88.89%, specificity 86.67%, accuracy 70.00%, positive predictive value 95.24% and 72.22% negative predictive value, while MR conventional serial scan was 84.44%, 93.33% specificity, 65.00% accuracy, 97.44% positive predictive value and 66.67% negative predictive value, with no statistically significant difference between the two methods (all P > 0.05); arthroscopy diagnosed 5 outer layer tears, 32 inner layer tears and 8 tendon MR arthrography diagnosed 6 outer layer tears, 33 inner layer tears and 6 intra-tendon tears, with an accuracy rate of 88.89% (40/45); MR conventional serial scan diagnosed 6 outer layer tears, 33 inner layer tears and 6 intra-tendon tears, with an accuracy rate of 84.44% (38/45). The difference in accuracy between the two groups was not&nbsp

Supervision Timing Simulation Analysis of Community E-commerce Platform Supply Chain Based on Tripartite Game Model

With the development of network and the popularity of e-commerce, the network service industry has shown strong development potential, and many community e-commerce platforms have emerged as the times require. In order to ensure the profit of the supply chain of community e-commerce platform and supervise whether the suppliers of enterprises and grid station service providers try their best to participate in value co-creation, this paper introduces the delay parameter a of community e-commerce platform, constructs a three-party evolutionary game model of "community e-commerce platform-grid station service provider-supplier", simulates the strategies of each agent with matlab, studies the behaviours of community e-commerce platform under different delay parameters, and concludes that the delay parameter a of community e-commerce platform has a great influence on the timing of community e-commerce platform supervision. Finally, three suggestions are put forward for the supervision of the supply chain of community e-commerce platform: (1) encourage consumers to report; (2) formulate the reward and punishment system for the settled enterprises; (3) formulating a reasonable supervision system

Difference or Indifference: China's Development Assistance Unpacked

China's growing role in international development through so?called ‘South–South cooperation’ has attracted considerable global attention. This article aims to provide a nuanced understanding of the nature of foreign aid policies implemented by China and help facilitate a new set of dialogues between China and more established providers of aid. It unpacks the developmental side of the story by first analysing the official discourse of Chinese aid in a historical context and thereafter examines the practice of conditional aid in relation to the Chinese emphasis on non?interference and mutual interest. The empirical basis for this article is largely derived from field studies undertaken in Malawi, Tanzania and Zimbabwe. We argue that although centrally controlled, Chinese aid has been consistently developmental, reflecting both the country's own development path and, to a lesser extent, international developmental goals

Added value of high resolution models in simulating global precipitation characteristics

Climate models tend to overestimate percentage of the contribution (to total precipitation) and frequency of light rainfall while underestimate the heavy rainfall. This article investigates the added value of high resolution of atmospheric general circulation models (AGCMs) in simulating the characteristics of global precipitation, in particular extremes. Three AGCMs, global high resolution atmospheric model from the Geophysical Fluid Dynamics Laboratory (GFDL-HiRAM), the Meteorological Research Institute-atmospheric general circulation model (MRI-AGCM) and the Met Office Unified Model (MetUM), each with one high and one low resolution configurations for the period 1998–2008 are used in this study. Some consistent improvements are found across all three AGCMs with increasing model resolution from 50–83 to 20–35 km. A reduction in global mean frequency and amount percentile of light rainfall (20 mm day−1) are shown in high resolution models of GFDL-HiRAM and MRI-AGCM, while the improvement in MetUM is not obvious. A consistent response to high resolution across the three AGCMs is seen from the increase of light rainfall frequency and amount percentile over the desert regions, particularly over the ocean desert regions. It suppresses the overestimation of CDD over ocean desert regions and makes a better performance in high resolution models of GFDL-HiRAM and MRI-AGCM, but worse in MetUM-N512. The impact of model resolution differs greatly among the three AGCMs in simulating the fraction of total precipitation exceeding the 95th percentile daily wet day precipitation. Inconsistencies among models with increased resolution mainly appear over the tropical oceans and in simulating extreme wet conditions, probably due to different reactions of dynamical and physical processes to the resolution, indicating their crucial role in high resolution modelling

The effect of horizontal resolution on the representation of the global monsoon annual cycle in Atmospheric General Circulation Models

The sensitivity of the representation of the global monsoon annual cycle to horizontal resolution is compared in three Atmospheric General Circulation Models (AGCMs): the Met Office Unified Model-Global Atmosphere 3.0 (MetUM-GA3), the Meteorological Research Institute AGCM3 (MRI-AGCM3) and Global High Resolution AGCM from the Geophysical Fluid Dynamics Laboratory (GFDL-HiRAM). For each model, we use two horizontal resolution configurations for the period 1998–2008. Increasing resolution consistently improves simulated precipitation and low-level circulation of the annual mean and the first two annual cycle modes, as measured by pattern correlation coefficient and Equitable Threat Score. Improvements in simulating the summer monsoon onset and withdrawal are region-dependent. No consistent response to resolution is found in simulating summer monsoon retreat. Regionally, increased resolution reduces the positive bias in simulated annual mean precipitation, the two annual-cycle modes over the West African monsoon and Northwestern Pacific monsoon. An overestimation of the solstitial mode and an underestimation of the equinoctial asymmetric mode of the East Asian monsoon are reduced in all high-resolution configurations. Systematic errors exist in lower-resolution models for simulating the onset and withdrawal of the summer monsoon. Higher resolution models consistently improve the early summer monsoon onset over East Asia and West Africa, but substantial differences exist in the responses over Indian monsoon region, where biases differ across the three low-resolution AGCMs. This study demonstrates the importance of a multi-model comparison when examining the added value of resolution and the importance of model physical parameterizations for the Indian monsoon simulation

Value of Autonomous Last-mile Delivery: Evidence from Alibaba

This paper provides the first empirical evidence of consumer responses to autonomous last-mile delivery using Alibaba\u27s recent implementation in Chinese university campuses as a case study. The study leverages customer-level data from three universities over three years, employing a difference-in-differences (DID) approach combined with dynamic matching to estimate the impact of autonomous delivery adoption on order quantities. The results reveal a significant increase in the number of orders following autonomous delivery adoption with a 21% growth. The efficiency and flexibility of autonomous vehicles reduce consumers\u27 travel costs, driving long-term usage and increased sales. However, the value of autonomous delivery diminishes when a fee is charged. The study contributes to our understanding of the value of autonomous last-mile delivery and its potential advantages over traditional courier delivery

AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by explicitly constructing domain-specific networks, but the computation and memory boost significantly with the increase of domains. To reduce computational complexity, manually grouping domains with particular business strategies is common in industrial applications. However, this pre-defined data partitioning way heavily relies on prior knowledge, and it may neglect the underlying data distribution of each domain, hence limiting the model's representation capability. Regarding the above issues, we propose an elegant and flexible multi-distribution modeling paradigm, named Adaptive Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization hierarchical structure consisting of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Extensive experiments on both public and large-scale Alibaba industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our model achieves impressive prediction accuracy and its time cost during the training stage is more than 50% less than that of other models

Sequential Star Formation in the filamentary structures of Planck Galactic cold clump G181.84+0.31

We present a multi-wavelength study of the Planck cold clump G181.84+0.31, which is located at the northern end of the extended filamentary structure S242. We have extracted 9 compact dense cores from the SCUBA-2 850 um map, and we have identified 18 young stellar objects (YSOs, 4 Class I and 14 Class II) based on their Spitzer, Wide-field Infrared Survey Explorer (WISE) and Two-Micron All-Sky Survey (2MASS) near- and mid-infrared colours. The dense cores and YSOs are mainly distributed along the filamentary structures of G181.84 and are well traced by HCO$^{+}$(1-0) and N$_{2}$H$^{+}$(1-0) spectral-line emission. We find signatures of sequential star formation activities in G181.84: dense cores and YSOs located in the northern and southern sub-structures are younger than those in the central region. We also detect global velocity gradients of about 0.8$\pm$0.05 km s$^{-1}$pc$^{-1}$ and 1.0$\pm$0.05 km s$^{-1}$pc$^{-1}$ along the northern and southern sub-structures, respectively, and local velocity gradients of 1.2$\pm$0.1 km s$^{-1}$pc$^{-1}$ in the central substructure. These results may be due to the fact that the global collapse of the extended filamentary structure S242 is driven by an edge effect, for which the filament edges collapse first and then further trigger star formation activities inward. We identify three substructures in G181.84 and estimate their critical masses per unit length, which are $\sim$ 101$\pm$15 M$_{\odot}$ pc$^{-1}$, 56$\pm$8 M$_{\odot}$ pc$^{-1}$ and 28$\pm$4 M$_{\odot}$ pc$^{-1}$, respectively. These values are all lower than the observed values ($\sim$ 200 M$_{\odot}$ pc$^{-1}$), suggesting that these sub-structures are gravitationally unstable.Comment: 20 pages, 17 figures, article, accepte

G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System

Text-based delivery addresses, as the data foundation for logistics systems, contain abundant and crucial location information. How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural Language Process (NLP) have emerged as the dominant tools for encoding semantic information in text. Though promising, those NLP-based PTMs fall short of encoding geographic knowledge in the delivery address, which considerably trims down the performance of delivery-related tasks in logistic systems such as Cainiao. To tackle the above problem, we propose a domain-specific pre-trained model, named G2PTL, a Geography-Graph Pre-trained model for delivery address in Logistics field. G2PTL combines the semantic learning capabilities of text pre-training with the geographical-relationship encoding abilities of graph modeling. Specifically, we first utilize real-world logistics delivery data to construct a large-scale heterogeneous graph of delivery addresses, which contains abundant geographic knowledge and delivery information. Then, G2PTL is pre-trained with subgraphs sampled from the heterogeneous graph. Comprehensive experiments are conducted to demonstrate the effectiveness of G2PTL through four downstream tasks in logistics systems on real-world datasets. G2PTL has been deployed in production in Cainiao's logistics system, which significantly improves the performance of delivery-related tasks