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Abstract This paper constructs a two-layer road data asset revenue allocation model based on a modified Shapley value approach. The first layer allocates revenue to three roles in the data value realization process: the original data collectors, the data processors, and the data product producers. It fully considers and appropriately adjusts the revenue allocation to each role based on data risk factors. The second layer determines the correction factors for different roles to distribute revenue among the participants within those roles. Finally, the revenue values of the participants within each role are synthesized to obtain a consolidated revenue distribution for each participant. Compared to the traditional Shapley value method, this model establishes a revenue allocation evaluation index system, uses entropy weighting and rough set theory to determine the weights, and adopts a fuzzy comprehensive evaluation and numerical analysis to assess the degree of contribution of participants. It fully accounts for differences in both the qualitative and quantitative contributions of participants, enabling a fairer and more reasonable distribution of revenues. This study provides new perspectives and methodologies for the benefit distribution mechanism in road data assets, which aid in promoting the market-based use of road data assets, and it serves as an important reference for the application of data assetization in the road transportation industry.

Abstract Bi-directional pedestrian flow in corridors is a complex dynamic system due to the diversity in pedestrian psychological characteristics. Incorporating individual differences of pedestrians is vital for improving pedestrian flow models. However, due to the inherent complexity and variability of pedestrian movement, model parameter calibration remains challenging. Controlled experiments are needed to collect empirical pedestrian movement data under different environments. This enriches the database on pedestrian movement patterns and provides necessary support for improving pedestrian flow models. To address this issue, we conducted controlled experiments to quantify pedestrian heterogeneity by defining the direction of fuzzy visual field (DFVF). The DFVF incorporates various static and dynamic pedestrian factors. We used it to modify the traditional cellular automata model. This improved model simulates bi-directional pedestrian movements in the corridors, reproduces density-speed and density-volume relationships, and reveals self-organization phenomena. Furthermore, an analysis was conducted to examine the impacts of pedestrian density and facility spatial layout on evacuation time. Pedestrian interactions were also studied to uncover fundamental bi-directional flow properties. As pedestrian density increased, the evacuation time showed an exponential upward trend. Corridor length significantly impacts evacuation time, while increasing corridor width helps control it. As crowd density increases, pedestrian flows exhibit three distinct steady states: the strolling flow at low densities, directional separated flows at medium densities, and dynamic multi-lane flows at high densities. In summary, the modified cellular automata model successfully incorporates pedestrian heterogeneity and reveals intrinsic bi-directional pedestrian flow patterns. This study provides valuable insights for pedestrian facility design and optimizing pedestrian flow organization.

Abstract Delayed intracerebral hemorrhage (DICH) secondary to ventriculoperitoneal (VP) shunt is considered to be a potentially severe event. This study aimed to investigate the association between a ratio of postoperative neutrophil-to-lymphocyte ratio to preoperative neutrophil-to-lymphocyte ratio (NLRR) and DICH secondary to VP shunt. We performed a retrospective review of patients who underwent VP shunt between January 2016 and June 2020. Multivariable logistic regression analysis was used to assess the association of DICH and NLRR. Then patients were divided into two groups according to the optimal cut-off point of NLRR, propensity score matching (PSM) method was performed to reconfirm the result. A total of 130 patients were enrolled and DICH occurred in 29 patients. Elevated NLRR and history of craniotomy were independent risk factors for DICH secondary to VP shunt. The optimal cut off point of NLRR was 2.05, and the sensitivity was 89.7%, the specificity was 63.4%. Patients with NLRR > 2.05 had much higher incidence of DICH (40.6% vs 4.5%). Our finding suggested that DICH following VP shunt was not a rare complication and elevated NLRR could independently predict DICH. Inflammatory responses might play an important role in the development of DICH following VP shunt.

Abstract Explore whether the axillary outcomes differ among HER2 positive subgroups receiving standard dual-targeted therapy, aiming to identify subgroups exhibiting enhanced sensitivity to NAT among HER2-positive/node-positive breast cancer patients. HER2 positive female patients with biopsy-proven node-positive disease from April 2020 to May 2023 were included. All patients underwent standard Neoadjuvant HER2-targeted dual therapy and axillary lymph node dissection (ALND) at Breast Surgery Center of Sichuan Cancer Hospital. Univariate and multivariate analyses were used to identify factors associate with axillary pathological complete response (ApCR). Statistical analysis and graphing were performed using SPSS 24.0 and GraphPad Prism 9.0 software. This study enrolled 215 HER2 positive patients with a total ApCR rate of 76.7%, which included 49 HER2 2+/FISH + and 166 HER2 3 + cases with approximate ApCR rates of 63.3% and 80.7% (P = 0.011). Univariate and multivariate analysis indicated that HER2 3 + disease (OR = 2.43, 95% CI 1.21–4.88, P = 0.012), Ki-67 ≥ 20% disease (OR = 3.00, 95% CI 1.26–7.13, P = 0.013) and NAC regimen of TCb (OR = 2.71, 95% CI 1.39–5.38, P = 0.004) were more likely to achieve ApCR. Further subgroup analysis revealed that HER2 3 + patients receiving TCb regimen showed the highest ApCR rate of 88% compared to other subgroups. HER2 3 + breast cancer had a higher ApCR rate than HER2 2+/FISH + breast cancer during Neoadjuvant HER2-targeted dual therapy. HER2 positive patients could benefit from NAC regimen of TCb in axillary response.

Abstract Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.

Abstract Acute kidney injury (AKI) represents a significant challenge to global public health problem and is associated with poor outcomes. There is still considerable debate about the effect of mean blood glucose (MBG) and coefficient of variation (CV) of blood glucose on the short-term mortality of AKI patients. This retrospective cohort study aimed to explore the association between glycemic variability and short-term mortality in patients with AKI. Data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed, including 6,777 adult AKI patients. MBG and CV on the first day of ICU admission were calculated to represent the overall glycemic status and variability during the ICU stay in AKI patients. The primary outcome indicator was ICU 30-day mortality of AKI patients. Multivariate Cox regression analysis and smoothed curve fitting were used to assess the relationship between blood glucose levels and mortality. Eventually, the ICU 30-day mortality rate of AKI patients was 23.5%. The increased MBG and CV were significantly correlated with ICU 30-day mortality (hazards ratio (HR) = 1.20, 95% confidence interval (CI) 1.14–1.27; HR = 1.08, 95% CI 1.03–1.13). The smoothed curve fitting showed a U-shaped relationship between MBG on the first day of ICU admission and ICU 30-day mortality (inflection point = 111.3 mg/dl), while CV had a linear relationship with 30-day ICU mortality. Thus, we conclude that MBG and CV were significantly associated with short-term mortality in intensive care patients with AKI. Tighter glycemic control may be an effective measure to improve the prognosis of patients with AKI.

Abstract Maca (Lepidium meyenii Walp.), originated in the high Andes of Peru, is rich in nutrients and phytochemicals. As a new resource food in China, Maca suffers marketing disorders due to the limitation of basic research. Due to the close relationship of Maca quality and origin of place, it’s of scientific, economic and social importance to set up a rapid, reliable and efficient method to identify Maca origin. In the present study, 303 Maca samples were collected from 101 villages of the main producing area in China. Using electronic nose and BP neutral network algorithm, a Maca odor database was set up to trace the origin. GC-MS was then employed to analyze the characteristic components qualitatively and semi-quantitatively. As a result, very significant differences (p

Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology, and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution, and diversity in cell size, and morphology. We believe this method would speed drawing the neural circuits and boost brain studies. [ABSTRACT FROM AUTHOR]