A new pricing model of China’s parallel rail lines under the diversified property rights

Purpose: The purpose of this paper is to study on the pricing of China railway company under the background of diversified property rights, especially the pricing of the parallel line system that belong to different owners. Design/methodology/approach: Through theoretical analysis of the main influential factors of railway pricing, this paper designs a basic quotation system for the parallel railway lines. Findings: The transaction price of parallel line consists of two parts, which are fixed railway network price and variable network using price. Practical implications: Through the reasonable designing of fixed network price and variable network using price, it can not only lead to high profitability and low government subsidy, but also can ensure remaining more railway network resources and fulfill the social responsibilities. Originality/value: The conclusions of this study will lay the foundation for the harmonious development of Chinese railway network under the diversified property rights.Peer Reviewe

A dataset of 30-meter annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States

Fine-resolution satellite observations show great potential for characterizing seasonal and annual dynamics of vegetation phenology in urban domains, from local to regional and global scales. However, most previous studies were conducted using coarse or moderate resolution data, which are inadequate for characterizing the spatiotemporal dynamics of vegetation phenology in urban domains. In this study, we produced an annual vegetation phenology dataset in urban ecosystems for the conterminous United States (US), using all available Landsat images on the Google Earth Engine (GEE) platform. First, we characterized the long-term mean seasonal pattern of phenology indicators of the start of season (SOS) and the end of season (EOS), using a double logistic model. Then, we identified the annual variability of these two phenology indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived phenology indicators agree well with in-situ observations from PhenoCam network and Harvard Forest. Comparing with results derived from the moderate resolution imaging spectroradiometer (MODIS) data, our Landsat derived phenology indicators can provide more spatial details. Also, temporal trends of phenology indicators (e.g., SOS) derived from Landsat and MODIS are consistent overall, but the Landsat derived results from 1985 have a longer temporal span compared to MODIS from 2001. In general, there is a spatially explicit pattern of phenology indicators from the North to the South in cities in the conterminous US, with an overall advanced SOS in the past three decades. The derived phenology product in the US urban domains at the national level is of great use for urban ecology studies for its fine spatial resolution (30 m) and long temporal span (30 years). The data are available at https://doi.org/10.6084/m9.figshare.7685645.v2

Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine

The Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales

The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot

Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images from diverse geographical contexts. Our exploration involved testing SAM across multi-scale datasets using various input prompts, such as bounding boxes, individual points, and text descriptors. To enhance the model's performance, we implemented a novel automated technique that combines a text-prompt-derived general example with one-shot training. This adjustment resulted in an improvement in accuracy, underscoring SAM's potential for deployment in remote sensing imagery and reducing the need for manual annotation. Despite the limitations encountered with lower spatial resolution images, SAM exhibits promising adaptability to remote sensing data analysis. We recommend future research to enhance the model's proficiency through integration with supplementary fine-tuning techniques and other networks. Furthermore, we provide the open-source code of our modifications on online repositories, encouraging further and broader adaptations of SAM to the remote sensing domain.Comment: 20 pages, 9 figure

Preoperative strain ultrasound elastography can predict occult central cervical lymph node metastasis in papillary thyroid cancer: a single-center retrospective study

ObjectiveTo determine whether preoperative ultrasound elastography can predict occult central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid cancer.MethodsThis retrospective study included 541 papillary thyroid cancer patients with clinically negative lymph nodes prior to surgery between July 2019 and December 2021. Based on whether CCLNM was present on postoperative pathology, patients were categorized as CCLNM (+) or CCLNM (-). Preoperative clinical data, conventional ultrasound features, and ultrasound elastography indices were compared between the groups. Univariate and multivariate logistic regression analysis were performed to identify the independent predictors of occult CCLNM.ResultsA total of 36.60% (198/541) patients had confirmed CCLNM, while 63.40% (343/541) did not. Tumor location, bilaterality, multifocality, echogenicity, margin, shape, vascularity, capsule contact, extrathyroidal extension, aspect ratio, and shear wave elasticity parameters were comparable between the groups (all P > 0.05). Univariate analysis showed statistically significant differences between the two groups in age, sex, tumor size, calcification, capsule invasion, and strain rates ratio in strain ultrasound elastography (all P < 0.05). In multivariate logistic regression analysis, the independent predictors of occult CCLNM were age (OR = 0.975, 95% CI = 0.959-0.991, P = 0.002), sex (OR = 1.886, 95% CI = 1.220-2.915, P = 0.004), tumor size (OR = 1.054, 95% CI = 1.014-1.097, P = 0.008), and strain rates ratio (OR = 1.178, 95% CI = 1.065-1.304, P = 0.002).ConclusionPreoperative strain ultrasound elastography can predict presence of occult CCLNM in papillary thyroid cancer patients and help clinicians select the appropriate treatment strategy

Maize microrna166 inactivation confers plant development and abiotic stress resistance

MicroRNAs are important regulators in plant developmental processes and stress responses. In this study, we generated a series of maize STTM166 transgenic plants. Knock-down of miR166 resulted in various morphological changes, including rolled leaves, enhanced abiotic stress resistance, inferior yield-related traits, vascular pattern and epidermis structures, tassel architecture, as well as abscisic acid (ABA) level elevation and indole acetic acid (IAA) level reduction in maize. To profile miR166 regulated genes, we performed RNA-seq and qRT-PCR analysis. A total of 178 differentially expressed genes (DEGs) were identified, including 118 up-regulated and 60 down-regulated genes. These DEGs were strongly enriched in cell and intercellular components, cell membrane system components, oxidoreductase activity, single organism metabolic process, carbohydrate metabolic process, and oxidation reduction process. These results indicated that miR166 plays important roles in auxin and ABA interaction in monocots, yet the specific mechanism may differ from dicots. The enhanced abiotic stress resistance is partly caused via rolling leaves, high ABA content, modulated vascular structure, and the potential changes of cell membrane structure. The inferior yield-related traits and late flowering are partly controlled by the decreased IAA content, the interplay of miR166 with other miRNAs and AGOs. Taken together, the present study uncovered novel functions of miR166 in maize, and provide insights on applying short tandem target mimics (STTM) technology in plant breeding

The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset

Floodplains provide essential ecosystem functions, yet \u3e80% of European and North American floodplains are substantially modified. Despite floodplain changes over the past century, comprehensive, long-term land use change data within large river basin floodplains are limited. Long-term land use data can be used to quantify floodplain functions and provide spatially explicit information for management, restoration, and flood-risk mitigation. We present a comprehensive dataset quantifying floodplain land use change along the 3.3 million km2 Mississippi River Basin (MRB) covering 60 years (1941–2000) at 250-m resolution. We developed four unique products as part of this work, a(n): (i) Google Earth Engine interactive map visualization interface, (ii) Python code that runs in any internet browser, (iii) online tutorial with visualizations facilitating classroom code application, and (iv) instructional video demonstrating code application and database reproduction. Our data show that MRB’s natural floodplain ecosystems have been substantially altered to agricultural and developed land uses. These products will support MRB resilience and sustainability goals by advancing data-driven decision making on floodplain restoration, buyout, and conservation scenarios

Black holes regulate cold gas accretion in massive galaxies

Nearly every massive galaxy contains a supermassive black hole (BH) at its center. For decades, both theory and numerical simulations have indicated that BHs play a central role in regulating the growth and quenching of galaxies. Specifically, BH feedback by heating or blowing out the interstellar medium (ISM) serves as the groundwork for current models of massive galaxy formation. However, direct evidence for such an impact on the galaxy-wide ISM from BHs has only been found in some extreme objects. For general galaxy populations, it remains unclear whether and how BHs impact the ISM. Here based on a large sample of nearby galaxies with measurements of masses of both black holes and atomic hydrogen, the major component of cold ISM, we reveal that the atomic hydrogen content ($f_{\rm HI} = M_{\rm HI}/M_{\star}$) is tightly and anti-correlated with black hole mass ($M_{\rm BH}$) with $f_{\rm HI} \propto M^{-\alpha}_{\rm BH}$ ($\alpha \sim 0.5-0.6$). This correlation is valid across five orders of magnitude in $M_{\rm BH}$. Once this correlation is taken into account, $f_{\rm HI}$ loses dependence on other galactic parameters, demonstrating that $M_{\rm BH}$ serves as the primary driver of $f_{\rm HI}$. These findings provide critical evidence for how the accumulated energy from BH accretion impacts galaxy-wide ISM, representing a crucial step forward in our understanding on the role of BHs in regulating the growth and quenching of massive galaxies.Comment: 24 pages, 7 figures. Submitted to Natur