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Abstract Accurate classification of rail transit stations is crucial for successful Transit-Oriented Development (TOD) and sustainable urban growth. This paper introduces a novel classification model integrating traditional methodologies with advanced machine learning algorithms. By employing mathematical models, clustering methods, and neural network techniques, the model enhances the precision of station classification, allowing for a refined evaluation of station attributes. A comprehensive case study on the Chengdu rail transit network validates the model’s efficacy, highlighting its value in optimizing TOD strategies and guiding decision-making processes for urban planners and policymakers. The study employs several regression models trained on existing data to generate accurate ridership forecasts, and data clustering using mathematical algorithms reveals distinct categories of stations. Evaluation metrics confirm the rationality and accuracy of the results. Additionally, a neural network achieving high accuracy on labeled data enhances the model’s predictive capabilities for unlabeled instances. The research demonstrates high accuracy, with the Mean Squared Error (MSE) for regression models (Multiple Linear Regression (MLR), Deep-Learning Neural Network (DNN), and K-Nearest Neighbor (KNN)) remaining below 0.012, while the neural networks used for station classification achieve 100% accuracy across seven time intervals and 98.15% accuracy for the eighth, ensuring reliable ridership forecasts and classification outcomes. Accuracy in rail transit station classification is critical, as it not only strengthens the model’s predictive capabilities but also ensures more reliable data-driven decisions for transit planning and development, allowing for more precise ridership forecasts and evidence-based strategies for optimizing TOD. This classification model provides stakeholders with valuable insights into the dynamics and features of rail transit stations, supporting sustainable urban development planning.

Abstract The intracellular bacterial pathogen Legionella pneumophila modulates host cell functions by secreting multiple effectors with diverse biochemical activities. In particular, effectors of the SidE family interfere with host protein ubiquitination in a process that involves production of phosphoribosyl ubiquitin (PR-Ub). Here, we show that effector LnaB converts PR-Ub into ADP-ribosylated ubiquitin, which is further processed to ADP-ribose and functional ubiquitin by the (ADP-ribosyl)hydrolase MavL, thus maintaining ubiquitin homeostasis in infected cells. Upon being activated by actin, LnaB also undergoes self-AMPylation on tyrosine residues. The activity of LnaB requires a motif consisting of Ser, His and Glu (SHxxxE) present in a large family of toxins from diverse bacterial pathogens. Thus, our study sheds light on the mechanisms by which a pathogen maintains ubiquitin homeostasis and identifies a family of enzymes capable of protein AMPylation.

Abstract ADP-ribosylation is a reversible post-translational modification involved in various cellular activities. Removal of ADP-ribosylation requires (ADP-ribosyl)hydrolases, with macrodomain enzymes being a major family in this category. The pathogen Legionella pneumophila mediates atypical ubiquitination of host targets using the SidE effector family in a process that involves ubiquitin ADP-ribosylation on arginine 42 as an obligatory step. Here, we show that the Legionella macrodomain effector MavL regulates this pathway by reversing the arginine ADP-ribosylation, likely to minimize potential detrimental effects caused by the modified ubiquitin. We determine the crystal structure of ADP-ribose-bound MavL, providing structural insights into recognition of the ADP-ribosyl group and catalytic mechanism of its removal. Further analyses reveal DUF4804 as a class of MavL-like macrodomain enzymes whose representative members show unique selectivity for mono-ADP-ribosylated arginine residue in synthetic substrates. We find such enzymes are also present in eukaryotes, as exemplified by two previously uncharacterized (ADP-ribosyl)hydrolases in Drosophila melanogaster. Crystal structures of several proteins in this class provide insights into arginine specificity and a shared mode of ADP-ribose interaction distinct from previously characterized macrodomains. Collectively, our study reveals a new regulatory layer of SidE-catalyzed ubiquitination and expands the current understanding of macrodomain enzymes.

Abstract This study investigated attapulgite-modified loess as an efficient and cost-effective method for creating an impermeable liner for landfills in regions with scarce clay resources. Laboratory permeability tests were conducted using a flexible wall permeameter to determine the permeability of compacted loess and attapulgite mixtures under varying osmotic conditions. The relationship between the permeability coefficient, attapulgite dosage, radial pressure, and osmotic pressure was analyzed. Nuclear magnetic resonance and scanning electron microscopy were also used to observe the microstructure of the modified loess. The results showed that attapulgite dosage significantly reduced the permeability coefficient, but the effect became limited when the content surpassed 10%. The decrease of the permeability coefficient of the modified loess is mainly due to the filling of pores between the loess by attapulgite, which makes the pore size and throat size of the modified loess smaller. The modified loess displayed a sheet structure that contributed to an increased permeability coefficient due to increased radial pressure. This study provides valuable insights into using attapulgite-modified loess as a material for landfill lining in regions with scarce clay resources.

Abstract Nowadays, Transit-Oriented Development (TOD) plays a vital role for public transport planners in developing potential city facilities. Knowing the necessity of this concept indicates that TOD effective parameters such as network accessibility (node value) and station-area land use (place value) should be considered in city development projects. To manage the coordination between these two factors, we need to consider ridership and peak and off-peak hours as essential enablers in our investigations. To aim this, we conducted our research on Chengdu rail-transit stations as a case study to propose our Node-Place-Ridership-Time (NPRT) model. We applied the Multiple Linear Regression (MLR) to examine the impacts of node value and place value on ridership. Finally, K-Means and Cube Methods were used to classify the stations based on the NPRT model results. This research indicates that our NPRT model could provide accurate results compared with the previous models to evaluate rail-transit stations.

Abstract In this paper, we investigated the effect of space irradiation on the lubricating properties of IL/(GO-MWCNT) solid–liquid lubricating coatings. The solid–liquid lubricating coatings consist of ionic liquids (IL), graphene oxide (GO), and multi-walled carbon nanotubes (MWCNT). Irradiation experiments were carried out using ground simulation equipment. Atomic oxygen (AO), ultraviolet (UV), proton (Pr), and electron (El) irradiation alter the composition, structure, morphology, and tribological properties of solid–liquid lubricating coatings. The experimental results show the composition changes induced by irradiation, including the decomposition of ILs lubricants. The damage to the lubricating material was the most serious by Pr irradiation and the least by UV irradiation.