Solute Transport Research Articles

Bi-level path tracking control of tractor semi-trailers by coordinated active front steering and differential braking

In recent years, autonomous tractor semi-trailers have been considered a promising solution for transportation on highways, ports, and large logistics centers to improve safety and efficiency. However, due to the complicated dynamics introduced by articulation structure, there are still challenges in achieving high-precision path tracking and stability control for tractor semi-trailers, especially in critical scenarios. In this work, a bi-level path tracking control scheme for the autonomous tractor semi-trailer is proposed. At the higher level, a robust model predictive controller coordinating active front steering and differential braking is devised for addressing path tracking, stability control and robustness for an autonomous tractor semi-trailer. At the lower level, incorporating a logic switching mechanism, the designed optimal braking torque distribution module and the PID-based longitudinal control module prioritize the implementation of differential braking and target speed tracking. Simulation results demonstrate that the proposed control strategy offers remarkable path tracking performance in three designed testing scenarios.

Analyzing the Impacts of Land Use and Network Features on Passenger Flow Distribution at Urban Rail Stations from a Classification Perspective

This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.

Insights into Passenger Transport Choices: A Mumbai Case Study

Understanding and studying passenger preferences in passenger transportation systems is important to optimise transportation infrastructure and increase passenger satisfaction. This paper presents a comprehensive case study conducted in Mumbai, India, which aims to understand the diverse preferences of commuters using different modes of transport within the city. Adopting a mixed-methods approach, data were accumulated by means of surveys, interviews, and observational studies conducted across different demographic sectors and geographical locations of Mumbai. The study analysed factors influencing commuters' mode choice, including cost, comfort, convenience, travel time, safety, and environmental concerns. The findings revealed significant differences in the preferences of commuters, impacted by factors like socioeconomic status, occupation, travel distance, and access to infrastructure. While public transportation emerged as the dominant mode for daily commuting, preferences were nuanced, some demographics prefer private modes like cars and motorcycles. Additionally, the study identifies areas of improvement in the existing transport infrastructure and suggests measures to enhance the reliability, frequency, safety, and connectivity of public transport services. The knowledge gleaned from this research provides valuable guidance for policymakers, urban planners, and transportation authorities in Mumbai, offering opportunities to tailor transportation solutions that align with commuters' preferences, thereby promoting sustainable and efficient passenger transport systems in the city.

Geo-guided deep learning for spatial downscaling of solute transport in heterogeneous porous media

Resolving solute transport in heterogeneous porous media is a complex task, because of the sparse experimental data and the high computational cost of numerical simulations. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a geo-guided super-resolution generative adversarial network (Gg-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of concentration and pressure profiles from a sparse, discontinuous profile with up to 85% missing data points. The Gg-SRGAN is then employed to generate a finer representation of field variables from the outputs generated by the dual-branch autoencoder (i.e., downscaling). We train and test our framework using six solute transport cases with varying levels of heterogeneity and compare the results with standalone methods, namely the vanilla autoencoder and vanilla SRGAN, in addition to ground truth profiles generated by the finite element method (FEM). The comparisons are performed based on several statistical metrics, such as absolute point error (APE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). The first four cases are used for training, evaluation, and testing. The last two cases are utilized for blind testing to determine the generalizability of the framework. Our results show that the dual-branch autoencoder outperforms the vanilla autoencoder, and the Gg-SRGAN outperforms the SRGAN during both the training and evaluation phases. Moreover, the proposed framework can successfully construct the fine representation of concentration profiles, compared to FEM, using the coarse representation of the pressure, concentration, and domain permeability fields. When tested using the two blind test cases, the proposed dual-branch autoencoder and Gg-SRGAN exhibit superior performance compared to their counterparts in terms of all evaluation metrics.

State of the Art on Fe Precipitation in Porous Media: Hydrogeochemical Processes and Evolving Parameters

The mixing of terrestrial groundwater and seawater creates dynamic reaction zones in intertidal areas, where land-derived Fe(II) is oxidized to Fe(III) and then precipitates as Fe hydroxides at the groundwater–seawater interface. These hydrogeochemical processes contribute to the formation of iron bands at the saltwater wedge (SW) and beneath the upper saline plume (USP). This study provides a comprehensive review of physical and geochemical processes at field scale in coastal areas, explores the impact of mineral precipitation on pore structure at pore scale, and synthesizes reactive transport modeling (RTM) approaches for illustrating continuum-scale soil physio-chemical parameters during the evolution of porous media. Upon this review, knowledge gaps and research needs are identified. Additionally, challenges and opportunities are presented. Therefore, we reach the conclusion that the incorporation of observational data into a comprehensive physico-mathematical model becomes imperative for capturing the pore-scale processes in porous media and their influence on groundwater flow and solute transport at large scales. Additionally, a synergistic approach, integrating pore-scale modeling and non-invasive imaging, is equally essential for providing detailed insights into intricate fluid–pore–solid interactions for future studies, as well as facilitating the development of regional engineering-scale models and physio-chemical coupled models with diverse applications in marine science and engineering.

Shannon entropy of transport self-organization due to dissolution–precipitation reaction at varying Peclet numbers in initially homogeneous porous media

Abstract. Dissolution and precipitation processes in reactive transport in porous media are ubiquitous in a multitude of contexts within the field of Earth sciences. In particular, the dynamic interaction between the reactive dissolution and precipitation processes and the solute transport is of interest as it is capable of giving rise to the emergence of preferential flow paths in the porous host matrix. It has been shown that the emergence of preferential flow paths can be considered to be a manifestation of transport self-organization in porous media as these create spatial gradients that distance the system from the state of perfect mixing and allow for a faster and more efficient fluid transport through the host matrix. To investigate the dynamic feedback between the transport and the reactive processes in the field and its influence on the emergence of transport self-organization, we consider a two-dimensional Darcy-scale formulation of a reactive-transport setup, where the precipitation and dissolution of the host matrix are driven by the injection of an acid compound, establishing local equilibrium with the resident fluid and an initially homogeneous porous matrix, composed of a calcite mineral. The coupled reactive process is simulated in a series of computational analyses employing the Lagrangian particle-tracking (LPT) approach, capable of capturing the subtleties of the multiple-scale heterogeneity phenomena. We employ the Shannon entropy to quantify the emergence of self-organization in the field, which we define as a relative reduction in entropy compared to its maximum value. Scalability of the parameters, which characterize the evolution of the reactive process, with the Peclet number in an initially homogeneous field is derived using a simple one-dimensional ADRE model with a linear adsorption reaction term and is then confirmed through numerical simulations, with the global reaction rate, the mean value, and the variance of the hydraulic-conductivity distribution in the field all exhibiting dependency on the reciprocal of the Peclet number. Our findings show that transport self-organization in an initially homogeneous field increases with time, along with the emergence of the field heterogeneity due to the interaction between the transport and reactive processes. By studying the influence of the Peclet number on the reactive process, we arrive at a conclusion that self-organization is more pronounced in diffusion-dominated flows, characterized by small Peclet values. The self-organization of the breakthrough curve exhibits the opposite tendencies, which are observed from the perspective of a thermodynamic analogy. The hydraulic power, required to maintain the driving head pressure difference between the inlet and outlet of the field, was shown to increase with the increasing variance, as well as with the increasing mean value of the hydraulic-conductivity distribution in the field, using a simple analytic model. This was confirmed by numerical experiments. This increase in power, supplied to the flow in the field, results in an increase in the level of transport self-organization. Employing a thermodynamic framework to investigate the dynamic reaction–transport interaction in porous media may prove to be beneficial whenever the need exists to establish relations between the intensification of the preferential flow path phenomenon, represented by a decline in the Shannon entropy of the transport, with the amount of reaction that occurred in the porous medium and the change in its heterogeneity.

Hydrological and biogeochemical processes controlling riparian groundwater quantity and quality during riverbank filtration

Groundwater exploitation in a riparian zone causes water infiltration from the river into the aquifer. Owing to adsorption and redox reactions along the flow path, the quality of water flowing from the river to groundwater wells is variably altered. The riverbed composition often involves spatiotemporal differences due to frequent changes in hydrological conditions. These changes create uncertainties in the transport and removal of solutes in the river water. In this study, the hydrodynamic field associated with riparian groundwater, changes in the structure of riverbed sediments caused by erosion and deposition, fluctuations in surface water and groundwater levels, and the removal efficiency of pollutants from groundwater through pumping were investigated. This involved in situ monitoring and sample testing of the composition of the river water, riverbed sediments, riverbed pore water, and groundwater during dry and wet seasons. Implementation of field in situ column experiments and molecular biology evidences were conducive to identifying the main biogeochemical processes occurring in the riverbed. The findings indicated that riparian groundwater exploitation alters the natural groundwater flow field, while fine sand deposition and microbial adsorption can reduce river recharge to aquifers by diminishing riverbed hydraulic conductivity. Shallow sediments within 1 m depth mainly involve NO3− reduction and E. coli adsorption. Reductive dissolution of Mn dominates in the deeper sediments. Additionally, reductive dissolution of Fe and dissimilatory nitrate reduction to ammonium (DNRA) drive high Fe2+ and NH4+ concentrations in groundwater. The findings can improve the management of riparian groundwater and aid in the optimization of a plan for its exploitation.

Retardation factor scaling for contaminant transport in fractured media

This paper delves into the key factors governing contaminant retardation and the scaling of retardation factor for contaminant transport in naturally fractured media. It employs a geostatistical approach, utilizing hierarchical transition probability and covariance models, to characterize multimodal reactive mineral facies. Subsequently, scale-dependent models are developed to illustrate the transport behaviors of reactive contaminants under equilibrium sorption conditions and quantify the spatial and temporal variations of the effective retardation factor. These models incorporate various factors, including the structural characteristics of reactive minerals (the volume proportions and lengths of different facies types), fracture characteristics (the means and variances of fracture apertures), and the adsorption capacity of contaminants on minerals (sorption coefficients (ln Kd)). The results from these Lagrangian-based models are compared and validated against experimental data of solute transport in a single natural fracture. These findings underscore the remarkable agreement between the derived scale-dependent analytical expressions and the experimental results. Additionally, a global sensitivity analysis was performed using the Monte Carlo-based Sobol’ indices method to understand the influential factors governing contaminant retardation. The study reveals that the primary influential factor is the sorption capacity of contaminants on minerals, while fracture and reactive mineral structural characteristics play a secondary role. These insights provide significant information for understanding the reactive transport processes and the environmental impact of transport parameters in fractured media.

Preferential flow of phosphorus and nitrogen under steady‐state saturated conditions

AbstractRepeated broiler litter application on agricultural lands can cause nutrient enrichment of subsurface effluent, especially with the existence of preferential flow through soil macropores. Previous studies quantifying soil macropores have not attempted to establish a connection of soil macropore characteristics with the subsurface nutrient (nitrogen [N] and phosphorus [P]) losses, across different topographical locations in the field. This study investigated the effect of broiler litter application and preferential flow on subsurface nutrient transport (N and P) at different topographical positions (upslope, midslope, and downslope) in a no‐till pasture field located in Alabama, USA. Twelve intact soil columns (150 mm id and 500 mm length) were used, and the nutrient leaching measurements from laboratory experiments were linked to soil macropore characteristics quantified using X‐ray computed tomography image analysis and solute transport modeling. Treatments included surface broadcast broiler litter (5 Mg ha−1, on dry basis) and unamended control. Leachates were analyzed for dissolved reactive P (DRP), total P (TP), and nitrate + nitrite‐N (NO3− + NO2−–N). The bromide breakthrough curves provided evidence of preferential flow in all columns. Litter application significantly increased leachate P concentrations, and average TP and DRP concentrations were significantly higher in the leachate from upslope columns compared to those at downslope location. The NO3−–N concentrations in leachate exceeded the US EPA drinking water standard of 10 mg L−1 in all the treatment columns. The highest flow‐weighted mean concentrations of TP and DRP, at 2.7 and 2.5 mg L−1, respectively, were recorded in the upslope columns. Soil physicochemical properties and nutrient leaching losses varied substantially across topographical positions, indicating a need for variable litter application rates to reduce P build‐up and subsequent leaching in vulnerable locations within the field. The relevance of the effect of topographic position on nutrient leaching found in this study should be further tested by investigating a wider range of slopes and soil types in pastures.

Quantifying the impact of upscaled parameters on radionuclide transport in three-dimensional fracture-matrix systems

Assessing the long-term safety of geological repositories for high-level radioactive waste is critically dependent on understanding radionuclide transport in multi-scale fractured rocks. This study explores the influence of upscaled parameters on radionuclide movement within a three-dimensional fracture-matrix system using a discrete fracture-matrix (DFM) model. The developed numerical simulation workflow includes creating a random discrete fracture network, meshing of the fractures and matrix, assigning upscaled parameters, and conducting finite element simulations. We simulated the spatiotemporal evolution of radionuclide concentrations in the fractures and matrix over a century, revealing significant spatial heterogeneity driven by a heterogeneous seepage field. Employing geostatistics-based upscaling methods, we predicted the effective ranges of crucial solute transport parameters at the field scale. The matrix diffusion coefficient, matrix distribution coefficient, and longitudinal dispersivity were upscaled by factors of 2.0–3.0, 2.5–4.0, and 10–104, respectively, based on laboratory-scale measurements. Incorporating these upscaled parameters into the DFM model, we analyzed their impact on radionuclide transport. Our findings demonstrate that an upscaled matrix diffusion coefficient and matrix distribution coefficient result in a delayed transport of radionuclides in fractures by enhancing mass transfer between the fractures and rock matrix, while an upscaled longitudinal dispersivity accelerates transport by advancing the positions of concentration peaks. Sensitivity analysis revealed that the matrix distribution coefficient is the most impactful, followed by dispersivity and matrix diffusion coefficient. These insights are important for minimizing parameter uncertainties and enhancing the accuracy of predictions concerning radionuclide transport in multi-scale fractured rocks.

ABCG2 and SLC1A5 functionally interact to rewire metabolism and confer a survival advantage to cancer cells under oxidative stress

ABCG2, a member of the ABC transporter superfamily, is overexpressed in many human tumors and has long been studied for its ability to export a variety of chemotherapeutic agents, thereby conferring a multidrug resistance (MDR) phenotype. However, several studies have shown that ABCG2 can also confer an MDR-independent survival advantage to tumor cells exposed to stress. While investigating the mechanism by which ABCG2 enhances survival in stressful milieus, we have identified a physical and functional interaction between ABCG2 and SLC1A5, a member of the solute transporter superfamily and the primary transporter of glutamine in cancer cells. This interaction was accompanied by increased glutamine uptake, increased glutaminolysis and rewired cellular metabolism, as evidenced by an increase in key metabolic enzymes and alteration of glutamine-dependent metabolic pathways. Specifically, we observed an increase in glutamine metabolites shuttled to the TCA cycle, an increase in the synthesis of glutathione, accompanied by a decrease in basal levels of reactive oxygen species and a marked increase in cell survival in the face of oxidative stress. Notably, knockdown of SLC1A5 or depletion of exogenous glutamine diminished ABCG2-enhanced autophagy flux, further implicating this solute transporter in ABCG2-mediated cell survival. This is, to our knowledge, the first report of a functionally significant physical interaction between members of the two major transporter superfamilies. Moreover, these observations may underlie the protective role of ABCG2 in cancer cells under duress and suggest a novel role for ABCG2 in the regulation of metabolism in normal and diseased states.

Irreversibility estimation in triply stratified bio-marangoni convection in powell-eyring nanofluid under the influence of external flow

Due to the growing importance of bioconvection phenomena in diverse industrial processes such as oil refining, biotechnology, and food processing, it is essential to examine several effects like a chemical reaction, stratification, Marangoni convection, etc in nanofluid suspensions in the context of transport of microorganisms. The outcome of such studies may provide significant insight into controlling and as well as manipulating the transport of energy, solute, and microorganisms for achieving the requisite target. The aim of our study is to investigate the thermo-solutal Marangoni convection of gyrotactic microorganisms suspended in Powell-Eyring nanofluid in a stratified medium. This analysis also takes into account the effects of external flow and transverse magnetic field. The impact of Arrhenius activation energy, thermal radiation, and binary chemical reactions on bioconvection flow is considered under stratified Marangoni convection. Under appropriate assumptions without violating the physics, the present problem is expressed in terms of nonlinear PDEs. Some capable similarity transformations are employed to convert the PDEs into ODEs and solved numerically by the Runge-Kutta Fehlberg method. The graphical illustrations depict the effects of relevant flow parameters on temperature, nanoparticle volume fraction, and density distributions of motile microorganisms. The study focuses on understanding the variations in significant engineering quantities resulting from changes in crucial effects and analyzing irreversibilities. It is noticed that Marangoni accelerates the mass transfer process while on the other hand delaying the heat transfer and microorganisms density gradient. The dominance of Powell-Eyring nanofluid on the heat and mass transport amplified in accompanying Marangoni convection. The results indicate that the Eyring-Powell fluid significantly reduces entropy generation and the Bejan number. Conversely, an increase in entropy generation is observed for the Marangoni ratio parameter. Hence, this work provides insight into interlinked flow in bioconvective systems, with potential diverse applications in microbiological processes.

Effects of leucine on hydrate formation: A combined experimental and molecular dynamics study

Compared to conventional natural gas storage and transportation methods, hydrates offer a safer, more compact alternative with broad future potential. To enhance hydrate formation, there has been considerable interest in environmentally friendly and cost-effective amino acid promoters, such as leucine. This study utilized a transparent vessel for CH4 hydrate formation experiments, identifying the optimal 0.5–0.6 wt% leucine concentration, which led to enhanced gas consumption, shorter induction time, and maximum gas storage density. In the leucine system, hydrates exhibited distinctive branching growth on container walls, both upward and downward. The formation of numerous microchannels amplified capillary effects, enhancing gas–liquid contact and fostering hydrate formation. Simultaneously, isobaric-isothermal molecular dynamics (MD) simulations integrated system growth configurations, structural parameters, radial distribution functions, energy barriers and mean square displacements, unveiling leucine’s microscopic impact on hydrate formation. Simulation results unequivocally show that leucine significantly accelerates CH4 hydrate growth by momentarily adsorbing at the solid–liquid interface, expediting water molecule ordering into cage-like structures. Leucine also enhances water molecule structural order around the mobile leucine molecule. Moreover, leucine lowers the energy barrier for methane transitioning from gas to liquid, facilitating gas molecule diffusion into the liquid phase and affording plenty interaction time for water and methane molecules, thereby providing favorable conditions for hydrate formation. These findings provide crucial perspectives for future research in the field of eco-friendly hydrate promoters, paving the way for innovative solutions in natural gas storage and transportation.

Expression of bile acid receptors and transporters along the intestine of patients with type 2 diabetes and controls.

The enterohepatic circulation of bile acids depends on intestinal absorption by bile acid transporters and activation of bile acid receptors, which stimulates secretion of hormones regulating glucose and lipid metabolism and appetite. Distribution of bile acid transporters and receptors in the human gut and their potential involvement in type 2 diabetes (T2D) pathophysiology remain unknown. We explored the expression of genes involved in bile acid metabolism throughout the intestines of patients with T2D and matched healthy controls. Intestinal mucosa biopsies sampled along the intestinal tract in 12 individuals with T2D and 12 healthy controls were subjected to mRNA sequencing. We report expression profiles of apical sodium-dependent bile acid transporter (ASBT), organic solute transporter (OST) α/β, farnesoid X receptor (FXR), Takeda G receptor 5 (TGR5), fibroblast growth factor 19 (FGF19) and FGF receptor 4 (FGFR4). Expression of ASBT and OSTα/β was evident in the duodenum of both groups with increasing levels through the small intestine, and no (ASBT) or decreasing levels (OSTα/β) through the large intestine. The FXR expression pattern followed that of OSTα/β whereas FGFR4 were evenly expressed through the intestines. Negligible levels of TGR5 and FGF19 were evident. Patients with T2D exhibited lower levels of FGF19, FXR, ASBT and OSTα/β mRNAs compared with healthy controls, although the differences were not statistically significant after adjusting for multiple testing. We demonstrate distinct expression patterns of bile acid transporters and receptors through the intestinal tract with signs of reduced ASBT, OSTα/β, FXR and FGF19 mRNAs in T2D.

Considering the importance of transport to the wellbeing of Māori social housing residents

BackgroundIndigenous Māori conceptualisations of home extend beyond the house and encompass the ways in which people move in and around their environments. This movement is enabled by appropriate and accessible transport systems. For Māori living in social housing, who may already be disconnected from their whānau (family group, extended family), friends and places of belonging, access to the wider urban environment is essential for supporting health and wellbeing. Despite literature depicting Māori inequities in both transport access and in transport-related health outcomes such as access to a general practitioner, there is little research that explores transport as an enabler of wellbeing for Māori living in social housing. This is the gap that this research fills. MethodsThis qualitative research took a Kaupapa Māori approach and privileged Indigenous Māori worldviews and concepts throughout. Interviews were conducted with twelve Māori residents living in social housing complexes in Christchurch New Zealand. Interviews explored how Māori experienced their housing complex and the travel they did between their complex and the wider urban environment. Interviews were transcribed verbatim, coded and analysed by the lead Māori researcher. ResultsResults describe how residents use different forms of transport to access the environment around their social housing complexes. Despite utilising a wide range of transport modes, participants discussed the barriers they face when using both private and public forms of transport. The research also looks at how Māori living in social housing perceive novel transport technologies and poses questions surrounding the suitability of these schemes for reducing transport inequities. ConclusionsThese localised experiences provide insight into the role transport plays in creating a home and in establishing and maintaining social connections for Māori living in social housing. In doing so, the research explains the link between transport access and Indigenous wellbeing while also exploring some of the concerns those living in social housing have surrounding their inclusion in transport solutions. Findings reinforce the notion that successful transport systems for Indigenous populations come from a place of co-design where Indigenous peoples are equal partners in decision-making.

Environmental risk assessment of underground concentrated brine reservoir with solute transport model: A case study of a coal mine in Northwest China

Underground reservoirs have emerged as a promising technology for storing and reusing saline wastewater from coal mines. However, the high salinity and potential leakage risks pose environmental threats needing assessment. This study developed an integrated solute transport and relative risk modeling framework to evaluate the environmental risks of an underground concentrated brine reservoir (UCBR) in Northwest China. The solute transport model simulated subsurface brine migration under long-term penetration, sudden leakage, and complete leakage scenarios, parameterized by field data. The relative risk model integrated source, receptor, and exposure analyses to quantify normalized risk levels. Results revealed that risks incrementally rose over longer storage durations, with 70 years posing high risk under 10 m hydraulic gradients in the long-term scenario. The risk of sudden leakage is controllable within 30 days but dramatically elevated by 60 days. Complete leakage was predicted to affect a stable 3 km2 zone. The integrated modeling provides an improved methodology for assessing UCBR risks and highlights the critical influence of bottom barrier permeability, groundwater gradients, and storage time. Targeted strategies are proposed, including reinforcing bottom barriers, managing hydraulic gradients, controlling storage durations, implementing early warning systems, and contingency planning to promote sustainable mining water management.

Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation

This study introduces a groundbreaking hybrid deep learning Intrusion Detection System (IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the context of computer-aided mobility solutions for smart urban transportation. The proposed system ingeniously integrates XGBoost and Convolutional Neural Network (CNN) technologies, leveraging their combined strengths to fortify the resilience of EV charging infrastructure. In response to the demand for transparency in decision-making, our approach incorporates Explainable AI Techniques, specifically employing Shapley Additive exPlanations (SHAP) values. This not only enables the IDS to identify anomalous behavior but also provides clear insights into the features influencing the detection process. The transparency achieved is pivotal for fostering stakeholder trust, ensuring regulatory compliance, and facilitating well-informed decision-making within the dynamic landscape of smart urban transportation. A sophisticated mechanism is implemented to enable the model to autonomously adjust and learn from evolving charging patterns over time. This adaptive capability ensures the IDS remains effective in the face of changing charging behaviors, contributing to the resilience of the overall smart urban transportation system. This research not only propels the field of cybersecurity in smart urban transportation but also establishes a robust framework for the development of intelligent systems. These systems play a critical role in securing essential processes while providing interpretable insights for stakeholders in the realm of computer-aided mobility solutions.

BMSC-derived Exosomes Ameliorate Peritoneal Dialysis-associated Peritoneal Fibrosis via the Mir-27a-3p/TP53 Pathway.

Peritoneal fibrosis (PF) is the main cause of declining efficiency and ultrafiltration failure of the peritoneum, which restricts the long-term application of peritoneal dialysis (PD). This study aimed to investigate the therapeutic effects and mechanisms of bone marrow mesenchymal stem cells-derived exosomes (BMSC-Exos) on PF in response to PD. Small RNA sequencing analysis of BMSC-Exos was performed by second-generation sequencing. C57BL/6J mice were infused with 4.25% glucose-based peritoneal dialysis fluid (PDF) for 6 consecutive weeks to establish a PF model. A total of 36 mice were randomly divided into 6 groups: control group, 1.5% PDF group, 2.5% PDF group, 4.25% PDF group, BMSC-Exos treatment group, and BMSC-Exos+TP53 treatment group. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed to measure the expression level of miR-27a-3p in BMSC-Exos and peritoneum of mice treated with different concentrations of PDF. HE and Masson staining were performed to evaluate the extent of PF. The therapeutic potential of BMSC-Exos for PF was examined through pathological examination, RT-qPCR, Western blotting, and peritoneal function analyses. Epithelial-mesenchymal transition (EMT) of HMrSV5 was induced with 4.25% PDF. Cells were divided into control group, 4.25% PDF group, BMSC-Exos treatment group, and BMSC-Exos+TP53 treatment group. Cell Counting Kit-8 assay was used to measure cell viability, and transwell migration assay was used to verify the capacity of BMSC-Exos to inhibit EMT in HMrSV5 cells. Small RNA sequencing analysis showed that miR-27a-3p was highly expressed in BMSC-derived exosomes compared to BMSCs. The RT-qPCR results showed that the expression of miR-27a-3p was upregulated in BMSC-Exos, but decreased in PD mice. We found that PF was glucose concentration-dependently enhanced in the peritoneum of the PD mice. Compared with the control mice, the PD mice showed high solute transport and decreased ultrafiltration volume as well as an obvious fibroproliferative response, with markedly increased peritoneal thickness and higher expression of α-SMA, collagen-I, fibronectin, and ECM1. The mice with PD showed decreased miR-27a-3p. Peritoneal structural and functional damage was significantly attenuated after BMSC-Exos treatment, while PF and mesothelial damage were significantly ameliorated. Additionally, markers of fibrosis (α-SMA, collagen-I, fibronectin, ECM1) and profibrotic cytokines (TGF-β1, PDGF) were downregulated at the mRNA and protein levels after BMSC-Exos treatment. In HMrSV5 cells, BMSC-Exos reversed the decrease in cell viability and the increase in cell migratory capacity caused by high-glucose PDF. Western blotting and RT-qPCR analysis revealed that BMSC-Exos treatment resulted in increased expression of E-cadherin (epithelial marker) and decreased expression of α-SMA, Snail, and vimentin (mesenchymal markers) compared to those of the 4.25% PDF-treated cells. Importantly, a dual-luciferase reporter assay showed that TP53 was a target gene of miR-27a-3p. TP53 overexpression significantly reversed the decreases in PF and EMT progression induced by BMSC-Exos. The present results demonstrate that BMSC-Exos showed an obvious protective effect on PD-related PF and suggest that BMSC-derived exosomal miR-27a-3p may exert its inhibitory effect on PF and EMT progression by targeting TP53.

Unsteady Flow of Bingham Model: Investigating the Impact of Arterial Inclination on Solute Transport in Stenosed Artery

Unsteady Flow of Bingham Model: Investigating the Impact of Arterial Inclination on Solute Transport in Stenosed Artery

DESIGN OF IOT BASED TRICYCLE

The rapid advancement in Internet of Things (IoT) technology has opened up numerous opportunities for innovation across various industries. In the transportation sector, IoT has the potential to revolutionize conventional vehicles into smart, connected modes of transportation. This project focuses on the design of an IoT-based electric tricycle, aimed at providing a sustainable and efficient mode of personal transportation.The electric tricycle integrates IoT technology to enhance its functionality and user experience. Key features include real-time monitoring of vehicle parameters such as speed, battery status, and location, enabling remote tracking and management through a dedicated mobile application. Additionally, the tricycle incorporates smart safety features such as collision detection and automatic emergency braking, enhancing rider safety on the road.The design phase involves the selection of appropriate components and the integration of IoT modules, sensors, and actuators into the tricycle framework. The fabrication process encompasses building the chassis, integrating the electric propulsion system, and assembling the IoT components. Emphasis is placed on ensuring durability, reliability, and energy efficiency throughout the construction process.Overall, the project aims to contribute to the advancement of sustainable transportation solutions by leveraging IoT technology to create an innovative, eco-friendly electric tricycle. Through this endeavor, we envision promoting the adoption of electric vehicles and facilitating a more connected and efficient urban mobility ecosystemTop of Form Keywords: IoT-based electric tricycle, Sustainable transportation, Connected vehicles, Electric vehicle innovation, Real-time monitoring, Safety features, Fabrication process, Integration of IoT technology, User experience, Smart mobility solution