Set Of Model Parameters Research Articles

Study on the Suitability of Concrete Constitutive Models for Perforation Simulation.

The choice of constitutive model significantly affects the accuracy of concrete perforation simulation. This study analyzes four concrete constitutive models, HJC, RHT, KCC, and TCK, focusing on their strength models, damage evolution, and strain rate effects. Combining the damage pattern and erosion cracks, the effectiveness of the four constitutive models in simulating the penetration of reinforced concrete targets is evaluated using LS-DYNA 11.0. The results indicate that the RHT and TCK models accurately depict the concrete damage and failure modes under the same test conditions. In contrast, the KCC and HJC models demonstrate superior capability in predicting the residual velocity of the projectile. Additionally, this study highlights the significant impact of the erosion parameters on the simulation results. This study offers a valuable reference for the application and parameter set of constitutive models in simulating concrete target perforation.

Sensitivity analysis of a dense discrete phase model for 3D simulations of a tapered fluidized bed

This study aims to conduct a sensitivity analysis of closure models and modeling parameters for the Dense Discrete Phase Modeling (DDPM) approach in order to investigate the hydrodynamics of a 3D lab-scale Tapered Fluidized Bed (TFB). The closure models and model parameters under investigation include the gas-solid drag force, viscous models, particle-particle interaction models, restitution coefficient, specularity coefficient, and rebound coefficient. The primary objective of this sensitivity analysis is to optimize the numerical model's performance. The numerical results, in terms of axial and lateral Solid Volume Fraction (SVF) profiles obtained from the sensitivity analysis, indicate that the drag force and restitution coefficient significantly influence the hydrodynamics of the TFB. Properly selecting these parameters could result in the improved performance of the numerical model. However, the sensitivity of turbulence models, particle-particle interaction models, specularity coefficient, and rebound coefficient has a lesser impact on the hydrodynamics results. This work concludes with the recommendation of a set of closure models and modeling parameters that offer the most accurate prediction of the hydrodynamics of the TFB.

Temperature and strain rate effects on ultra‐high‐molecular‐weight‐polyethylene compression: An experimental and modeling approach

AbstractThis study aims to predict the compression behavior of ultra‐high molecular weight polyethylene (UHMWPE) at various temperatures (25, 40, and 55°C) and strain rates (~10−4/s and ~10−2/s) using a single set of three‐network (TN) viscoplastic model parameters. The TN model is made up of three parallel networks: networks A and B use nonlinear springs and dashpots to control the responses of the crystalline phase and confined amorphous phases, respectively, while network C captures the macromolecular amorphous networks using a single nonlinear spring. A single set of TN model parameters captures the yield and post‐yield hardening responses of the stress–strain curves at experimental temperatures and strain rates. When these TN model calibrated parameters are used as material property in a commercial finite element tool to simulate UHMWPE compression, the predicted and simulated results match well, showing the model's fidelity. Additionally, the model predicts experiments conducted at 40 and 70°C with loading rates of ~10−3/s and ~10−2/s, respectively. The study also correlates the deformations of UHMWPE's crystalline structure and macromolecular amorphous networks with its global stress versus strain response by extracting stresses from individual networks of the TN model at different strain rates and temperatures.Highlights UHMWPE's mechanical behavior predicted by a single set of TN model parameters TN model predicts mechanical response at various strain rates and temperatures. TN model explains microstructural crystalline and amorphous phase deformations.

Limited data-oriented building heating load prediction method: A novel meta learning-based framework

Data-driven models have been widely used in building heating load prediction, but often fail when facing limited data. Previous studies have shown transfer learning can assist model learning of target building under limited data by means of other source building data, however, which is subject to the similarity between source and target building. Selecting similar source building data is not easy, especially when the target building is with limited data. This paper, therefore, proposes a novel meta learning-based framework for building heating load prediction. Using meta learning method, a set of promising model parameters is trained by local and global learning on multiple source buildings data. The obtained model parameters has the ability to get quickly trained with few data in each source building, which is further used as model initialization parameters of target building to assist model learning. Framework validity is confirmed by 550 groups of practical buildings data (50 are as target buildings for testing and 500 are as source buildings). The results showed the proposed framework could reduce the prediction errors by 2.04 %∼61.59 % compared with six common transfer learning methods. The novel meta learning-based framework provides an effective solution for building heating load prediction with limited data.

Application of CALPHAD Method for Predicting of Concentration Range of Amorphization of Transition Metals Melts

Early, the efficiency of the CALPHAD (Calculation of Phase Diagrams) method to a targeted search for compositions of amorphous alloys has been shown. The method for predicting the ranges of amorphization is based on the calculation of diagrams of metastable phase transformations between supercooled melts and boundary solid solutions on the base of pure elements. In this work, the model parameters for thermodynamic properties of liquid alloys and boundary solid solutions were summarized in a self-consistent database for the multicomponent Cu–Fe–Ni–Ti–Zr–Hf system. Such database for the multicomponent system is based on a common set of model parameters for boundary binary and ternary systems. This database was used to predict the concentration ranges of amorphization for the quinary Cu–Fe–Ni–Ti–Zr, Cu–Fe–Ni–Ti–Hf and boundary ternary and quaternary systems. The results of calculations are presented along sections in quaternary and quinary systems. The ternary and quaternary equiatomic alloys along with high entropy CuFeNiTiZr and CuFeNiTiHf alloys are trapped into prognosed composition ranges of amorphization. Predicted composition space of amorphization for melts of the Fe–Ni–Ti–Zr system is shown on the concentration tetrahedron. Based on the obtained results, a new criterion for predicting the concentration regions of amorphization of multicomponent melts is proposed, according to which the presence of a sufficient content of metals that are electron acceptors and donors is a chemical factor that affects the thermodynamic stability of melts and determines their glass-forming ability. For multicomponent melts of the Cu–Fe–Ni–Ti–Zr–Hf system the concentration ranges of amorphization correspond to the simultaneous fulfillment of the conditions xFe + xNi + xCu > 0.25 and xTi + xZr + xHf > 0.15, where Fe, Ni, and Cu are electron acceptors and Ti, Zr, and Hf are electron donors.

Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics.

Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual's activity rate and the possibility of group interaction, we propose a probabilistic activity-driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. Surprisingly, the power-law exponents and the clustering coefficients of the aggregated PAD network could be tuned in a wide range by altering a set of model parameters. We further provide an approximation algorithm to select the proper parameters that can generate networks with given structural properties, the effectiveness of which is verified by fitting various real-world networks. Finally, we construct the co-evolution framework of the PAD model and higher-order contagion dynamics and derive the critical conditions for phase transition and bistable phenomenon using theoretical and numerical methods. Results show that tendency of participating in higher-order interactions can promote the emergence of bistability but delay the outbreak under heterogeneous activity rates. Our model provides a basic tool to reproduce complex structural properties and to study the widespread higher-order dynamics, which has great potential for applications across fields.

Deep Neural Network Confidence Calibration from Stochastic Weight Averaging

Overconfidence in deep neural networks (DNN) reduces the model’s generalization performance and increases its risk. The deep ensemble method improves model robustness and generalization of the model by combining prediction results from multiple DNNs. However, training multiple DNNs for model averaging is a time-consuming and resource-intensive process. Moreover, combining multiple base learners (also called inducers) is hard to master, and any wrong choice may result in lower prediction accuracy than from a single inducer. We propose an approximation method for deep ensembles that can obtain ensembles of multiple DNNs without any additional costs. Specifically, multiple local optimal parameters generated during the training phase are sampled and saved by using an intelligent strategy. We use cycle learning rates starting at 75% of the training process and save the weights associated with the minimum learning rate in every iteration. Saved sets of the multiple model parameters are used as weights for a new model to perform forward propagation during the testing phase. Experiments on benchmarks of two different modalities, static images and dynamic videos, show that our method not only reduces the calibration error of the model but also improves the accuracy of the model.

Anand constitutive modeling of multilayer Silver-Tin transient liquid phase foils using tensile and creep testing

Purpose This paper aims to present two Anand’s model parameter sets for the multilayer silver–tin (AgSn) transient liquid phase (TLP) foils. Design/methodology/approach The AgSn TLP test samples are manufactured using pre-defined optimized TLP bonding process parameters. Consequently, tensile and creep tests are conducted at various loading temperatures to generate stress–strain and creep data to accurately determine the elastic properties and two sets of Anand model creep coefficients. The resultant tensile- and creep-based constitutive models are subsequently used in extensive finite element simulations to precisely survey the mechanical response of the AgSn TLP bonds in power electronics due to different thermal loads. Findings The response of both models is thoroughly addressed in terms of stress–strain relationships, inelastic strain energy densities and equivalent plastic strains. The simulation results revealed that the testing conditions and parameters can significantly influence the values of the fitted Anand coefficients and consequently affect the resultant FEA-computed mechanical response of the TLP bonds. Therefore, this paper suggests that extreme care has to be taken when planning experiments for the estimation of creep parameters of the AgSn TLP joints. Originality/value In literature, there is no constitutive modeling data on the AgSn TLP bonds.

Decentralized asynchronous adaptive federated learning algorithm for securely prediction of distributed power data

Introduction: Improving the precision and real-time speed of electricity data prediction while safeguarding data privacy and security holds immense significance for all power system participants’ decision-making. To surmount the issues of exorbitant computational expenses and privacy breaches of traditional centralized prediction methods, this paper proposes a decentralized asynchronous adaptive federated learning algorithm for securely prediction of distributed power data, which makes predictions from distributed data more flexible and secure.Methods: First, each regional node trains its own deep neural network model locally. After that, the node model parameters are uploaded to the decentralized federated learning chain for ensuring local data protection. Asynchronous aggregated update of the global prediction model is then achieved via block mining and shared maintenance. The algorithm has been enhanced based on the traditional federated learning algorithm, which introduces an asynchronous mechanism while adaptively adjusting the regional node model weights and local update step size to overcomes the inefficiency of traditional methods.Results and Discussion: The experimental analysis of actual electricity price data is conducted to compare and analyze with the centralized prediction model, study the impact of model adoption and parameter settings on the results, and compare with the prediction performance of other federated learning algorithms. The experimental results show that the method proposed in this paper is highly accurate, efficient, and safe.

Identifying key sectors of sustainable development: A Bayesian framework estimating policy‐impacts in a general equilibrium

AbstractTransformation of the previous centrally growth‐oriented economic systems to a sustainable bio‐economy is a global political trend, where public policy is a key factor in making this successful. Designing effective and efficient policies requires understanding the linkages between policy choices and outcomes. Most existing studies are missing a direct link to policy choices and ignore fundamental model uncertainty present in policy analysis. We empirically estimate a sector‐specific, nested two‐stage policy impact function to address these shortcomings. We apply a Bayesian estimation approach that combines existing statistical data with a priori information from political experts, thus reducing data and estimation problems. This is linked with a Computable General Equilibrium to model the entire link from policies to outcomes. We derive a theoretical framework that allows the definition of indicators for key sectors of an efficient Pro‐Poor‐Growth strategy. In our generalized framework, we show that indicators based only on growth‐poverty linkages might be misleading. To deal with model uncertainty inherent in the application, we derive a set of metamodels via simulations conducted under different model parameter settings and apply Markov Chain Monte Carlo sampling. Applying Bayesian model selection allows drawing statistical inferences on competing models to generate relatively robust policy‐relevant messages even under model uncertainty. The approach is empirically applied to Ghana, Senegal, and Uganda, analyzing the allocation of public spending on agriculture under the Comprehensive Africa Agriculture Development Programme. [EconLit Citations: C11—Bayesian Analysis: General; C63—Computational Techniques, Simulation Modeling; D58—Computable and Other Applied General Equilibrium Models; O55—Africa; Q01—Sustainable Development; Q18—Agricultural Policy].

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data.

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each "virtual individual", which we define as each fixed set of model parameters (1) and (2) above. The "virtual population" and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated "virtual population database" of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus-cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.

Elastoplastic damage behavior of quasi-brittle rocks considering crack closure evolution

Crack closure evolution in compaction stage of quasi-brittle rocks is a very common phenomenon. A distinct feature of quasi-brittle rocks is that, under compression, presence of microcracks can increase nonlinear mechanical response of rock under low confining pressure, while gradually decrease nonlinear behavior of rock under high confining pressure. Previous studies have mainly focused on modelling the damage evolution caused by plastic strain, with few attempts to describe nonlinear behavior of compaction stage and reveal elastic damage dissipation mechanism triggered by the crack closure evolution. This paper presents a coupled elastoplastic damage model, which incorporates crack closure to describe the distinct behavior of rock based on the framework of continuum thermodynamics. Comparison between the model predictions and test results of three rock specimens shows that the model can capture whole process mechanical behavior of quasi-brittle rocks over a wide range of confining pressure using a set of model parameters. Parametric studies are carried out to quantify the effects of crack closure evolution and dissipation mechanism of elastic damage and plastic flow of the rock. The proposed model based on an implicit return mapping algorithm has been used to simulate the plastic damage response of rock specimen under displacement control.

Application of QGA-BP Neural Network in Debt Risk Assessment of Government Platforms

How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.

RESEARCH OF INFLUENCE OF MOLAR COMPOSITION ON ELECTRON MOBILITY IN ALUMINUM-GALLIUM ARSENIDE

Improvement in the technology of growing multicomponent semiconductors and the study of their potential applications contribute to the development of semiconductor devices with improved characteristics. Aluminum gallium arsenide is a promising three-component semiconductor in terms of crystal lattice matching, which caused a comprehensive study of the material in order to create high-performance electronic and optoelectronic devices on its basis. Predicting the prospects for creating devices based on certain materials requires a thorough knowledge, first of all, of their electrical properties.It should be noted that the possibilities of modeling the mobility of electrons in aluminum-gallium arsenide are not sufficiently disclosed in the scientific and technical literature. The aim of this work is to simulate the dependence of the electron mobility of the AlxGa1-xAs solid solution on the molar composition x.The simulation was carried out by the method of relaxation equations; based on the three-valley model of the conduction band. A technique is presented and a numerical experiment is carried out to determine the dependence of the mobility of charge carriers on the molar composition x of the solid solution. Attention is drawn to the peculiarity of modeling the processes of intervalley nonequivalent scattering, which corresponds to a significant convergence ofenergy valleys to each other. A model dependence of the Hall electron mobility in AlxGa1-xAs is obtained in the full range of molar composition values and compared with experimental results. Additional simulation results are presented that contribute to the understanding of the processes leading to the appearance of a specific feature of the dependence of the electron mobility on the molar composition of AlxGa1-xAs.A set of initial modeling parameters is determined, which provides good agreement with the experimental results considered in the work. The obtained results of numerical simulation open up opportunities for additional studies of the properties of aluminum-gallium arsenide. A technique for modeling the dependence of charge carrier mobility on the molar composition for three-component solid solutions is proposed.

Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling

Abstract. Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability.

Modeling Sorption of Hydrocarbons in Polyethylene with the SAFT-γ Mie Approach Combined with a Statistical-Mechanical Model to Describe Semicrystalline Polymers.

A recently developed statistical-mechanical model is applied systematically to estimate the fraction of tie-molecules (polymer chains linking different crystals directly or via entanglements) in semicrystalline polyethylene (PE) samples. The amorphous domains of the polymer are divided into constrained interlamellar domains and "free" outer-lamellar domains. A set of model parameters is assigned to each sample by correlating previous experimental measurements and minimizing the difference between the predicted solubility of pure hydrocarbons in the sample and the experimental values. We show that the sorption isotherms of multiple pure fluids in each sample can be described by a single parameter set, proving that the polymer-solute interactions (described accurately by the SAFT-γ Mie EoS) are decoupled from the sample-specific properties of the polymer. We find that ∼30% of the crystalline stems in the lamellae of PE are connected to tie-molecules, within the bounds suggested by previous theoretical and computational work. The transferability of the sample-specific parameters is assessed by predicting cosolubility effects and solubility at different temperatures, leading to good agreement with experimental data.

Evaluating the performance of propagation models of flow-like landslides at regional scale

Propagation models of flow-like landslides can be calibrated by comparing on-site evidence of past occurrences with the propagation paths and the deposition zones resulting from numerical simulations of the phenomena. Most typically, the performance of these models is evaluated considering the events independently from one another and, heuristically, i.e., subjectively assessing the fit between numerical results and available on-site data. At regional scale, however, storms often trigger, within a given area, multiple landslides of the flow type that occur more or less simultaneously. At this scale, a procedure that objectively quantifies the success, or the errors, of the numerical simulations of multiple landslides is lacking. In this study, such a quantitative calibration procedure is proposed, and assessed, considering the debris flows that occurred in Sarno in 1998 (Italy). The numerical model used is called Debris Flow Predictor (DFP), which is able to simulate the propagation paths and the accumulation depths of multiple debris flows, at regional scale, from a series of predefined triggering areas. The model employs a cellular automata method with a probabilistic behavioral rule, which is a function of the adopted digital elevation model and a series of parameters related to the erosional, the depositional, and the spreading capacity of the propagating soil mass. The numerical simulations were evaluated over the study area considering the entire set of debris flow events, as well as the individual debris flows, following a preliminary discretization of both the mapped footprints and the remaining portion of the territory. The relative and total operator characteristic curves, in addition to 6 indicators derived from a confusion matrix, have been used to quantify the performance of the simulations. The results show that the quantitative evaluation of the numerical results is essential to properly calibrate the adopted model, i.e., to discriminate among different simulations arising from different sets of model parameters.

A Course Recommendation Algorithm for a Personalized Online Learning Platform for Students From the Perspective of Deep Learning

This paper mainly studies the content of the recommendation algorithm of learning resource courses in online learning platforms such as MOOC and mainly introduces the automatic encoder neural network that integrates course relevance to realize the personalized course recommendation model. The authors first introduce how to embed a course relevance decoder in an autoencoder neural network. Secondly, the proposed confidence matrix method is introduced to distinguish the recommendation effect of the learned to the unlearned courses, and the training process of the model is introduced. Then, the design content of the experiment is introduced, including the model structure, comparative experiments, parameter settings, and evaluation indicators. Finally, the experimental results are analyzed in detail from the horizontal and vertical aspects. It is hoped that this research can provide a reference for personalized recommendation of learning resources based on deep learning technology and big data analysis.

Research on the mechanism of mechanical ventilation induced endoplasmic reticulum stress promoting mechanical ventilation-induced pulmonary fibrosis

To demonstrate the mechanism of mechanical ventilation (MV) induced endoplasmic reticulum stress (ERS) promoting mechanical ventilation-induced pulmonary fibrosis (MVPF), and to clarify the role of angiotensin receptor 1 (AT1R) during the process. The C57BL/6 mice were randomly divided into four groups: Sham group, MV group, AT1R-shRNA group and MV+AT1R-shRNA group, with 6 mice in each group. The MV group and MV+AT1R-shRNA group mechanically ventilated for 2 hours after endotracheal intubation to establish MVPF animal model (parameter settings: respiratory rate 70 times/minutes, tidal volume 20 mL/kg, inhated oxygen concentration 0.21). The Sham group and AT1R-shRNA group only underwent intubation after anesthesia and maintained spontaneous breathing. AT1R-shRNA group and MV+AT1R-shRNA group were airway injected with the adeno-associated virus one month before modeling to inhibit AT1R gene expression in lung tissue. The expressions of AT1R, ERS signature proteins [immunoglobulin heavy chain-binding protein (BIP), protein disulfide isomerase (PDI)], fibrosis signature proteins [collagen I (COL1A1), α-smooth muscle actin (α-SMA)] in lung tissues were detected by immunofluorescence and Western blotting. Hematoxylin-eosin (HE) staining was used to evaluate lung injury and Masson staining was used to evaluate pulmonary fibrosis. Compared with the Sham group, the degree of pulmonary fibrosis and lung injury were more significant in the MV group. In the MV group, the protein expressions of AT1R, BIP, PDI, COL1A1 and α-SMA were increased (AT1R/β-actin: 1.40±0.02 vs. 1, BIP/β-actin: 2.79±0.07 vs. 1, PDI/β-actin: 2.07±0.02 vs. 1, COL1A1/α-Tubulin: 2.60±0.15 vs. 1, α-SMA/α-Tubulin: 2.80±0.25 vs. 1, all P < 0.01). The number of E-cad+/AT1R+ and E-cad+/BIP+ cells in lung tissue increased, and the fluorescence intensity of COL1A1 and α-SMA increased. Compared with the MV group, the degree of pulmonary fibrosis and lung injury were significantly relieved in the MV+AT1R-shRNA group. In the MV+AT1R-shRNA group, the protein expressions of AT1R, BIP, PDI, COL1A1 and α-SMA were decreased (AT1R/β-actin: 0.53±0.03 vs. 1.40±0.02, BIP/β-actin: 1.73±0.15 vs. 2.79±0.07, PDI/β-actin: 1.04±0.07 vs. 2.07±0.02, COL1A1/α-Tubulin: 1.29±0.11 vs. 2.60±0.15, α-SMA/α-Tubulin: 1.27±0.10 vs. 2.80±0.25, all P < 0.01). The number of E-cad+/AT1R+ and E-cad+/BIP+ cells in lung tissue decreased, and the fluorescence intensity of COL1A1 and α-SMA decreased. There was no statistically significant difference in the indicators between AT1R-shRNA group and Sham group. MV up-regulate the expression of AT1R in alveolar epithelial cells, activate the AT1R pathway, induce ERS and promote the progression of MVPF.

Spin-polarized Majorana zero modes in proximitized superconducting penta-silicene nanoribbons

We theoretically propose penta-silicene nanoribbons (p-SiNRs) with induced p-wave superconductivity as a platform for the emergence of spin-polarized Majorana zero-modes (MZMs). The model explicitly considers the key ingredients of well-known Majorana hybrid nanowire setups: Rashba spin-orbit coupling, magnetic field perpendicular to the nanoribbon plane, and first nearest neighbor hopping with p-wave superconducting pairing. The energy spectrum of the system, as a function of chemical potential, reveals the existence of MZMs with a well-defined spin orientation localized at the opposite ends of both the top and bottom chains of the p-SiNR, associated with well-localized and nonoverlapping wave function profiles. Well-established experimental techniques enable the fabrication of highly ordered p-SiNRs, complemented by a thin lead film on top, responsible for inducing p-wave superconductivity through proximity effect. Moreover, the emergence of MZMs with explicit opposite spin orientations for some set of model parameters opens a new avenue for exploring quantum computing operations, which accounts for both MZMs and spin properties, as well as for new MZMs probe devices based on spin-polarized electronic transport mechanisms.