Multiple Factors Coupling Probability Calculation Model of Transmission Line Ice-Shedding DOI Creative Commons
Hao Pan,

Fangrong Zhou,

Yi Ma

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(5), P. 1208 - 1208

Published: March 3, 2024

After a transmission line is covered by ice in winter, ice-shedding and vibration occurs under special meteorological external dynamic conditions, which leads to intense shaking. Transmission often cause flashover trips outages. In January 2018, three 500 kV lines, namely, the Guanli line, Dushan Guanqiao tripped cut off due Anhui province, seriously threatening safe operation of large power grid. Current studies mainly focus on analyzing influence factors characteristics investigating suppression measures, but they only analyze correlation between each influencing factor icing or shedding, do not consider coupling effects multiple factors. this paper, key probability distribution were analyzed, multiple-factor fault calculation model based Copula function was proposed. The calculated directly considering at same time, effectively overcame error caused multi-factor transformation fuzzy membership degree other methods. It provided an important decision-making basis for preventing controlling faults.

Language: Английский

Evaluation of the probiotic, anti-bacterial, anti-biofilm, and safety properties of Lacticaseibacillus paracasei B31-2 DOI Creative Commons
Behrooz Alizadeh Behbahani, Hossein Jooyandeh, Morteza Taki

et al.

LWT, Journal Year: 2024, Volume and Issue: 207, P. 116676 - 116676

Published: Aug. 26, 2024

Language: Английский

Citations

14

Evaluation of the probiotic, anti-microbial, anti-biofilm, and safety properties of Levilactobacillus brevis Lb13H DOI Creative Commons
Mostafa Rahmati‐Joneidabad, Behrooz Alizadeh Behbahani, Morteza Taki

et al.

LWT, Journal Year: 2024, Volume and Issue: 207, P. 116636 - 116636

Published: Aug. 15, 2024

In this research, the probiotic potential, anti-microbial, anti-biofilm, and safety properties of Levilactobacillus brevis Lb13H were investigated. Also, Gaussian Process Regression (GPR) model was applied to predict some experimental parameters. The strain demonstrated resistance acidic pH, gastric intestinal juices, bile salts. Additionally, exhibited 45.34% surface hydrophobicity, 36.55% auto-aggregation capacity, 26.30% co-aggregation, 10.20% adhesion Caco-2 cells, 42.57% cholesterol removal, 48.63% radical-scavenging properties. able inhibit Listeria monocytogenes cells by 31.30%, its competitive inhibition percentage 37.5%. Furthermore, replacement L. with measured at 22.10%. most significant antimicrobial effect observed against monocytogenes, while least noted Rhizopus stolonifer. Haemolytic activity, DNase production, biogenic amine production not in strain, which also found be sensitive antibiotics ciprofloxacin chloramphenicol. cell-free supernatant effectively inhibited degraded biofilm formation achieving effectiveness rates 40.80% 36.60% respectively. results modeling indicated that there is any difference between actual predicted data GPR can variables high accuracy.

Language: Английский

Citations

12

A hybrid electric load forecasting model based on decomposition considering fisher information DOI

Wenjing Xiao,

Mo Li, Zhanxing Xu

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 364, P. 123149 - 123149

Published: April 10, 2024

Language: Английский

Citations

11

Computational investigation on physical properties of lead based perovskite RPbBr3 (R = Cs, Hg, and Ga) materials for photovoltaic applications DOI Creative Commons
Muhammad Khuram Shahzad, Shoukat Hussain, Ghulam Abbas Ashraf

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 24, 2024

In the modern era, major problem is solving energy production and consumption. For this purpose, perovskite materials meet these issues fulfill at a low cost. Density functional theory Cambridge Serial Total Energy Package (CASTEP) are used to examine characteristics of cubic inorganic perovskites RPbBr3 (R = Cs, Hg, Ga). context generalized gradient approximation (GGA), ultrasoft pseudo-potential plane wave technique Perdew–Burke–Ernzerhof exchange–correlation for investigations. Structural, mechanical, electronics, optical properties investigated using CASTEP code. According structural properties, compounds have nature with space 221 (Pm3m). Compounds formation (− 3.46, − 2.21, 3.14 eV)of (CsPbBr3, HgPbBr3, GaPbBr3) phonon calculations studied find that stable. The results our investigation show narrow bandgaps direct kind, 1.85 eV CsPbBr3, 1.56 1.71 GaPbBr3, respectively, indicating they may be improve conductivity. Additionally, anisotropy (2.135, 3.651, 10.602), Pugh's ratio (1.87, 2.25, 2.14), Poison's (0.27, 0.31, 0.29) traits display ductile nature. CsPbBr3 compound showed significant conductivity absorption in terms their especially visible region, which makes them suitable use solar cell applications as well LED applications.

Language: Английский

Citations

11

Light-Activated Nanofibers: Advances in Photo-Responsive Electrospun Polymer Technologies DOI

Elyas Sharif Bakhsh,

Masoud Tavakoli Dare, Aliakbar Jafari

et al.

Polymer-Plastics Technology and Materials, Journal Year: 2024, Volume and Issue: 64(4), P. 397 - 438

Published: Oct. 1, 2024

Language: Английский

Citations

11

Nanogels in Biomedical Engineering: Revolutionizing Drug Delivery, Tissue Engineering, and Bioimaging DOI

Atieh Janmaleki Dehchani,

Aliakbar Jafari,

Farangis Shahi

et al.

Polymers for Advanced Technologies, Journal Year: 2024, Volume and Issue: 35(10)

Published: Oct. 1, 2024

ABSTRACT Nanogels represent a significant innovation in the fields of nanotechnology and biomedical engineering, combining properties hydrogels nanoparticles to create versatile platforms for drug delivery, tissue bioimaging, other applications. These nanoscale hydrogels, typically ranging from 10 1000 nm, possess unique characteristics such as high water content, biocompatibility, ability encapsulate both hydrophilic hydrophobic molecules. The review explores synthesis, structural configurations, stimuli‐responsive nature nanogels, highlighting their adaptability targeted including across challenging barriers like blood–brain barrier. Furthermore, paper delves into applications particularly delivery systems, demonstrating potential revolutionize these fields. Despite promising preclinical results, challenges remain translating technologies clinical practice, issues related stability, scalability, regulatory approval. concludes by discussing future perspectives, emphasizing need further research optimize ultimately aiming enhance efficacy safety settings.

Language: Английский

Citations

11

A hydrological process-based neural network model for hourly runoff forecasting DOI
Shuai Gao, Shuo Zhang, Yuefei Huang

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 176, P. 106029 - 106029

Published: April 3, 2024

Language: Английский

Citations

10

Soft computing models for prediction of bentonite plastic concrete strength DOI Creative Commons
Waleed Bin Inqiad, Muhammad Faisal Javed, Kennedy C. Onyelowe

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 5, 2024

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite added to mixes for adsorption toxic metals. The modified design BPC, as compared normal concrete, requires a reliable tool predict its strength. Thus, this study presents novel attempt at application two innovative evolutionary techniques known multi-expression programming (MEP) gene expression (GEP) boosting-based algorithm AdaBoost 28-day compressive strength ( ) BPC based on mixture composition. MEP GEP algorithms expressed their outputs form an empirical equation, while failed do so. were trained using dataset 246 points gathered from published literature having six important input factors predicting. developed models subject error evaluation, results revealed that all satisfied suggested criteria had correlation coefficient (R) greater than 0.9 both training testing phases. However, surpassed terms accuracy demonstrated lower RMSE 1.66 2.02 2.38 GEP. Similarly, objective function value was 0.10 0.176 0.16 MEP, which indicated overall good performance techniques. Shapley additive analysis done model gain further insights into prediction process, cement, coarse aggregate, fine aggregate are most predicting BPC. Moreover, interactive graphical user interface (GUI) has been be practically utilized civil engineering industry

Language: Английский

Citations

9

Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches DOI Creative Commons

Laiba Khawaja,

Usama Asif, Kennedy C. Onyelowe

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 6, 2024

Accurately predicting the Modulus of Resilience (MR) subgrade soils, which exhibit non-linear stress–strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques determining MR are often costly and time-consuming. This study explores efficacy Genetic Programming (GEP), Multi-Expression (MEP), Artificial Neural Networks (ANN) in forecasting using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that GEP consistently outperforms MEP ANN models, demonstrating lowest error metrics highest correlation indices (R2). During training, achieved an R2 value 0.996, surpassing (R2 = 0.97) 0.95) models. Sensitivity SHAP (SHapley Additive exPlanations) analysis also performed gain insights into input parameter significance. revealed confining stress (21.6%) dry density (26.89%) most influential parameters MR. corroborated these findings, highlighting critical impact on predictions. underscores reliability as a robust tool precise prediction applications, providing valuable performance significance across various machine-learning (ML) approaches.

Language: Английский

Citations

9

Stretchable Electronics: Advances in Elastic Conductive Fibers for Multifunctional Applications DOI
Aliakbar Jafari

Organic Electronics, Journal Year: 2024, Volume and Issue: unknown, P. 107145 - 107145

Published: Sept. 1, 2024

Language: Английский

Citations

9