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Study on the migration mechanism of heterogeneous cuttings in long-reach horizontal wells
Camellia sinensis powder rich in epicatechin and polyphenols attenuates isoprenaline induced cardiac injury by activating the Nrf2 HO1 antioxidant pathway in rats
Threshold effect of prognostic nutritional index on mortality in geriatric hip fracture patients
Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models
Abstract Accurate parameter estimation in photovoltaic (PV) models is essential for optimizing solar energy systems, enhancing their efficiency, and ensuring precise performance predictions. This paper proposes a novel Improved version of Rime Metaheuristic Optimization (RMO) influenced by rime growth and combined with Quadratic Interpolation Learning (QIL) technique for the simulation and design of the triple-Diode Model (DM). This novel combination seeks to provide a more accurate perspective in the field of solar energy optimization by managing the complexities of PV module characterization with greater flexibility and resilience. By meticulously replicating the distinctive features of both processes, the hard-rime puncture and soft-rime searching are disclosed. The QIL technique improves the search process by selecting three different rime particles rather than relying solely on the current best solution. This selection allows for a more diverse set of candidate solutions, fostering better exploration and reducing premature convergence to local optima. By leveraging quadratic interpolation, QIL adjusts the solution updates in a flexible and nonlinear manner, enabling a more precise and adaptive parameter estimation process. QIL’s capacity to adjust its quadratic function in a flexible and non-linear way makes it easier to navigate complex terrain. The novel IRMO as well as the original RMO are developed for predicting PV parameters for the triple-diode model (DM) of the three distinct PV modules which are Photowatt PWP201, STM6-40/36, and R.T.C France. In accordance with other published publications, the results of the suggested IRMO are also compared with those of contemporary algorithms. According to the results of the simulation, the upgraded IRMO shows significant average improvements of 49.56%, 62.56%, and 34.15% for the three modules, correspondingly.
MMP14 and DDR2 are potential molecular markers for metastatic triple-negative breast cancer
Plant leaf disease detection using vision transformers for precision agriculture
Development and evaluation of cepharanthine-β-cyclodextrin inclusion complex oral tablets for prevention and treatment of COVID-19 lung injury
Helicobacter pylori eradication and gastric cancer prevention in a pooled analysis of large-scale cohort studies in Japan
Historical data analysis and future prediction of lung cancer in Zhejiang province, China
Development of the coupled smoothing technique λS-FEM for mechanical analysis of twist drills
Abstract A coupled smoothing technique, λ S-FEM, is introduced to improve the accuracy of numerical simulations in the mechanical analysis of twist drills. This method combines the edge-based smoothing finite element method (ES-FEM) with the node-based smoothing finite element method (NS-FEM). The λ S-FEM model is designed to evaluate the mechanical properties of twist drills made from tungsten carbide (WC), titanium nitride (TiN) coatings, and high-speed steel (M35), providing a theoretical basis for lifespan estimation and wear prediction. Linear tetrahedral elements construct the smoothing domain, and optimized weighting parameters balance and combine the smoothed strains from ES-FEM and NS-FEM. This integration enhances the accuracy of solutions for displacements, stresses, and strain energies, constructing stiffness matrices with optimal precision. The method’s feasibility is demonstrated through numerical case studies involving flange and shell extractor components. Analyses of straight shank twist drills compare displacement and stress magnitudes across FEM, S-FEM, and λ S-FEM under various degrees of freedom (DOF). Results show λ S-FEM significantly reduces errors, particularly with coarse meshes, validating its practical application in solving engineering challenges.
Analysis of spatiotemporal changes and driving forces of ecological environment quality in Northwest China mining area
Impact of AZFc deletion subtypes on sperm retrieval rates via micro-TESE and ICSI outcomes in non-obstructive azoospermia patients
Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage
Bacillus megaterium favours CO₂ mineralization into CaCO₃ over the ureolytic pathway
Abstract Microbially induced calcite precipitation (MICP) has long been the focus of material scientists, environmental microbiologists and civil engineers because of its potential to yield biosynthesized binders that can serve as alternatives to cement or resins. Several microbial strains play crucial roles in this process and catalyse pathways for the formation of minerals, which are believed to substantially reduce the environmental impact of building materials and activities. Among the studied strains, Bacillus megaterium is not as common as Sporosarcina species. The latter microorganisms are well known to drive the fastest ureolytic-driven MICP process, i.e., precipitation of CaCO3 after urea breakdown into carbonate and CaCl2 addition to the system. This paper sheds light on the activities of B. megaterium, which possesses dual enzymatic capabilities for MICP and is equipped with both the enzymes urease and carbonic anhydrase. We postulate that, depending on the growth conditions, B. megaterium can activate either of these genes to ultimately induce CaCO3 precipitation. Herein, experiments are carried out in open and closed systems. C13-labelled urea is employed to identify the carbon source in the precipitated CaCO3. The results from Fourier transform infrared spectroscopy (FTIR) revealed the precipitation of calcite. In the presence of urea and CO2 at atmospheric levels, B. megaterium activates the ureolytic pathway to perform urea hydrolysis. However, at increased CO2 levels, more precisely, at levels greater than 470 times the atmospheric level, carbonic anhydrase is activated, catalysing the hydration of the molecule to produce HCO3 −. When C13-labelled urea was utilized, only 6% of the precipitated CaCO3 mineral was linked to ureolysis, and it was found that the remaining 94% was formed due to the mineralization of CO2. Overall, in this work, we aim to introduce the process conditions and protocols that favour the sequestration of atmospheric CO2 as CaCO3 via the metabolic activities of B. megaterium.