Abstract This study evaluates the accuracy of displacement demand-estimating equations in seismic design codes by benchmarking them against results from nonlinear time-history analyses. The investigation focuses on single-degree-of-freedom (SDOF) systems, examining maximum displacement demands and conducting a reliability-based assessment. Natural ground motions, whose spectra align with the elastic design spectrum, and synthetic earthquakes (whose spectra matched the elastic design spectrum) are considered. The analyzed SDOF systems cover fundamental vibration periods between 0.1 and 2.0 s, assuming elastic–perfectly plastic behavior with strength reduction factors ranging from 2 to 8. Introducing a wide range of probabilistic structural parameters into analytical models, the results demonstrate that code-based displacement estimates lead to nonuniform reliability levels, strongly influenced by vibration period, soil type, and strength factor. To overcome this limitation, the study introduces reliability-based displacement demand equations designed to provide uniform reliability across different conditions. The proposed framework is capable of enhancing seismic design by ensuring displacement demands correspond to a consistent confidence level, thereby enabling safer and more damage-tolerant structural performance.
This study investigates the effectiveness of ensemble combining techniques in improving the accuracy of satellite-based gridded precipitation estimations in the Sirjan watershed, Iran. For this purpose, four satellite-based products (CHIRPS, MSWEP, PERSIANN-CDR, and PERSIANN-CCS-CDR) were analyzed over 25 years (1996–2020). Various ensemble combining techniques, including Simple Model Averaging (SMA), Weighted Averaging Model (WAM), Multi-Model Super Ensemble (MMSE), and Modified MMSE (M3SE), were implemented to generate new estimations of precipitation based on satellite-based products. The performance of the ensemble combining techniques was evaluated through a series of comparative tests. The findings indicate that the MMSE and M3SE significantly improve the accuracy of precipitation estimates compared to individual products, increasing the accuracy rate up to 48
Squalene, a high-value terpenoid, can be sustainably produced by Aurantiochytrium Ch25. This study integrated experimental optimization using Taguchi design with genome-scale metabolic modeling (GEM) to investigate its biosynthesis. The Taguchi L9 orthogonal array identified a promising culture condition (glucose 15 g/L, yeast extract 3 g/L, seawater 20
Mycotoxins, hazardous secondary metabolites naturally produced by filamentous fungi (molds), pose ongoing global concerns for food safety due to their wide range of toxic effects in humans and animals, as well as their contribution to significant economic losses. A common mycotoxin, zearalenone (ZEA), is mostly present in grains, such as rye, sorghum, barley, wheat, and maize. Its detrimental effects, such as mutagenicity and carcinogenicity, pose a serious risk to both human and animal health. The current work created a sensitive electrochemical sensor to determine ZEA utilizing a screen-printed graphene electrode modified with MoS2 nanosheets/MnO2 nanorods. Fourier transform infrared and transmission electron microscopy were used to investigate the properties of the as-fabricated sensor. The detection limit based on S/N = 3 was determined to be 3.0 nM, and the oxidation current of ZEA on the modified electrode surface showed a linear response in the range of 0.008–100.0 µM. The applicability of the modified electrode was evaluated by determining ZEA in food specimens using the standard addition method, yielding acceptable relative standard deviations (2.9–3.4
Dimensionality Reduction plays a pivotal role in improving feature learning accuracy and reducing training time by eliminating redundant features, noise, and irrelevant data. Nonnegative Matrix Factorization (NMF) has emerged as a popular and powerful method for dimensionality reduction. Despite its extensive use, there remains a need for a comprehensive analysis of NMF in the context of dimensionality reduction. To bridge this gap, this article presents a comprehensive survey of NMF, focusing on its applications in both feature extraction and feature selection. We propose a novel classification scheme for dimensionality reduction to enhance understanding of its core principles. Subsequently, we delve into a thorough summary of diverse NMF approaches used for feature extraction and selection. Furthermore, we discuss the latest research trends and potential future directions for leveraging NMF in dimensionality reduction, aiming to highlight areas that need further exploration and development.
