Structural dynamic modeling is a key element in the analysis of building behavior for different environmental factors. Having this in mind, the authors propose a simple nonlinear model for studying the behavior of buildings in the case of earthquakes. Structural analysis is a key component of seismic design and evaluation. It began more than 100 years ago when seismic regulations adopted static analyzes with lateral loads of about 10% of the weight of the structure. Due to the dynamics and non-linear response of the structures, advanced analytical procedures were implemented over time. The authors' approach is the following: having a nonlinear dynamic model (in this case, a multi-segment inverted pendulum on a cart with mass-spring-damper rotational joints) and at least two datasets of a building, the parameters of the building's model are estimated using optimization algorithms: Particle Swarm Optimization (PSO) and Differential Evolution (DE). Not having much expertise on structural modeling, the present paper is focused on two aspects: the proposed model's performance and the optimization algorithms performance. Results show that among these algorithms, the DE algorithm outperformed its counterpart in most situations. As for the model, the results show us that it performs well in prediction scenarios.
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http://dx.doi.org/10.3390/s20071980 | DOI Listing |
Viruses
December 2024
Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung 41354, Taiwan.
This study investigated a library of known and novel glycyrrhizic acid (GL) conjugates with amino acids and dipeptide esters, as inhibitors of the DENV NS2B-NS3 protease. We utilized docking algorithms to evaluate the interactions of these GL derivatives with key residues (His51, Asp75, Ser135, and Gly153) within 10 Å of the DENV-2 NS2B-NS3 protease binding pocket (PDB ID: 2FOM). It was found that compounds and exhibited unique binding patterns, forming hydrogen bonds with Asp75, Tyr150, and Gly153.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
View Article and Find Full Text PDFVaccines (Basel)
December 2024
Center for Vaccines and Immunology, University of Georgia, Athens, GA 30605, USA.
Standard-of-care influenza vaccines contain antigens that are typically derived from components of wild type (WT) influenza viruses. Often, these antigens elicit strain-specific immune responses and are susceptible to mismatch in seasons where antigenic drift is prevalent. Thanks to advances in viral surveillance and sequencing, influenza vaccine antigens can now be optimized using computationally derived methodologies and algorithms to enhance their immunogenicity.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.
Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Xuejiawan Power Supply Company, Ordos 010300, China.
Recently, massive intelligent applications have emerged for the smart grid (SG), such as inspection and sensing. To support these applications, there have been high requirements on wireless communication for the SG, especially in remote areas. To tackle these challenges, a UAV-assisted heterogeneous wireless network is proposed in this paper for the SG, where multiple UAVs and a macro base station collaboratively provide a wide range of communication services.
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