The importance of dynamic behaviour of vibrating systems on the response in case of non-Gaussian random excitations

Abstract Dynamic response of vibrating system subjected to non-Gaussian random loads was investigated through a set of numerical simulation on several lumped systems aimed to determine whether and in what form the dynamic behaviour of a vibrating system transfers or masks non-Gaussianity features of the input to the output response. Indeed, in several numerical and experimental activities performed on a Y-shaped specimen it was observed how the system response, both in terms of displacement or stress, changed according to an input variation (stationary and non-stationary Gaussian and non-Gaussian load time histories) and according to a change of the system frequency response function. Moreover, it was observed that even if the system was excited in its frequency range, the response remains unchanged and similar to the input in case of non-stationary and non-Gaussian load, removing preliminarily the possibility to use spectral methods for damage evaluation, going necessarily back to a more "expensive" time-domain analysis. Since the system response characteristics may change significantly according to the input excitation features and to the dynamic system parameters allowing, in some cases, the use of spectral techniques for fatigue damage evaluation also in case of non-Gaussian input loads, the aim of this paper is to understand whether and how the dynamic behaviour of a generic mechanical system transforms the non-Gaussian input excitations into a Gaussian response. To this aim several numerical displacement responses of 1-dof lumped systems characterized by different frequency response functions (resonance frequency position and damping) were analysed and investigated for different stationary and non-stationary Gaussian and non-Gaussian excitations. In such a way, it was possible to a-priori establish under what circumstances the frequency-domain approaches can be adopted to compute the fatigue damage of real mechanical systems

sine sweep qualification test for engine components the choice of simulation technique

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piezoresistive dynamic simulations of fdm 3d printed embedded strain sensors a new modal approach

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TCT-737 MONITORING THE LEARNING CURVE AND QUALITY OF CARE IN TAVI PROCEDURES BY CUSUM ANALYSIS

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INSIDIA:a FIJI macro delivering high-throughput and high-content spheroid invasion analysis

Time-series image capture of in vitro 3D spheroidal cancer models embedded within an extracellular matrix affords examination of spheroid growth and cancer cell invasion. However, a customizable, comprehensive and open source solution for the quantitative analysis of such spheroid images is lacking. Here, the authors describe INSIDIA (INvasion SpheroID ImageJ Analysis), an open-source macro implemented as a customizable software algorithm running on the FIJI platform, that enables high-throughput high-content quantitative analysis of spheroid images (both bright-field gray and fluorescent images) with the output of a range of parameters defining the spheroid “tumor” core and its invasive characteristics

Pharmacologic or Genetic Targeting of Glutamine Synthetase Skews Macrophages toward an M1-like Phenotype and Inhibits Tumor Metastasis.

Glutamine-synthetase (GS), the glutamine-synthesizing enzyme from glutamate, controls important events, including the release of inflammatory mediators, mammalian target of rapamycin (mTOR) activation, and autophagy. However, its role in macrophages remains elusive. We report that pharmacologic inhibition of GS skews M2-polarized macrophages toward the M1-like phenotype, characterized by reduced intracellular glutamine and increased succinate with enhanced glucose flux through glycolysis, which could be partly related to HIF1α activation. As a result of these metabolic changes and HIF1α accumulation, GS-inhibited macrophages display an increased capacity to induce T cell recruitment, reduced T cell suppressive potential, and an impaired ability to foster endothelial cell branching or cancer cell motility. Genetic deletion of macrophagic GS in tumor-bearing mice promotes tumor vessel pruning, vascular normalization, accumulation of cytotoxic T cells, and metastasis inhibition. These data identify GS activity as mediator of the proangiogenic, immunosuppressive, and pro-metastatic function of M2-like macrophages and highlight the possibility of targeting this enzyme in the treatment of cancer metastasis

Inhibiting the growth of 3D brain cancer models with bio-coronated liposomal temozolomide

Nanoparticles (NPs) have emerged as an effective means to deliver anticancer drugs into the brain. Among various forms of NPs, liposomal temozolomide (TMZ) is the drug-of-choice for the treatment and management of brain tumours, but its therapeutic benefit is suboptimal. Although many possible reasons may account for the compromised therapeutic efficacy, the inefficient tumour penetration of liposomal TMZ can be a vital obstacle. Recently, the protein corona, i.e., the layer of plasma proteins that surround NPs after exposure to human plasma, has emerged as an endogenous trigger that mostly controls their anticancer efficacy. Exposition of particular biomolecules from the corona referred to as protein corona fingerprints (PCFs) may facilitate interactions with specific receptors of target cells, thus, promoting efficient internalization. In this work, we have synthesized a set of four TMZ-encapsulating nanomedicines made of four cationic liposome (CL) formulations with systematic changes in lipid composition and physical−chemical properties. We have demonstrated that precoating liposomal TMZ with a protein corona made of human plasma proteins can increase drug penetration in a 3D brain cancer model derived from U87 human glioblastoma multiforme cell line leading to marked inhibition of tumour growth. On the other side, by fine-tuning corona composition we have also provided experimental evidence of a non-unique effect of the corona on the tumour growth for all the complexes investigated, thus, clarifying that certain PCFs (i.e., APO-B and APO-E) enable favoured interactions with specific receptors of brain cancer cells. Reported results open new perspectives into the development of corona-coated liposomal drugs with enhanced tumour penetration and antitumour efficacy

Translating Material Science into Bone Regenerative Medicine Applications: State-of-The Art Methods and Protocols

In the last 20 years, bone regenerative research has experienced exponential growth thanks to the discovery of new nanomaterials and improved manufacturing technologies that have emerged in the biomedical field. This revolution demands standardization of methods employed for biomaterials characterization in order to achieve comparable, interoperable, and reproducible results. The exploited methods for characterization span from biophysics and biochemical techniques, including microscopy and spectroscopy, functional assays for biological properties, and molecular profiling. This review aims to provide scholars with a rapid handbook collecting multidisciplinary methods for bone substitute R&D and validation, getting sources from an up-to-date and comprehensive examination of the scientific landscape

CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

Recently, there has been a shift from modeling driven approach to data driven approach in Agent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models [1, 2]. In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, evaluation of the output of the simulation platform. That raises the question how to manage empirical data, simulation data and compare those data in such agent-based simulation platform. In this paper, we first introduce a logical framework for data driven approach in agent-based modeling and simulation. The introduced framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform). Secondly, we demonstrate the application of CFBM for data driven approach via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to initialize and validate the models. The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach