Designing high-performance microstrip quad-band bandpass filters (for multi-service communication systems): a novel method based on artificial neural networks

Microtubules (MTs) are cylindrical polymers of the tubulin dimer, are constituents of all eukaryotic cells cytoskeleton and are involved in key cellular functions and are claimed to be involved as sub-cellular information or quantum information communication systems. The authors evaluated some biophysical properties of MTs by means of specific physical measures of resonance and birefringence in presence of electromagnetic field, on the assumption that when tubulin and MTs show different biophysical behaviours, this should be due to their special structural properties. Actually, MTs are the closest biological equivalent to the well-known carbon nanotubes (CNTs), whose interesting biophysical and quantum properties are due to their peculiar microscopic structure. The experimental results highlighted a physical behaviour of MTs in comparison with tubulin. The dynamic simulation of MT and tubulin subjected to electromagnetic field was performed via MD tools. Their level of self-organization was evaluated using artificial neural networks, which resulted to be an effective method to gather the dynamical behaviour of cellular and non-cellular structures and to compare their physical properties.

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Exploring Structural and Dynamical Properties Microtubules by Means of Artificial Neural Networks

Complexity Science, Living Systems, and Reflexing Interfaces ◽

10.4018/978-1-4666-2077-3.ch005 ◽

2013 ◽

pp. 78-91

Author(s):

R. Pizzi ◽

S. Fiorentini ◽

G. Strini ◽

M. Pregnolato

Keyword(s):

Neural Networks ◽

Electromagnetic Field ◽

Artificial Neural Networks ◽

Communication Systems ◽

Dynamical Behaviour ◽

Information Communication ◽

Cellular Functions ◽

Dynamical Properties ◽

Physical Measures ◽

Artificial Neural

Microtubules (MTs) are cylindrical polymers of the tubulin dimer, are constituents of all eukaryotic cells cytoskeleton and are involved in key cellular functions and are claimed to be involved as sub-cellular information or quantum information communication systems. The authors evaluated some biophysical properties of MTs by means of specific physical measures of resonance and birefringence in presence of electromagnetic field, on the assumption that when tubulin and MTs show different biophysical behaviours, this should be due to their special structural properties. Actually, MTs are the closest biological equivalent to the well-known carbon nanotubes (CNTs), whose interesting biophysical and quantum properties are due to their peculiar microscopic structure. The experimental results highlighted a physical behaviour of MTs in comparison with tubulin. The dynamic simulation of MT and tubulin subjected to electromagnetic field was performed via MD tools. Their level of self-organization was evaluated using artificial neural networks, which resulted to be an effective method to gather the dynamical behaviour of cellular and non-cellular structures and to compare their physical properties.

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A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming

Algorithms ◽

10.3390/a13050124 ◽

2020 ◽

Vol 13 (5) ◽

pp. 124 ◽

Cited By ~ 2

Author(s):

Jianshen Zhu ◽

Chenxi Wang ◽

Aleksandar Shurbevski ◽

Hiroshi Nagamochi ◽

Tatsuya Akutsu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Chemical Compounds ◽

Mixed Integer ◽

Training Phase ◽

Cycle Index ◽

Inference Problem ◽

Inverse Prediction ◽

Novel Method ◽

Artificial Neural

Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method.

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Structural Vibration Tests: Use of Artificial Neural Networks for Live Prediction of Structural Stress

Applied Sciences ◽

10.3390/app10238542 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8542

Author(s):

Laura Wilmes ◽

Raymond Olympio ◽

Kristin M. de Payrebrune ◽

Markus Schatz

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Space Structure ◽

Structural Vibration ◽

Future Application ◽

Different Types ◽

Novel Method ◽

Artificial Neural ◽

Input Load ◽

Vibration Tests

One of the ongoing tasks in space structure testing is the vibration test, in which a given structure is mounted onto a shaker and excited by a certain input load on a given frequency range, in order to reproduce the rigor of launch. These vibration tests need to be conducted in order to ensure that the devised structure meets the expected loads of its future application. However, the structure must not be overtested to avoid any risk of damage. For this, the system’s response to the testing loads, i.e., stresses and forces in the structure, must be monitored and predicted live during the test. In order to solve the issues associated with existing methods of live monitoring of the structure’s response, this paper investigated the use of artificial neural networks (ANNs) to predict the system’s responses during the test. Hence, a framework was developed with different use cases to compare various kinds of artificial neural networks and eventually identify the most promising one. Thus, the conducted research accounts for a novel method for live prediction of stresses, allowing failure to be evaluated for different types of material via yield criteria.

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