scholarly journals Machine Learning Optimization of Photosynthetic Microbe Cultivation and Recombinant Protein Production

2021 ◽  
Author(s):  
Caitlin Gamble ◽  
Drew Bryant ◽  
Damian Carrieri ◽  
Eli Bixby ◽  
Jason Dang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Production ◽  
Low Cost ◽  
Therapeutic Proteins ◽  
High Growth ◽  
Arthrospira Platensis ◽  
Culture Conditions ◽  
Heterologous Proteins ◽  
Machine Learning Approach ◽  
Low Cost Manufacturing

Background: Arthrospira platensis (commonly known as spirulina) is a promising new platform for low-cost manufacturing of biopharmaceuticals. However, full realization of the platform's potential will depend on achieving both high growth rates of spirulina and high expression of therapeutic proteins. Objective: We aimed to optimize culture conditions for the spirulina-based production of therapeutic proteins. Methods: We used a machine learning approach called Bayesian black-box optimization to iteratively guide experiments in 96 photobioreactors that explored the relationship between production outcomes and 17 environmental variables such as pH, temperature, and light intensity. Results: Over 16 rounds of experiments, we identified key variable adjustments that approximately doubled spirulina-based production of heterologous proteins, improving volumetric productivity between 70% to 100% in multiple bioreactor setting configurations. Conclusion: An adaptive, machine learning-based approach to optimize heterologous protein production can improve outcomes based on complex, multivariate experiments, identifying beneficial variable combinations and adjustments that might not otherwise be discoverable within high-dimensional data.

scholarly journals Using Ground Reaction Forces To Predict Fatigue In Treadmill Running: A Machine Learning Approach

2021 ◽  
Author(s):  
Sérgio Baldo Junior ◽  
Thiago Faria dos Santos ◽  
Renato Tinós ◽  
Paulo Roberto Pereira Santiago
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Good Accuracy ◽  
Low Cost ◽  
Treadmill Running ◽  
Ground Reaction Forces ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Reaction Forces ◽  
Interesting Alternative

Abstract The analysis of running patterns, especially those associated with fatigue, can help specialists in designing more efficient workouts and preventing injuries in high-performance sports. However, classifying running patterns is not trivial for humans. An interesting alternative is to use Machine Learning methods, such as Artificial Neural Networks (ANNs), to classify running patterns. In this work, ground reaction forces are measured by sensors coupled to the base of a low-cost open-source treadmill. ANNs are used to classify the force signals and to indicate the occurrence of fatigue. Different features, extracted from the force signals, are proposed and investigated. A Genetic Algorithm (GA) is used to select the best features. The experimental results indicate that the ANN is able to classify the running patterns with good accuracy. In addition, some features selected by the GA provide important information regarding the identification of fatigue in treadmill running.

scholarly journals Ferroelectret-based Hydrophone Employed in Oil Identification—A Machine Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2979 ◽  
Author(s):  
Daniel R. de Luna ◽  
T.T.C. Palitó ◽  
Y.A.O. Assagra ◽  
R.A.P. Altafim ◽  
J.P. Carmo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Analysis ◽  
Low Cost ◽  
Mineral Oil ◽  
Supervised Machine Learning ◽  
Classification Error ◽  
Acoustic Transducer ◽  
Machine Learning Approach ◽  
Electrical Transformers

This work focuses on acoustic analysis as a way of discriminating mineral oil, providing a robust technique, immune to electromagnetic noise, and in some cases, depending on the applied sensor, a low-cost technique. Thus, we propose a new method for the diagnosis of the quality of mineral oil used in electrical transformers, integrating a ferroelectric-based hydrophone and an acoustic transducer. Our classification solution is based on a supervised machine learning technique applied to the signals generated by an in-home built hydrophone. A total of three statistical datasets entries were collected during the acoustic experiments on four types of oils. The first, the second, and third datasets contain 180, 240, and 420 entries, respectively. Eighty-four features were considered from each dataset to apply to two classification approaches. The first classification approach is able to distinguish the oils from the four possible classes with a classification error less than 2%, while the second approach is able to successfully classify the oils without errors (e.g., with a score of 100%).

scholarly journals Optical system for on line monitoring of welding: a machine learning approach for optimal set up

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (4) ◽  
pp. 4 ◽  
Author(s):  
David Di Gasbarro ◽  
Giulio D'Emilia ◽  
Emanuela Natale
Keyword(s):  
Machine Learning ◽  
Vision System ◽  
Low Cost ◽  
Laser Sheet ◽  
Measuring System ◽  
P Value ◽  
Learning Approach ◽  
Optical Measuring ◽  
Machine Learning Approach ◽  
On Line

<p class="Abstract">In this paper a methodology is described for continuous checking of the settings of a low cost vision system for automatic geometrical measurement of welding embedded on components of complicated shape. The measurement system is based on a laser sheet. Measuring conditions and the corresponding uncertainty are analyzed by evaluating their p-value and its closeness to an optimal measurement configuration also when working conditions are changed. The method aims to check the holding of optimal measuring conditions by using a machine learning approach for the vision system: based on a such methodology single images can be used to check the settings, therefore allowing a continuous and on line monitoring of the optical measuring system capabilities.</p><p class="Abstract">According to this procedure, the optical measuring system is able to reach and to hold uncertainty levels adequate for automatic dimensional checking of welding and of defects, taking into account the effects of system hardware/software incorrect settings and environmental effects, like varying lighting conditions. The paper also studies the effects of process variability on the method for quantitative evaluation, in order to propose on line solutions for this system.</p>

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