Currrent Issue: Volume 3, Number 1, 2015


Article

Assessment Evaluation of Bio-Ethanol Yield for Energizing Prosthetics Production Plant Based on Bacterial Growth and Shaking Rate

1Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria

2Department of Metallurgical and Materials Engineering, Federal University of Technology, Owerri, Nigeria

3Department of Mechanical Engineering, Imo State University, Owerri, Nigeria

4Department of Industrial Physics, Ebonyi State University, Abakiliki, Nigeria


Biomedical Science and Engineering. 2015, 3(1), 15-22
doi: 10.12691/bse-3-1-4
Copyright © 2015 Science and Education Publishing

Cite this paper:
C. I. Nwoye, P. C. Agu, B. C. Chukwudi, S. O. Nwakpa, I. A. Ijomah, N. E. Idenyi. Assessment Evaluation of Bio-Ethanol Yield for Energizing Prosthetics Production Plant Based on Bacterial Growth and Shaking Rate. Biomedical Science and Engineering. 2015; 3(1):15-22. doi: 10.12691/bse-3-1-4.

Correspondence to: C.  I. Nwoye, Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria. Email: nwoyennike@gmail.com

Abstract

This paper presents an assessment evaluation of bio-ethanol yield based on the bacteria growth (BG) and shaking rate (SR) during bioprocessing of sugar cane molasses with Saccharomyces cerevisiae. Critical computational analysis of generated experimental results indicates that the bio-ethanol yield response typified an empirical model which is exponential-linear in nature. The model was validated prior to evaluation of the yield response coefficient and predictive analysis of generated results. The validity of the derived model expressed as; ζ = 4.6335e[0.0068(ϑ/ɤ)] + 0.00012₰ - 0.00004ε was rooted on the core model expression ζ - 0.00012 ₰ = 4.6335e 0.0068(ϑ/ɤ) - 0.00004ε where both sides of the expression are correspondingly approximately equal. Results of ethanol yield were generated using regression model and its trend of distribution was compared with that from derived model for the purpose of verifying its validity relative to experimental results. The results of the verification process show very close dimensions of covered areas and alignment of curves designating ethanol yield, which precisely translated into significantly similar trend of data point’s distribution for experimental (ExD), derived model (MoD) and regression model-predicted (ReG) results. Ethanol yield per unit input ratio SR/ BG were evaluated from experimental, derived model & regression model predicted results as 0.0496, 0.0573 & 0.0565 rpm/ O.D respectively. Standard errors incurred in predicting ethanol yield for each value of SR, BG & SR/ BG considered as obtained from experiment, derived model and regression model were 0.13369, 0.9674 and 1.3380%, 1.3096, 1.3615 and 1.5300 % & 1.3701, 0.5969 and 1.1459 x 10-5 respectively. The operationally viable deviation range of model-predicted ethanol yield from the experimental results was 0.9 -13.47 %. This translated into 86.53-99.1 % operational confidence and reliability level for the derived models, as well as 0.86 - 0.99 yield response coefficient of ethanol to the input ratio SR/ BG. Consequently, in order to obtain high confidence level, the derived model considers input parameter value; 50 rpm (shaking rate) very extraneous. This was as a result of 23.66% deviation associating the use of this input parameter value.

Keywords

References

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Article

EMG Signals for Co-Activations of Major Lower Limb Muscles in Knee Joint Dynamics

1Department of Advanced Technology Fusion, Graduate School of Science & Engineering, Saga University 1 Honjo-machi, Saga, Japan


Biomedical Science and Engineering. 2015, 3(1), 9-14
doi: 10.12691/bse-3-1-3
Copyright © 2015 Science and Education Publishing

Cite this paper:
Md. T. I. Khan, T. Kurita. EMG Signals for Co-Activations of Major Lower Limb Muscles in Knee Joint Dynamics. Biomedical Science and Engineering. 2015; 3(1):9-14. doi: 10.12691/bse-3-1-3.

Correspondence to: Md.  T. I. Khan, Department of Advanced Technology Fusion, Graduate School of Science & Engineering, Saga University 1 Honjo-machi, Saga, Japan. Email: khan@me.saga-u.ac.jp

Abstract

Integrity analysis of knee joint involves a detail study of several anatomical parts such as bones, cartilage, tendons etc. Any disorder or damage of these anatomical parts causes severe knee disease, like osteoarthritis (OA), which is generally found in an increasing tendency, particularly, in an aged society. Although, the reasoning of OA in knee joint is not concentrated to the present paper, however, the influences of related muscular co-activities to knee flexor-extensor actions are figured out in the present research. Particularly, the muscle reflection actions of two major skeletal muscles at knee are investigated with aging functions of participants. EMG signals have been collected from the vastus lateralis and the gastrocnemius for the dynamic movements (standing and sitting) of knee joint. Aged participants (over 60 years old) and young participants (20 -25 years old) joined the experiments. Data have been collected from both legs, however, analysis is shown only for left leg in this paper. EMG sensors and the related devices of the present sensing technique have been installed based on the instructions of Biometric Co. Ltd. Result show that the voltage amplitudes of EMG signals fluctuate largely with increasing ages and thus, the result focuses on the postural effectiveness in muscular activities for the stability challenges of knee joints in their movements.

Keywords

References

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Article

The Main Molecules Involved in Human Mesenchymal Stem Cells Immunomodulation

1Laboratory of Genetics and Molecular Hematology, University of Sao Paulo Medical School, Sao Paulo, SP, Brazil

2Laboratory of Pharmaceutical Science, University of Sao Paulo, Sao Paulo, SP, Brazil


Biomedical Science and Engineering. 2015, 3(1), 4-8
doi: 10.12691/bse-3-1-2
Copyright © 2015 Science and Education Publishing

Cite this paper:
Felipe de Lara Janz, Helen Dutra Leite, Sergio Paulo Bydlowski. The Main Molecules Involved in Human Mesenchymal Stem Cells Immunomodulation. Biomedical Science and Engineering. 2015; 3(1):4-8. doi: 10.12691/bse-3-1-2.

Correspondence to: Felipe  de Lara Janz, Laboratory of Genetics and Molecular Hematology, University of Sao Paulo Medical School, Sao Paulo, SP, Brazil. Email: fljanz@usp.br

Abstract

Mesenchymal stem cells (MSCs) are described as undifferentiated cells with high capacity for self-renewal and differentiation ability in different tissues. MSCs are found in various locations in the adult organism as bone marrow, adipose tissue; and in fetal tissues as umbilical cord, placenta and amniotic fluid. They are able to produce and secrete a number of bioactive molecules with different effects: anti-fibrotic, angiogenic and mitogen. These cells also present a great immunomodulatory and anti-inflammatory potential described in experimental and human models. Several studies have demonstrated the ability of MSCs to suppress the proliferation and activation of T, B and NK cells in vitro and in vivo. Its low immunogenic action causes are not recognized by HLA mismatched receptor complex because they express low levels of MHC-I do not express MHC-II and costimulatory molecules CD40, CD80 and CD86. Because of these unique characteristics, MSCs arouse great interest in possible clinical applications in the therapy of diseases that affect the immune system. Although depending on the tissue microenvironment, the MSCs can also trigger inflammatory events. In this work, we described the main factors and another structures involved in MSCs immunoregulation.

Keywords

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Article

Multi-dimensional Analysis, Information Mining and Numerical Modeling for Small Samples of Biomedical Microfluidic Droplets

1School of life science, Zhengzhou Univ. 100 Science Avenue, Zhengzhou, P.R.China

2Henan institute of pharmaceutical science, Zhengzhou University, Zhengzhou, P.R.China


Biomedical Science and Engineering. 2015, 3(1), 1-3
doi: 10.12691/bse-3-1-1
Copyright © 2015 Science and Education Publishing

Cite this paper:
Peiyuan He, Liguo Zhang. Multi-dimensional Analysis, Information Mining and Numerical Modeling for Small Samples of Biomedical Microfluidic Droplets. Biomedical Science and Engineering. 2015; 3(1):1-3. doi: 10.12691/bse-3-1-1.

Correspondence to: Peiyuan  He, School of life science, Zhengzhou Univ. 100 Science Avenue, Zhengzhou, P.R.China. Email: hepeiyuan@live.com

Abstract

Information mining has turned to be increasingly important in the modern digital world. The mining results lead to the essential advances in not only information technology, but general natural science, such as life science, mechanics, numerical analysis, calculation technology, etc. These innovations permitted for the manipulation of biomicrofluidic droplets at micrometer-scale. Through the acquisition of raw data and the multidimensional analysis, numerical modeling can possibly be established and the further optimization can be processed. This work was focused on the multi-dimensional analysis based on small samples of the biomicrofludic droplet generation. Then numerical modeling was used to describe the fabrication of microdroplets in complex circumstances. Both linear modeling and nonlinear optimization were performed while the artificial intelligence technology was applied. Although the linear model presented the rough descriptive capacities of microdroplets variation, non-linear optimization improved its descriptive properties. Artificial neural network was established to depict the microfluidic droplets and its accuracy was validated satisfactorily.

Keywords

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