Production of Bacterial Cellulose Using Different Carbon Sources by Two-Stage Cultivation Strategy

2011 ◽  
Vol 201-203 ◽  
pp. 722-725 ◽  
Author(s):  
Jun Hong Lin ◽  
Yi Jen Lin ◽  
Jui Chih Kuo ◽  
Ting Yu Chen ◽  
Wen Pei Sung
Keyword(s):  
Bacterial Cellulose ◽  
Reducing Sugars ◽  
Cell Concentration ◽  
Carbon Sources ◽  
Gluconacetobacter Xylinus ◽  
Glucose Medium ◽  
Two Stage ◽  
Second Stage ◽  
Cultivation Strategy ◽  
Cell Volumes

The production of bacterial cellulose (BC) from Gluconacetobacter xylinus could be improved by the two-stage cultivation strategy. The jar fermentor was applied at first stage to increase cell concentration. At the second stage, the bacteria statically grew within beaker. Three different fraction of cell volumes were cultured in modified YPD-glucose medium or YPD-molasses one. In the modified YPD-molasses medium, the BC yield were highest in this study when the consuming rate of reducing sugars (RS) was 0.181 g-dry BC L-1 d-1 and the BC production rate was 0.183 g-dry BC/g-RS, respectively. The modified YPD-glucose medium could get the maximum pellicle thickness of 5.56±0.64 mm and water content of 99.4%, respectively.

Effect of carbon sources on physicochemical properties of bacterial cellulose produced from Gluconacetobacter xylinus MTCC 7795

e-Polymers ◽  
2016 ◽  
Vol 16 (4) ◽  
pp. 331-336
Author(s):  
Rushali Singh ◽  
Ashwani Mathur ◽  
Navendu Goswami ◽  
Garima Mathur
Keyword(s):  
Physicochemical Properties ◽  
Bacterial Cellulose ◽  
Specific Growth ◽  
Carbon Sources ◽  
Xrd Analysis ◽  
Culture Conditions ◽  
Shake Flask Culture ◽  
Gluconacetobacter Xylinus ◽  
X Ray ◽  
First Time

AbstractIn this study, the effect of modified Hestrin Schramm (HS) medium supplemented with different carbon sources viz., glucose, fructose, galactose and lactic acid on the yield and physicochemical properties of bacterial cellulose (BC) produced from Gluconacetobacter xylinus strain MTCC 7795 in shake flask culture conditions was investigated. Growth studies indicated that all carbon sources supported the growth of bacteria, though specific growth rate and doubling time differs. Fructose gave the highest cellulose yield of 7.72 mg/ml after 130 h of fermentation, while yield in glucose and galactose supplemented medium were 4.49 mg/ml and 3.38 mg/ml, respectively. X-ray powder diffraction (XRD) analysis revealed that all BC samples were amorphous in comparison to commercial cellulose. Fourier transform infrared (FTIR) spectroscopic investigations of bacterial cellulose (BC) samples affirm the purity of the cellulose produced. No significant variations in physicochemical properties of cellulose samples produced with different carbon sources were observed. This study for the first time has investigated the effect of carbon sources on physicochemical properties of bacterial cellulose produced by G. xylinus MTCC 7795 and provides a strategy for economical production of BC with anticipated application in therapeutics and tissue engineering.

The comparison of trichloroethylene removal rates by methane- and aromatic-utilizing microorganisms

1998 ◽  
Vol 38 (7) ◽  
pp. 19-24 ◽  
Author(s):  
C.-J. Lu ◽  
C. M. Lee ◽  
M.-S. Chung
Keyword(s):  
Cell Concentration ◽  
Removal Rate ◽  
Carbon Sources ◽  
Batch Reactors ◽  
Initial Cell Concentration ◽  
Removal Rates ◽  
Initial Cell ◽  
Toluene Concentration ◽  
Cell Source ◽  
Removal Efficiencies

The comparison of TCE cometabolic removal by methane, toluene, and phenol utilizers was conducted with a series of batch reactors. Methane, toluene, or phenol enriched microorganisms were used as cell source. The initial cell concentration was about 107 cfu/mL. Methane, toluene, and phenol could be readily biodegraded resulting in the cometabolic removal of TCE. Among the three primary carbon sources studied, the presence of phenol provided the best cometabolic removal of TCE. When the concentration of carbon source was 3 mg-C/L, the initial TCE removal rates initiated by methane, toluene, and phenol utilizers were 1.5, 30, and 100 μg/L-hr, respectively. During the incubation period of 80 hours, TCE removal efficiencies were 26% and 96% with the presence of methane and toluene, respectively. However, it was 100% within 20 hours with the presence of phenol. For phenol utilizers, the initial TCE removal rates were about the same, when the phenol concentrations were 1.35, 2.7, and 4.5 mg/L. However, TCE removal was not proportional to the concentrations of phenol. TCE removal was hindered when the phenol concentration was higher than 4.5 mg/L because of the rapid depletion of dissolved oxygen. The presence of toluene also initiated cometabolic removal of TCE. The presence of toluene at 3 and 5 mg/L resulted in similar TCE removal. The initial TCE removal rate was about 95 μg/L-hr at toluene concentrations of 3 and 5 mg/L compared to 20 μg/L-hr at toluene concentration of 1 mg/L.

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

scholarly journals Fast Two-Stage Computation of an Index Policy for Multi-Armed Bandits with Setup Delays

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
José Niño-Mora
Keyword(s):  
Numerical Study ◽  
Arithmetic Operation ◽  
Bandit Problem ◽  
Index Policy ◽  
Two Stage ◽  
Second Stage ◽  
Whittle Index ◽  
Set Up ◽  
Computing Method ◽  
Special Case

We consider the multi-armed bandit problem with penalties for switching that include setup delays and costs, extending the former results of the author for the special case with no switching delays. A priority index for projects with setup delays that characterizes, in part, optimal policies was introduced by Asawa and Teneketzis in 1996, yet without giving a means of computing it. We present a fast two-stage index computing method, which computes the continuation index (which applies when the project has been set up) in a first stage and certain extra quantities with cubic (arithmetic-operation) complexity in the number of project states and then computes the switching index (which applies when the project is not set up), in a second stage, with quadratic complexity. The approach is based on new methodological advances on restless bandit indexation, which are introduced and deployed herein, being motivated by the limitations of previous results, exploiting the fact that the aforementioned index is the Whittle index of the project in its restless reformulation. A numerical study demonstrates substantial runtime speed-ups of the new two-stage index algorithm versus a general one-stage Whittle index algorithm. The study further gives evidence that, in a multi-project setting, the index policy is consistently nearly optimal.

scholarly journals Multiple Sclerosis: Assessment of Disease Progression and Effects of Treatment

1987 ◽  
Vol 14 (S3) ◽  
pp. 518-520 ◽  
Author(s):  
D.W. Paty
Keyword(s):  
Multiple Sclerosis ◽  
Disease Progression ◽  
Treatment Effects ◽  
Two Stage ◽  
Second Stage ◽  
Sequential Development ◽  
Stage Disease

ABSTRACT:MS could well be a two stage disease. The first stage involves the sequential development of multiple small lesions, mostly inflammatory, that accumulate at a given rate. The second stage could be that of consolidation and confluence of lesions that involves not only demyelination but gliosis. MRI now gives us an opportunity to watch the evolution of these processes and also to monitor treatment effects. It is only after the evolution of this process is understood that we can design rational therapies directed toward the prevention of spasticity in MS.

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