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Processes, Volume 7, Issue 5 (May 2019)

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Cover Story (view full-size image) The sustainable production of clean and cheap energy is an unmet need of the world’s growing [...] Read more.
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Open AccessArticle
Comparative Study of the Performances of Al(OH)3 and BaSO4 in Ultrafine Powder Coatings
Processes 2019, 7(5), 316; https://doi.org/10.3390/pr7050316
Received: 7 May 2019 / Revised: 22 May 2019 / Accepted: 23 May 2019 / Published: 27 May 2019
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Abstract
Ultrafine powder coatings are one of the development directions in the powder coating industry, as they can achieve thin coatings with good leveling and high surface smoothness comparable to liquid coatings. Compared to regular coatings, they experience a higher sensitivity to any incompatibilities, [...] Read more.
Ultrafine powder coatings are one of the development directions in the powder coating industry, as they can achieve thin coatings with good leveling and high surface smoothness comparable to liquid coatings. Compared to regular coatings, they experience a higher sensitivity to any incompatibilities, e.g., filler from coating components. The properties of fillers play a great role in the performance of coating films. Aluminum trihydrate (Al(OH)3) is a well-known filler in solvent-based coatings and other polymer industries. To study and evaluate the performances of Al(OH)3 in ultrafine powder coatings, a popular filler, barium sulfate (BaSO4) is used for comparison. Both fillers are added in ultrafine powder coatings based on two of the most commonly used resin systems (polyester-epoxy and polyester). The differences of physical and chemical properties between both fillers have significant influences on several properties of powder paints and coating films. The polar groups (hydrogen bond) in Al(OH)3 result in the strong interaction between inorganic filler and organic polymer matrix, thus decreasing the molecular network mobility and influencing the chain formation, which is verified by differential scanning calorimetric (DSC). The bed expansion ratio (BERs) of powder paints incorporated with Al(OH)3 are much higher than those with BaSO4, which indicate more uniform gas-solid contact during the spraying process. Samples with Al(OH)3 exhibit much lower specular gloss at 60°, which are expected to achieve remarkable matting effects. Superior corrosion resistances can be observed for almost all the coated panels incorporated with Al(OH)3 in contrast to those with BaSO4. Other aspects are slightly influenced by the difference between the two fillers, such as the angle of repose values (AORs) of powder paints, the impact resistance and flexibility of coating films. Full article
(This article belongs to the Special Issue Thin Film Processes)
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Open AccessFeature PaperArticle
Hydroxymethylation-Modified Lignin and Its Effectiveness as a Filler in Rubber Composites
Processes 2019, 7(5), 315; https://doi.org/10.3390/pr7050315
Received: 26 March 2019 / Revised: 19 May 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
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Abstract
Kraft lignin was modified by using hydroxymethylation to enhance the compatibility between rubber based on a blend of natural rubber/polybutadiene rubber (NR/BR) and lignin. To confirm this modification, the resultant hydroxymethylated kraft lignin (HMKL) was characterized using Fourier transform infrared (FTIR) and nuclear [...] Read more.
Kraft lignin was modified by using hydroxymethylation to enhance the compatibility between rubber based on a blend of natural rubber/polybutadiene rubber (NR/BR) and lignin. To confirm this modification, the resultant hydroxymethylated kraft lignin (HMKL) was characterized using Fourier transform infrared (FTIR) and nuclear magnetic resonance (NMR) spectroscopy. It was then incorporated into rubber composites and compared with unmodified rubber. All rubber composites were investigated in terms of rheology, mechanical properties, aging, thermal properties, and morphology. The results show that the HMKL influenced the mechanical properties (tensile properties, hardness, and compression set) of NR/BR composites compared to unmodified lignin. Further evidence also revealed better dispersion and good interaction between the HMKL and the rubber matrix. Based on its performance in NR/BR composites, hydroxymethylated lignin can be used as a filler in the rubber industry. Full article
(This article belongs to the Special Issue Renewable Polymers: Processing and Chemical Modifications)
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Open AccessArticle
Wave Characteristics of Coagulation Bath in Dry-Jet Wet-Spinning Process for Polyacrylonitrile Fiber Production Using Computational Fluid Dynamics
Processes 2019, 7(5), 314; https://doi.org/10.3390/pr7050314
Received: 30 April 2019 / Revised: 21 May 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
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Abstract
In this work, a three-dimensional volume-of-fluid computational fluid dynamics (VOF-CFD) model was developed for a coagulation bath of the dry-jet wet spinning (DJWS) process for the production of polyacrylonitrile (PAN)-based carbon fiber under long-term operating conditions. The PAN-fiber was assumed to be a [...] Read more.
In this work, a three-dimensional volume-of-fluid computational fluid dynamics (VOF-CFD) model was developed for a coagulation bath of the dry-jet wet spinning (DJWS) process for the production of polyacrylonitrile (PAN)-based carbon fiber under long-term operating conditions. The PAN-fiber was assumed to be a deformable porous zone with variations in moving speed, porosity, and permeability. The Froude number, interpreted as the wave-making resistance on the liquid surface, was analyzed according to the PAN-fiber wind-up speed ( v P A N ). The effect of the PAN speed on the reflection and wake flow formed by drag between a moving object and fluid is presented. A method for tracking the wave amplitude with time is proposed based on the iso-surface of the liquid volume fraction of 0.95. The wave signal for 30 min was divided into the initial and resonance states that were distinguished at 8 min. The maximum wave amplitude was less than 0.5 mm around the PAN-fiber inlet nozzle for v P A N = 0.1–0.5 m/s in the resonance state. The VOF-CFD model is useful in determining the maximum v P A N under an allowable air gap of the DJWS process. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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Open AccessArticle
In Situ Measurements and CFD Numerical Simulations of Thermal Environment in Blind Headings of Underground Mines
Processes 2019, 7(5), 313; https://doi.org/10.3390/pr7050313
Received: 9 May 2019 / Accepted: 22 May 2019 / Published: 24 May 2019
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Abstract
In order to gain a knowledge of the heat emitted from a variety of sources at the blind heading of an underground gold mine, the present study conducts an in situ measurement study in a blind heading within the load haul dumps (LHDs) [...] Read more.
In order to gain a knowledge of the heat emitted from a variety of sources at the blind heading of an underground gold mine, the present study conducts an in situ measurement study in a blind heading within the load haul dumps (LHDs) that are operating. The measurements can provide a reliable data basis for the setting of numerical simulations. The results demonstrate that the distances between the forcing outlet and the mining face (denoted as Zm), as well as the heat generation from LHDs (denoted as QL), has brought significant impacts on the airflow velocity, relative humidity, and temperature distributions in the blind heading. Setting Zm to 5 m could achieve a relative optimal cooling performance, also indicating that when the LHD is fully operating in the mining face, employing the pure forcing system has a limited effect on the temperature decrease of the blind heading. According to the numerical simulations, a better cooling performance can be achieved based on the near-forcing-far-exhausting (NFFE) ventilation system. Full article
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Open AccessArticle
A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty
Processes 2019, 7(5), 312; https://doi.org/10.3390/pr7050312
Received: 4 April 2019 / Revised: 14 May 2019 / Accepted: 20 May 2019 / Published: 24 May 2019
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Abstract
Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning [...] Read more.
Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning a water resource management system (WRMS) that can handle uncertain information by using interval values, fuzzy sets, and scenario analysis. The SIFCP-WRMS model is then applied to plan the middle route of the South-to-North Water Diversion Project (SNWDP) in Henan Province, China. Solutions of different water distribution proportion scenarios and varied credibility levels are considered. Results reveal that different water-distribution proportion scenarios and uncertainties used in the SIFCP-WRMS model can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions, and system benefits. Results also indicate that the variation of scenarios (i.e., from S2 to S3) can result in a change of 9% over the planning horizon for water allocation in the industrial sector. Findings can help decision-makers resolve conflicts among economic objective, water resource demand, and sewage discharge, as well as COD emissions. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessFeature PaperReview
Aeration Process in Bioreactors as the Main Energy Consumer in a Wastewater Treatment Plant. Review of Solutions and Methods of Process Optimization
Processes 2019, 7(5), 311; https://doi.org/10.3390/pr7050311
Received: 31 March 2019 / Revised: 10 May 2019 / Accepted: 16 May 2019 / Published: 24 May 2019
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Abstract
Due to the key role of the biological decomposition process of organic compounds in wastewater treatment, a very important thing is appropriate aeration of activated sludge, because microorganisms have to be supplied with an appropriate amount of oxygen. Aeration is one of the [...] Read more.
Due to the key role of the biological decomposition process of organic compounds in wastewater treatment, a very important thing is appropriate aeration of activated sludge, because microorganisms have to be supplied with an appropriate amount of oxygen. Aeration is one of the most energy-consuming processes in the conventional activated sludge systems of wastewater treatment technology (may consume from 50% to 90% of electricity used by a plant), which makes it the most cost-generating process incurred by treatment plants. The paper presents the construction of aeration systems, their classification as well as parameters and factors that significantly affect the aeration process e.g., oxygen transfer efficiency, diffuser fouling, methods of dealing with diffuser fouling, diffuser selection. Additionally, there are briefly presented “smart control” systems in wastewater treatment and effect of application control strategy based on Supervisory Control and Data Acquisition system connected with the decrease in the energy consumption for aeration of bioreactors with activated sludge. It is noted that before the process is optimized, the system should be equipped with suitable metering devices. Only when relevant data is available, the improvements can be carried out. However, it’s important, that the operator should regularly maintain good condition and high efficiency of diffusers. Full article
(This article belongs to the Special Issue Wastewater Treatment Processes)
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Open AccessArticle
Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers
Processes 2019, 7(5), 310; https://doi.org/10.3390/pr7050310
Received: 5 May 2019 / Revised: 17 May 2019 / Accepted: 20 May 2019 / Published: 23 May 2019
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Abstract
An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of [...] Read more.
An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of medium industrial consumers, therefore assuring an intelligent energy management and an efficient economic scheduling of their resources, having the possibility to negotiate in advance appropriate billing tariffs relying on accurate hourly forecasts, in the same time facilitating an optimal energy management for the dispatch operator. The forecasting method consists of developing first non-linear autoregressive, with exogenous inputs (NARX) artificial neural networks (ANNs) in order to forecast an initial daily electricity consumption, a forecast that is being further processed with custom developed long short-term memory (LSTM) neural networks with exogenous variables support in order to refine the daily forecast as to achieve an accurate hourly forecasted consumed electricity for the whole month-ahead. The obtained experimental results (highlighted also through a very good value of 0.0244 for the root mean square error performance metric, obtained when forecasting the month-ahead hourly electricity consumption and comparing it with the real consumption), the validation of the developed forecasting method, the comparison of the method with other forecasting approaches from the scientific literature substantiate the fact that the proposed approach manages to fill a gap in the current body of knowledge consisting of the need of a high-accuracy forecasting method for the month-ahead hourly electricity consumption in the case of medium industrial consumers. The developed forecasting method targets medium industrial consumers, but, due to its accuracy, it can also be a useful tool for promoting innovative business models with regard to industrial consumers willing to produce a part of their own electricity using renewable energy resources, benefiting from reduced production costs and reliable electricity prices. Full article
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems)
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Open AccessFeature PaperArticle
Data-Driven Estimation of Significant Kinetic Parameters Applied to the Synthesis of Polyolefins
Processes 2019, 7(5), 309; https://doi.org/10.3390/pr7050309
Received: 23 April 2019 / Revised: 14 May 2019 / Accepted: 16 May 2019 / Published: 22 May 2019
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Abstract
A data-driven strategy for the online estimation of important kinetic parameters was assessed for the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst at different diene concentrations and reaction temperatures. An initial global sensitivity analysis selected the significant kinetic parameters of the [...] Read more.
A data-driven strategy for the online estimation of important kinetic parameters was assessed for the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst at different diene concentrations and reaction temperatures. An initial global sensitivity analysis selected the significant kinetic parameters of the system. The retrospective cost model refinement (RCMR) algorithm was adapted and implemented to estimate the significant kinetic parameters of the model in real time. After verifying stability and robustness, experimental data validated the algorithm performance. Results demonstrate the estimated kinetic parameters converge close to theoretical values without requiring prior knowledge of the polymerization model and the original kinetic values. Full article
(This article belongs to the Special Issue Computational Methods for Polymers)
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Open AccessArticle
Off-Grid Solar PV Power Generation System in Sindh, Pakistan: A Techno-Economic Feasibility Analysis
Processes 2019, 7(5), 308; https://doi.org/10.3390/pr7050308
Received: 6 April 2019 / Revised: 7 May 2019 / Accepted: 17 May 2019 / Published: 22 May 2019
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Abstract
The off-grid solar photovoltaic (PV) system is a significant step towards electrification in the remote rural regions, and it is the most convenient and easy to install technology. However, the strategic problem is in identifying the potential of solar energy and the economic [...] Read more.
The off-grid solar photovoltaic (PV) system is a significant step towards electrification in the remote rural regions, and it is the most convenient and easy to install technology. However, the strategic problem is in identifying the potential of solar energy and the economic viability in particular regions. This study, therefore, addresses this problem by evaluating the solar energy potential and economic viability for the remote rural regions of the Sindh province, Pakistan. The results recommended that the rural regions of Sindh have suitable solar irradiance to generate electricity. An appropriate tilt angle has been computed for the selected rural regions, which significantly enhances the generation capacity of solar energy. Moreover, economic viability has been undertaken in this study and it was revealed that the off-grid solar PV power generation system provides electricity at the cost of Pakistani Rupees (PKR) 6.87/kWh and is regarded as much cheaper than conventional energy sources, i.e., around PKR 20.79/kWh. Besides, the off-grid solar PV power generation system could mitigate maximum CO2 annually on the condition that all of the selected remote rural regions adopt the off-grid solar PV system. Therefore, this study shall help the government to utilize the off-grid solar PV power generation system in the remote rural regions of Pakistan. Full article
(This article belongs to the Section Green Processes)
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Open AccessArticle
Risk Rating Method Based on the Severity Probability Risk Value and Reserved Risk Maintenance Resource Cost of the Node Disconnection of the Power System
Processes 2019, 7(5), 307; https://doi.org/10.3390/pr7050307
Received: 11 April 2019 / Revised: 13 May 2019 / Accepted: 17 May 2019 / Published: 22 May 2019
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Abstract
In order to solve the problem of traditional risk rating methods without considering the cost of risk maintenance resources and ignoring the low risk of “High Loss Severity (HLS) with low probability” and the low risk of “High Failure Probability (HFP) with low [...] Read more.
In order to solve the problem of traditional risk rating methods without considering the cost of risk maintenance resources and ignoring the low risk of “High Loss Severity (HLS) with low probability” and the low risk of “High Failure Probability (HFP) with low loss severity”, a node disconnection risk rating method (NDRRM) is proposed. This method considers the severity probability risk valuation (SPRV) and reserve risk maintenance resource cost (RRMRC). The risk rating method based on SPRV developed from the traditional risk valuation method can simultaneously identify the nodes with the highest severity values, the nodes with the highest probability of failure, and the nodes with the largest risk valuation. On the basis of the above model, we consider the cost constraints of the reserve risk maintenance resource and put forward a risk rating method based on SPRV and RRMRC. The risk rating results of this model are suitable for guiding risk maintenance in practice. Simulations are carried out on the modified IEEE RTS-79 system to illustrate the effectiveness of the proposed models, and the simulation results show that the model is reasonable and effective. Full article
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems)
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Open AccessArticle
Comparison of Riser-Simplified, Riser-Only, and Full-Loop Simulations for a Circulating Fluidized Bed
Processes 2019, 7(5), 306; https://doi.org/10.3390/pr7050306
Received: 31 March 2019 / Revised: 6 May 2019 / Accepted: 20 May 2019 / Published: 22 May 2019
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Abstract
With the development of computing power, the simulation of circulating fluidized bed (CFB) has developed from riser-simplified simulation to riser-only simulation, then to full-loop simulation. This paper compared these three methods based on pilot-scale CFB experiment data to find the scope of application [...] Read more.
With the development of computing power, the simulation of circulating fluidized bed (CFB) has developed from riser-simplified simulation to riser-only simulation, then to full-loop simulation. This paper compared these three methods based on pilot-scale CFB experiment data to find the scope of application of each method. All these simulations, using the Eulerian–Eulerian two-fluid model with the kinetic theory of granular theory, were conducted to simulate a pilot-scale CFB. The hydrodynamics, such as pressure balance, solids holdup distribution, solids velocity distribution, and instantaneous mass flow rates in the riser or CFB system, were investigated in different simulations. By comparing the results from different methods, it was found that riser-simplified simulation is not sufficient to obtain accurate hydrodynamics, especially in higher solids circulating rates. The riser-only simulation is able to make a reasonable prediction of time-averaged behaviors of gas–solids in most parts of riser but the entrance region. Further, the full-loop simulation can not only predict precise results, but also obtain comprehensive details and instantaneous information in the CFB system. Full article
(This article belongs to the Special Issue Multiphase Reaction Engineering, Reactors and Processes)
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Open AccessArticle
Process Optimization by a Response Surface Methodology for Adsorption of Congo Red Dye onto Exfoliated Graphite-Decorated MnFe2O4 Nanocomposite: The Pivotal Role of Surface Chemistry
Processes 2019, 7(5), 305; https://doi.org/10.3390/pr7050305
Received: 20 March 2019 / Revised: 21 April 2019 / Accepted: 14 May 2019 / Published: 21 May 2019
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Abstract
Natural graphite, a locally available, eco-friendly, and low-cost carbonaceous source, can be easily transformed into exfoliated graphite (EG) with many surface functional groups via a chemical oxidation route. Combination between EG and magnetic MnFe2O4 is a promising strategy to create [...] Read more.
Natural graphite, a locally available, eco-friendly, and low-cost carbonaceous source, can be easily transformed into exfoliated graphite (EG) with many surface functional groups via a chemical oxidation route. Combination between EG and magnetic MnFe2O4 is a promising strategy to create a hybrid kind of nanocomposite ([email protected]2O4) for the efficient adsorptive removal of Congo red (CR) dye from water. Here, we reported the facile synthesis and characterization of chemical bonds of [email protected]2O4 using several techniques such as Fourier-transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS). In particular, the quantity method by Boehm titration was employed to identify the content of functional groups: Carboxylic acid (0.044 mmol/g), phenol (0.032 mmol/g), lactone (0.020 mmol/g), and total base (0.0156 mmol/g) on the surface of [email protected]2O4. Through the response surface methodology-optimized models, we found a clear difference in the adsorption capacity between EG-decorated MnFe2O4 (62.0 mg/g) and MnFe2O4 without EG decoration (11.1 mg/g). This result was also interpreted via a proposed mechanism to elucidate the contribution of surface functional groups of [email protected]2O4 to adsorption efficiency towards CR dye. Full article
(This article belongs to the Section Materials Processes)
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Open AccessArticle
Determination of the Acidity of Waste Cooking Oils by Near Infrared Spectroscopy
Processes 2019, 7(5), 304; https://doi.org/10.3390/pr7050304
Received: 29 April 2019 / Revised: 15 May 2019 / Accepted: 15 May 2019 / Published: 21 May 2019
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Abstract
Waste cooking oils (WCO) recycling companies usually have economic losses for buying WCO not suitable for biodiesel production, e.g., WCO with high free acidity (FA). For this reason, the determination of FA of WCO by near infrared (NIR) spectroscopy was studied in this [...] Read more.
Waste cooking oils (WCO) recycling companies usually have economic losses for buying WCO not suitable for biodiesel production, e.g., WCO with high free acidity (FA). For this reason, the determination of FA of WCO by near infrared (NIR) spectroscopy was studied in this work to assess its potential for in situ application. To do this, FA of 45 WCO was measured by the classical titration method, which ranged between 0.15 and 3.77%. Then, the NIR spectra from 800 to 2200 nm of these WCO were acquired, and a partial least squares model was built, relating the NIR spectra to FA values. The accuracy of the model was quite high, providing r2 of 0.970 and a ratio of performance to deviation (RPD) of 4.05. Subsequently, a model using an NIR range similar to that provided by portable NIR spectrometers (950–1650 nm) was built. The performance was lower (r2 = 0.905; RPD = 2.66), but even so, with good accuracy, which demonstrates the potential of NIR spectroscopy for the in situ determination of FA of WCO. Full article
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Open AccessArticle
Optimization of Macroalgal Density and Salinity for Nutrient Removal by Caulerpa lentillifera from Aquaculture Effluent
Processes 2019, 7(5), 303; https://doi.org/10.3390/pr7050303
Received: 11 March 2019 / Revised: 5 May 2019 / Accepted: 5 May 2019 / Published: 21 May 2019
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Abstract
Determining the optimum levels of macroalgal density and salinity for removing aquaculture effluent has gained increasing research interest in recent years because of the growing concerns over environmental sustainability. Here, we determined the effects of macroalgal density and salinity on the uptake of [...] Read more.
Determining the optimum levels of macroalgal density and salinity for removing aquaculture effluent has gained increasing research interest in recent years because of the growing concerns over environmental sustainability. Here, we determined the effects of macroalgal density and salinity on the uptake of NO2, NO3, NH3, and PO43− by Caulerpa lentillifera from the effluent of Poecilia latipinna using spectrophotometry. Laboratory experiments were conducted to measure nutrient uptake at five different macroalgal density levels (10, 20, 30, 40, and 50 g/L) and three salinity levels (20, 30, and 40 ppt) with and without aeration. Quadratic regression analysis revealed significant nonlinear and linear effects of macroalgal density on the uptake of NO2, NO3, NH3, and PO43−, where the maximum uptake was predicted to occur at the macroalgal densities of 31.6, 33.3, 50.0, and 20.0 g/L, respectively. Likewise, the effects of salinity on the uptake of NO2, NO3, NH3, and PO43− were significant and nonlinear where the maximum uptake was predicted to occur at the salinity levels of 29.1, 30.7, 29.5, and 29.5 ppt, respectively. The result of the effects of aeration was mixed but somewhat indicated a positive effect on the nutrient uptake within the 24 h period. Our results could help aquaculturists to minimize the excessive nutrients by C. lentillifera from aquaculture effluent while achieving long-term sustainable aquaculture production. Full article
(This article belongs to the Special Issue Wastewater Treatment Processes)
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Open AccessArticle
An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer
Processes 2019, 7(5), 302; https://doi.org/10.3390/pr7050302
Received: 3 April 2019 / Revised: 14 May 2019 / Accepted: 15 May 2019 / Published: 21 May 2019
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Abstract
Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop [...] Read more.
Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods. Full article
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Open AccessFeature PaperArticle
Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles
Processes 2019, 7(5), 301; https://doi.org/10.3390/pr7050301
Received: 16 April 2019 / Revised: 12 May 2019 / Accepted: 15 May 2019 / Published: 21 May 2019
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Abstract
Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using [...] Read more.
Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests. Full article
(This article belongs to the Special Issue Modeling & Control of Disease States)
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Open AccessArticle
Highly Porous Graphitic Activated Carbons from Lignite via Microwave Pretreatment and Iron-Catalyzed Graphitization at Low-Temperature for Supercapacitor Electrode Materials
Processes 2019, 7(5), 300; https://doi.org/10.3390/pr7050300
Received: 17 April 2019 / Revised: 12 May 2019 / Accepted: 16 May 2019 / Published: 21 May 2019
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Abstract
At present, the preparation of highly porous graphitic activated carbons (HPGACs) using the usual physical and chemical activation methods has met a bottleneck. In this study, HPGACs are directly synthesized from lignite at 900 °C. The whole process is completed by a microwave [...] Read more.
At present, the preparation of highly porous graphitic activated carbons (HPGACs) using the usual physical and chemical activation methods has met a bottleneck. In this study, HPGACs are directly synthesized from lignite at 900 °C. The whole process is completed by a microwave pretreatment, a graphitization conversion of the carbon framework at a low temperature using a small amount of FeCl3 (10–30 wt%), and a subsequent physical activation using CO2. Consequently, the dispersed and mobile iron species, in the absence of oxygen functional groups (removed during the microwave pretreatment), can greatly promote catalytic graphitization during pyrolysis, and, as an activating catalyst, can further facilitate the porosity development during activation. The as-obtained AC-2FeHLH-5-41.4(H) presents a low defect density, high purity, and specific surface area of 1852.43 m2 g−1, which is far greater than the AC-HLH-5-55.6(H) obtained solely by physical activation. AC-2FeHLH-5-41.4(H) as a supercapacitor electrode presents an excellent performance in the further electrochemical measurements. Such a convenient and practical method with low cost proves a scalable method to prepare HPGACs from a wide range of coal/biomass materials for industrial scale-up and applications. Full article
(This article belongs to the Special Issue Sustainable Catalytic Processes Driven by Functional Nanomaterials)
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Open AccessArticle
Influence of Injection Timing on Performance and Exhaust Emission of CI Engine Fuelled with Butanol-Diesel Using a 1D GT-Power Model
Processes 2019, 7(5), 299; https://doi.org/10.3390/pr7050299
Received: 23 April 2019 / Revised: 13 May 2019 / Accepted: 14 May 2019 / Published: 21 May 2019
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Abstract
Injection timing variations have a significant effect on the performance and pollutant formation in diesel engines. Numerical study was conducted to investigate the impact of injection timing on engine performance and pollutants in a six-cylinder turbocharged diesel engine. Diesel fuel with different amounts [...] Read more.
Injection timing variations have a significant effect on the performance and pollutant formation in diesel engines. Numerical study was conducted to investigate the impact of injection timing on engine performance and pollutants in a six-cylinder turbocharged diesel engine. Diesel fuel with different amounts (5%, 15%, and 25% by volume) of n-butanol was used. Simulations were performed at four distinct injection timings (5°, 10°, 20°, 25°CA bTDC) and two distinct loads of brake mean effective pressure (BMEP = 4.5 bar and 10.5 bar) at constant engine speed (1800 rpm) using the GT-Power computational simulation package. The primary objective of this research is to determine the optimum injection timing and optimum blending ratio for improved efficiencies and reduced emissions. Notable improvements in engine performance and pollutant trends were observed for butanol-diesel blends. The addition of butanol to diesel fuel has greatly diminished NOX and CO pollutants but it elevated HC and CO2 emissions. Retarded injection timing decreased NOX and CO2 pollutants while HC and CO2 emissions increased. The results also indicated that early injection timings (20°CA bTDC and 25°CA bTDC) lowered both CO2 and unburned hydrocarbon emissions. Moreover, advanced injection timing slightly improved brake thermal efficiency (BTE) for all engine loads. It is concluded that retarded injection timing, i.e., 10°CA bTDC demonstrated optimum results in terms of performance, combustion and emissions and among the fuels 15B showed good outcome with regard to BTE, higher heat release rate, and lower pollution of HC, CO, and NOx. Full article
(This article belongs to the Section Computational Methods)
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Open AccessFeature PaperArticle
Distinct and Quantitative Validation Method for Predictive Process Modeling with Examples of Liquid-Liquid Extraction Processes of Complex Feed Mixtures
Processes 2019, 7(5), 298; https://doi.org/10.3390/pr7050298
Received: 18 March 2019 / Revised: 4 May 2019 / Accepted: 13 May 2019 / Published: 19 May 2019
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Abstract
As of today, industrial process development for liquid-liquid extraction and scale-up of extraction columns is based on an experimental procedure that requires tests in pilot-scale. This methodology consumes large amounts of material and time and the utilized scale-up equations are crude estimates including [...] Read more.
As of today, industrial process development for liquid-liquid extraction and scale-up of extraction columns is based on an experimental procedure that requires tests in pilot-scale. This methodology consumes large amounts of material and time and the utilized scale-up equations are crude estimates including considerable safety margins. This approach is practical for well-known systems or low-value products coupled with high production scale, where such a scale-up methodology has less impact on the overall profitability. However, for new high-value products in biologics manufacturing, a process development based on process understanding and the use of validated process models is imperative. Therefore, a distinct and quantitative validation workflow for liquid-liquid extraction modeling is presented on the example of two complex feed mixtures. Monte-Carlo simulations based on the presented model parameter determination concept result for both examples in prediction accuracy comparable to the experiments and prediction precision within the deviation of the respective experiments. Identification of statistically significant parameters is demonstrated. The presented methodology for model validation will support the implementation of liquid-liquid extraction in the manufacturing of new high value biological products in regulated industries by providing a workflow to derive a Quality-by-Design compatible process model. Full article
(This article belongs to the Special Issue Processes Accelerating Biologics Manufacturing by Modelling)
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Open AccessFeature PaperArticle
Injectable Chitosan Scaffolds with Calcium β-Glycerophosphate as the Only Neutralizing Agent
Processes 2019, 7(5), 297; https://doi.org/10.3390/pr7050297
Received: 11 March 2019 / Revised: 21 April 2019 / Accepted: 16 May 2019 / Published: 19 May 2019
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Abstract
The presented work describes the method of preparation of thermosensitive chitosan hydrogels using calcium β-glycerophosphate salt as the only pH neutralizing agent and supporting the crosslinking process. The presence of calcium ions instead of sodium ions is particularly important in the case of [...] Read more.
The presented work describes the method of preparation of thermosensitive chitosan hydrogels using calcium β-glycerophosphate salt as the only pH neutralizing agent and supporting the crosslinking process. The presence of calcium ions instead of sodium ions is particularly important in the case of scaffolds in bone tissue engineering. Rheological and physicochemical properties of low concentrated chitosan solutions with the addition of calcium β-glycerophosphate were investigated using rotational rheometry techniques, Zeta potential (by electrophoresis), XPS, and SEM analysis together with an EDS detector. It was found to be possible to prepare colloidal solutions of chitosan containing only calcium β-glycerophosphate (without sodium ions) undergoing a sol-gel phase transition at the physiological temperature of the human body. It has also been shown that it is possible to further enrich the obtained cellular scaffolds with calcium ions. Using the addition of calcium carbonate, hydrogels with a physiological ratio of calcium to phosphorus (1.6–1.8):1 were obtained. Full article
(This article belongs to the Special Issue Synthesis and Characterization of Biomedical Materials)
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Open AccessArticle
Multi-Objective Optimal Scheduling Method for a Grid-Connected Redundant Residential Microgrid
Processes 2019, 7(5), 296; https://doi.org/10.3390/pr7050296
Received: 1 April 2019 / Revised: 28 April 2019 / Accepted: 15 May 2019 / Published: 19 May 2019
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Abstract
Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system [...] Read more.
Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system of the RR-microgrid is treated as a virtual energy storage system (VESS). An optimization model for grid-connected RR-microgrid scheduling is established based on mixed-integer nonlinear programming (MINLP), which takes the operating cost (OC), thermal comfort level (TCL), and pollution emission (PE) as the optimization objectives. The non-dominate sorting genetic algorithm II (NSGA-II) is employed to search the Pareto front and the best scheduling scheme is determined by the analytic hierarchy process (AHP) method. In a case study, two kinds of heating/cooling systems, the radiant floor heating/cooling system (RFHCS) and the convection heating/cooling system (CHCS) are investigated for the RR-microgrid. respectively, and the feasibility and validity of the scheduling method are ascertained. Full article
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Open AccessArticle
Verifying the Representativeness of Water-Quality Monitoring to Manage Water Levels in the Wicheon River, South Korea
Processes 2019, 7(5), 295; https://doi.org/10.3390/pr7050295
Received: 4 April 2019 / Revised: 2 May 2019 / Accepted: 13 May 2019 / Published: 17 May 2019
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Abstract
Changes in water level between the mainstems and tributaries of a river can create backflow effects that alter the representativeness of water-quality monitoring data. In South Korea, 16 multi-functional weirs intended to manage water levels were constructed on 4 major rivers as part [...] Read more.
Changes in water level between the mainstems and tributaries of a river can create backflow effects that alter the representativeness of water-quality monitoring data. In South Korea, 16 multi-functional weirs intended to manage water levels were constructed on 4 major rivers as part of a restoration project. However, they are causing backwater effects in tributaries that coincide with poorer water-quality measurements at monitoring stations along these tributaries despite there being no change in upstream pollution sources. Therefore, this study developed a new methodology for verifying the representativeness of a water-quality monitoring network via spatiotemporal observations of electrical conductivity, self-organizing maps for monthly pattern analysis, locally weighted scatter plot smoothing for trend analysis, load duration curves, and numerical modeling. This approach was tested on the Wicheon River, a primary tributary of the Nakdong River, because the measured decline in water quality there has the potential to trigger major policy changes in the basin including limits on local development. The results clearly show that the monitoring station in the lower Wicheon is negatively affected by weir-derived backwater from the Nakdong, suggesting that this station needs to be moved upstream or a new station established upstream, beyond the backwater effects. Our approach was able to assess clearly the representativeness of an existing water-quality monitoring network using widely accessible data and methods, making this type of assessment applicable to many other situations around the world. Full article
(This article belongs to the Special Issue Water Quality Modelling)
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Open AccessArticle
Parametric Methodology to Optimize the Sizing of Solar Collector Fields in Series-Parallel Arrays
Processes 2019, 7(5), 294; https://doi.org/10.3390/pr7050294
Received: 5 April 2019 / Revised: 12 May 2019 / Accepted: 14 May 2019 / Published: 17 May 2019
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Abstract
The analysis of solar thermal systems through numerical simulation is of great importance, since it allows predicting the performance of many configurations in any location and under different climatic conditions. Most of the simulation tools are commercial and require different degrees of training; [...] Read more.
The analysis of solar thermal systems through numerical simulation is of great importance, since it allows predicting the performance of many configurations in any location and under different climatic conditions. Most of the simulation tools are commercial and require different degrees of training; therefore, it is important to develop simple and reliable methodologies to obtain similar results. This study presents a parametric methodology to size stationary solar collector fields, with operating temperatures up to 150 °C. The costs of the collector loop piping and the pumping power of different series–parallel arrays is considered. The proposed tool was validated with experimental data and through simulations using commercial software. The tool allows establishing series–parallel arrays and calculates the volume of the storage tank according to the thermal load. The calculation is based on the system energy balance, where the mass flow and the heat losses in the interconnections of the collectors are taken into account. The number of collectors and the optimal series–parallel array were determined. The results show deviations lower than 7% in the relative error of the temperature profiles and in the solar fraction, with respect to the results obtained by dynamic simulations. Full article
(This article belongs to the Special Issue Design and Control of Sustainable Systems)
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Open AccessArticle
Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods
Processes 2019, 7(5), 293; https://doi.org/10.3390/pr7050293
Received: 27 March 2019 / Revised: 28 April 2019 / Accepted: 6 May 2019 / Published: 17 May 2019
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Abstract
It is difficult to manually process and analyze large amounts of data. Therefore, to solve a given problem, it is easier to reach the solution by studying the data obtained from the environment of the problem with computational intelligence methods. In this study, [...] Read more.
It is difficult to manually process and analyze large amounts of data. Therefore, to solve a given problem, it is easier to reach the solution by studying the data obtained from the environment of the problem with computational intelligence methods. In this study, pool boiling heat flux was estimated in the isolated bubble regime using two optimization methods (genetic and artificial bee colony algorithm) and three machine learning algorithms (decision tree, artificial neural network, and support vector machine). Six boiling mechanisms containing eighteen different parameters in the genetic and the artificial bee colony (ABC) algorithms were used to calculate overall heat flux of the isolated bubble regime. Support vector machine regression (SVMReg), alternating model tree (ADTree), and multilayer perceptron (MLP) regression only used the heat transfer equation input parameters without heat transfer equations for prediction of pool boiling heat transfer over a horizontal tube. The performance of computational intelligence methods were determined according to the results of error analysis. Mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) error were used to calculate the validity of the predictive model in genetic algorithm, ABC algorithm, SVMReg, MLP regression, and alternating model tree. According to the MAPE error analysis, the accuracy values of MLP regression (0.23) and alternating model tree (0.22) methods were the same. The SVMReg method used for pool boiling heat flux estimation performed better than the other methods, with 0.17 validation error rate of MAPE. Full article
(This article belongs to the Special Issue Optimization of Heat and Mass Exchange)
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Open AccessArticle
Internal R&D and Acquisition Performance of Chinese Pharmaceutical Firms: Moderation Effect of Acquisition Motive and Corporate Ownership
Processes 2019, 7(5), 292; https://doi.org/10.3390/pr7050292
Received: 7 March 2019 / Revised: 2 May 2019 / Accepted: 13 May 2019 / Published: 16 May 2019
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Abstract
Although corporate capability has been recognized as a key factor affecting corporate acquisition performance, the role of R&D capability in acquisition performance has not been fully explained. The aim of this paper was to research the impact of internal R&D on acquisition performance [...] Read more.
Although corporate capability has been recognized as a key factor affecting corporate acquisition performance, the role of R&D capability in acquisition performance has not been fully explained. The aim of this paper was to research the impact of internal R&D on acquisition performance according to a sample of 215 acquisitions of Chinese listed pharmaceutical companies from 2012 to 2016. First, it was found that R&D has a significant negative effect on acquisition performance. Furthermore, it was confirmed that the acquisition motive and the ownership of the acquiring firm have a moderating effect on the relationship between R&D and acquisition performance. Compared to non-technical acquisitions, the negative effect of internal R&D on acquisition performance was reduced for technical acquisitions. Compared with non-state-owned-enterprise acquisition, the negative effect of internal R&D on the acquisition performance of state-owned enterprises was weakened. Our study enriches the research of the path dependence theory on the acquisition performance of enterprises and also the interpretation of acquisition performance on the basis of internal and external innovation and the institutional theory. Full article
(This article belongs to the Section Other Topics)
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Open AccessArticle
A Newly Designed EGFP-2A Peptide Monocistronic Baculoviral Vector for Concatenating the Expression of Recombinant Proteins in Insect Cells
Processes 2019, 7(5), 291; https://doi.org/10.3390/pr7050291
Received: 29 March 2019 / Revised: 22 April 2019 / Accepted: 23 April 2019 / Published: 15 May 2019
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Abstract
Recombinant proteins produced by the baculovirus expression vector system (BVES) have been widely applied in the agricultural and medical fields. However, the procedure for protein expression is inefficient and needs to be improved. Herein, we propose a simple construct that incorporates a selectable [...] Read more.
Recombinant proteins produced by the baculovirus expression vector system (BVES) have been widely applied in the agricultural and medical fields. However, the procedure for protein expression is inefficient and needs to be improved. Herein, we propose a simple construct that incorporates a selectable marker (enhanced green fluorescent protein, EGFP) and a picorna viral-derived “self-cleaving” 2A-like peptide to separate the EGFP and target proteins in a monocistronic baculovirus vector to facilitate isolation of the recombinant baculovirus in the BVES. In this study, porcine adiponectin (ADN), a secreted, multimeric protein with insulin-sensitizing properties, was used to demonstrate its utility in our EGFP-2A-based expression system. EGFP and ADN were simultaneously expressed by a recombinant alphabaculovirus. Co-expression of EGFP facilitates the manipulation of the following processes, such as determining expression kinetics and harvesting ADN. The results showed that the 2A “self-cleaving” process does not interfere with EGFP activity or with signal peptide removal and the secretion of recombinant ADN. Posttranslational modifications, including glycosylation, of the recombinant ADN occurred in insect cells, and the formation of various multimers was further verified. Most importantly, the insect-produced ADN showed a similar bioactivity to that of mammalian cells. This concept provides a practical and economic approach that utilizes a new combination of alphabaculovirus/insect cell expression systems for future applications. Full article
(This article belongs to the Special Issue Transient Gene Expression for Rapid Protein and Virus-Vector Supply)
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Open AccessFeature PaperArticle
Using a Microfluidics System to Reproducibly Synthesize Protein Nanoparticles: Factors Contributing to Size, Homogeneity, and Stability
Processes 2019, 7(5), 290; https://doi.org/10.3390/pr7050290
Received: 16 April 2019 / Revised: 8 May 2019 / Accepted: 9 May 2019 / Published: 15 May 2019
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Abstract
The synthesis of Zein nanoparticles (NPs) using conventional methods, such as emulsion solvent diffusion and emulsion solvent evaporation, is often unreliable in replicating particle size and polydispersity between batch-to-batch syntheses. We have systematically examined the parameters for reproducibly synthesizing Zein NPs using a [...] Read more.
The synthesis of Zein nanoparticles (NPs) using conventional methods, such as emulsion solvent diffusion and emulsion solvent evaporation, is often unreliable in replicating particle size and polydispersity between batch-to-batch syntheses. We have systematically examined the parameters for reproducibly synthesizing Zein NPs using a Y-junction microfluidics chip with staggered herringbone micromixers. Our results indicate that the total flow rate of the fluidics system, relative flow rate of the aqueous and organic phase, concentration of the base material and solvent, and properties of the solvent influence the polydispersity and size of the NPs. Trends such as increasing the total flow rate and relative flow rate lead to a decrease in Zein NP size, while increasing the ethanol and Zein concentration lead to an increase in Zein NP size. The solvent property that was found to impact the size of the Zein NPs formed the most was their hydropathy. Solvents that had a hydropathy index most similar to that of Zein formed the smallest Zein NPs. Synthesis consistency was confirmed within and between sample batches. Stabilizing agents, such as sodium caseinate, Tween 80, and Pluronic F-68, were incorporated using the microfluidics system, necessary for in vitro and in vivo use, into Zein-based NPs. Full article
(This article belongs to the Special Issue Biomaterials and Tissue Engineering)
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Open AccessArticle
An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria
Processes 2019, 7(5), 289; https://doi.org/10.3390/pr7050289
Received: 9 April 2019 / Revised: 3 May 2019 / Accepted: 10 May 2019 / Published: 15 May 2019
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Abstract
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers [...] Read more.
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, gender, glucose level, and body mass are fundamental reasons for diabetes, followed by work stress, diet, family diabetes history, physical exercise, and cardiovascular stroke history. The study validated that the proposed set of DR is practical for quick screening of diabetes mellitus patients at the initial stage without intrusive medical tests and was found to be effective in the initial diagnosis of diabetes. Full article
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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Open AccessFeature PaperArticle
Mapping Tyrosine Kinase Receptor Dimerization to Receptor Expression and Ligand Affinities
Processes 2019, 7(5), 288; https://doi.org/10.3390/pr7050288
Received: 17 April 2019 / Revised: 7 May 2019 / Accepted: 8 May 2019 / Published: 15 May 2019
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Abstract
Tyrosine kinase receptor (RTK) ligation and dimerization is a key mechanism for translating external cell stimuli into internal signaling events. This process is critical to several key cell and physiological processes, such as in angiogenesis and embryogenesis, among others. While modulating RTK activation [...] Read more.
Tyrosine kinase receptor (RTK) ligation and dimerization is a key mechanism for translating external cell stimuli into internal signaling events. This process is critical to several key cell and physiological processes, such as in angiogenesis and embryogenesis, among others. While modulating RTK activation is a promising therapeutic target, RTK signaling axes have been shown to involve complicated interactions between ligands and receptors both within and across different protein families. In angiogenesis, for example, several signaling protein families, including vascular endothelial growth factors and platelet-derived growth factors, exhibit significant cross-family interactions that can influence pathway activation. Computational approaches can provide key insight to detangle these signaling pathways but have been limited by the sparse knowledge of these cross-family interactions. Here, we present a framework for studying known and potential non-canonical interactions. We constructed generalized models of RTK ligation and dimerization for systems of two, three and four receptor types and different degrees of cross-family ligation. Across each model, we developed parameter-space maps that fully determine relative pathway activation for any set of ligand-receptor binding constants, ligand concentrations and receptor concentrations. Therefore, our generalized models serve as a powerful reference tool for predicting not only known ligand: Receptor axes but also how unknown interactions could alter signaling dimerization patterns. Accordingly, it will drive the exploration of cross-family interactions and help guide therapeutic developments across processes like cancer and cardiovascular diseases, which depend on RTK-mediated signaling. Full article
(This article belongs to the Special Issue Modeling & Control of Disease States)
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Open AccessArticle
Control Charts for Monitoring Process Capability Index Using Median Absolute Deviation for Some Popular Distributions
Processes 2019, 7(5), 287; https://doi.org/10.3390/pr7050287
Received: 4 February 2019 / Revised: 7 May 2019 / Accepted: 13 May 2019 / Published: 15 May 2019
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Abstract
A control chart monitoring the process capability index (PCI) using median absolute deviation (MAD) is proposed to analyze the industrial process performance. Extensive simulation studies were carried out to evaluate the performance of MAD-based PCI control charts under the low, moderate, and high [...] Read more.
A control chart monitoring the process capability index (PCI) using median absolute deviation (MAD) is proposed to analyze the industrial process performance. Extensive simulation studies were carried out to evaluate the performance of MAD-based PCI control charts under the low, moderate, and high asymmetric conditions when the process characteristic follows Weibull, log-normal, and gamma distributions. The performance of the proposed control charts was evaluated based on the average run lengths. The practical implementation of the proposed charts was also illustrated with industrial data. Full article
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