scholarly journals Marketing Expansion Strategies for Local Crops-Based Couscous in Tahoua State, Niger Republic

2021 ◽  
Vol 28 (2) ◽  
pp. 70-80
Author(s):  
Z. Amadou ◽  
B.H. Mohammed
Keyword(s):  
Willingness To Pay ◽  
Sample Size ◽  
Confidence Intervals ◽  
Market Share ◽  
Fractional Factorial Design ◽  
Fractional Factorial ◽  
Market Shares ◽  
Locally Grown ◽  
Analyze Data ◽  
Expansion Strategies

This study investigates the own-price and cross-price elasticities for locally grown crops-based couscous and compares confidence intervals computation for willingness-to-pay and market shares under Krinsky Robb and Delta bootstrapping methods. Syntheses of previous literature and a focus group with consumers had helped to identify nine brands of couscous included in this research. The fractional factorial design was used to collect data from three hundred consumers, while the multinomial logit was used to analyze data. Results indicate that rice, cowpea and millet-based couscous were the most preferred by consumers and their market share accounts of more than fifty percent. The Results from simulation showed that confidence intervals under Krinsky and Robb stabilize as a sample size increases and thereby adjusting for skewness. However, confidence intervals under Delta computation are constant regardless of sample size, thereby failing to adjust for skewness. Finally, results also indicate that skewness was also accommodated in confidence intervals for market share because its values progressively adjust as sample size increases. These findings may be useful to boost cropbased couscous demand in the study area and beyond and thereby improving farmers’ revenue and offering diet  diversification opportunity. Keywords: Marketing expansion, Strategies, Local crops, Couscous, Willingness-to-pay

Optimization of PES Nanocomposite Membrane Preparation Using Statistical Analysis and Experimental Design

2019 ◽  
Author(s):  
Yasin Orooji ◽  
Fatemeh Noorisafa ◽  
Nahid Imami ◽  
Amir R. Chaharmahali
Keyword(s):  
Statistical Analysis ◽  
Experimental Design ◽  
Factorial Design ◽  
Fractional Factorial Design ◽  
Fractional Factorial ◽  
Nanocomposite Membrane ◽  
Membrane Preparation ◽  
Membrane Fabrication

<p>Using experimental design and statistical analysis (½ Fractional Factorial Design), this study investigates the effect of different parameters in the membrane fabrication on the performance of nanocomposite PES/TiO<sub>2</sub> membrane. </p>

scholarly journals Fabrication of a Silicide Thermoelectric Module Employing Fractional Factorial Design Principles

2021 ◽  
Author(s):  
Joachim S. Graff ◽  
Raphael Schuler ◽  
Xin Song ◽  
Gustavo Castillo-Hernandez ◽  
Gunstein Skomedal ◽  
...  
Keyword(s):  
Factorial Design ◽  
Fractional Factorial Design ◽  
Waste Heat ◽  
Thermoelectric Module ◽  
Fractional Factorial ◽  
Taguchi Methods ◽  
Dimensional Parameter ◽  
Cu Metallization ◽  
Manganese Silicide ◽  
And Performance

AbstractThermoelectric modules can be used in waste heat harvesting, sensing, and cooling applications. Here, we report on the fabrication and performance of a four-leg module based on abundant silicide materials. While previously optimized Mg2Si0.3Sn0.675Bi0.025 is used as the n-type leg, we employ a fractional factorial design based on the Taguchi methods mapping out a four-dimensional parameter space among Mnx-εMoεSi1.75−δGeδ higher manganese silicide compositions for the p-type material. The module is assembled using a scalable fabrication process, using a Cu metallization layer and a Pb-based soldering paste. The maximum power output density of 53 μW cm–2 is achieved at a hot-side temperature of 250 °C and a temperature difference of 100 °C. This low thermoelectric output is related to the high contact resistance between the thermoelectric materials and the metallic contacts, underlining the importance of improved metallization schemes for thermoelectric module assembly.

scholarly journals Consumer Preferences and Willingness to Pay for Potting Mix with Biochar

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3432
Author(s):  
McKenzie Thomas ◽  
Kimberly L. Jensen ◽  
Dayton M. Lambert ◽  
Burton C. English ◽  
Christopher D. Clark ◽  
...  
Keyword(s):  
Willingness To Pay ◽  
Online Survey ◽  
Product Information ◽  
Consumer Preferences ◽  
Market Potential ◽  
Marginal Costs ◽  
Consumer Demographics ◽  
Market Shares ◽  
Potential Market ◽  
Dedicated Energy Crops

Biochar is a co-product of advanced biofuels production from feedstocks including food, agricultural, wood wastes, or dedicated energy crops. Markets for soil amendments using biochar are emerging, but little is known about consumer preferences and willingness to pay (WTP) for these products or the depth of the products’ market potential for this product. This research provides WTP estimates for potting mix amended with 25% biochar, conditioned on consumer demographics and attitudes about product information labeling. Data were collected with an online survey of 577 Tennessee home gardeners. WTP was elicited through a referendum contingent valuation. Consumer WTP for an 8.81 L bag of 25% biochar potting mix is $8.52; a premium of $3.53 over conventional potting mix. Demographics and attitudes toward biofuels and the environment influence WTP. Biochar amounts demanded are projected for the study area’s potential market. Optimal prices, profits, and market shares are estimated across different marginal costs of producing biochar potting mix.

scholarly journals Producing Magnetic Nanocomposites from Paper Sludge for the Adsorptive Removal of Pharmaceuticals from Water—A Fractional Factorial Design

Nanomaterials ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 287
Author(s):  
Luciana S. Rocha ◽  
Érika M. L. Sousa ◽  
María V. Gil ◽  
João A. B. P. Oliveira ◽  
Marta Otero ◽  
...  
Keyword(s):  
Activated Carbon ◽  
Factorial Design ◽  
Fractional Factorial Design ◽  
Metal Oxide Nanoparticles ◽  
Aqueous Media ◽  
Fractional Factorial ◽  
Paper Sludge ◽  
Synthesis Conditions ◽  
Adsorptive Removal ◽  
Iron Salts

In view of a simple after-use separation, the potentiality of producing magnetic activated carbon (MAC) by intercalation of ferromagnetic metal oxide nanoparticles in the framework of a powder activated carbon (PAC) produced from primary paper sludge was explored in this work. The synthesis conditions to produce cost effective and efficient MACs for the adsorptive removal of pharmaceuticals (amoxicillin, carbamazepine, and diclofenac) from aqueous media were evaluated. For this purpose, a fractional factorial design (FFD) was applied to assess the effect of the most significant variables (Fe3+ to Fe2+ salts ratio, PAC to iron salts ratio, temperature, and pH), on the following responses concerning the resulting MACs: Specific surface area (SBET), saturation magnetization (Ms), and adsorption percentage of amoxicillin, carbamazepine, and diclofenac. The statistical analysis revealed that the PAC to iron salts mass ratio was the main factor affecting the considered responses. A quadratic linear regression model A = f(SBET, Ms) was adjusted to the FFD data, allowing to differentiate four of the eighteen MACs produced. These MACs were distinguished by being easily recovered from aqueous phase using a permanent magnet (Ms of 22–27 emu g−1), and their high SBET (741–795 m2 g−1) were responsible for individual adsorption percentages ranging between 61% and 84% using small MAC doses (35 mg L−1).

Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Observational Studies ◽  
Treatment Effects ◽  
Treatment Selection ◽  
High Dimensional ◽  
Selection Mechanism ◽  
Dimensional Models ◽  
Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

Optimization of Magnesium Separation and Extraction from Boron Slurry Using Response Surface Methodology

2011 ◽  
Vol 366 ◽  
pp. 366-369
Author(s):  
Feng Gao ◽  
Rong Fu ◽  
Ming Yang Qian ◽  
Zhu Min Wang ◽  
Xiang Zhang
Keyword(s):  
Response Surface Methodology ◽  
Reaction Time ◽  
Factorial Design ◽  
Response Surface ◽  
Reaction Temperature ◽  
Fractional Factorial Design ◽  
Design Criterion ◽  
Fractional Factorial ◽  
Effective Factors

Response surface methodology was used to optimize the soaking Mg leaching ratio from the boron slurry screened by 25 fractional factorial design. Five effective factors such as H2SO4 concentrations, reaction time, reaction temperature and stir velocity were tested by using 25 fractional factorial design criterion and three effective factors H2SO4 concentrations, reaction time and reaction temperature showed significant effect(P2SO4 concentrations of 0.29mol/l, reaction time of 90 min and reaction temperature of 50°C. Three runs of additional confirmation experiments were conducted. The mixture magnesium leaching value was 58.20%.

Sign in / Sign up

Export Citation Format

Share Document