scholarly journals Construction of Thirty Six Points Second Order Rotatable Design in Three Dimensions with a Practical Hypothetical Case Study

2019 ◽  
Vol 5 (6) ◽  
pp. 111-118
Author(s):  
Nyakundi Omwando Cornelious
Keyword(s):  
Second Order ◽  
Three Dimensions ◽  
Hypothetical Case ◽  
Rotatable Design

scholarly journals Modeling of rose coco beans using twenty four points optimum second order rotatable design

2017 ◽  
Vol 5 (2) ◽  
pp. 96
Author(s):  
Isaac Tum ◽  
John Mutiso ◽  
Joseph Koske
Keyword(s):  
Response Surface ◽  
Maximum Yield ◽  
Second Order ◽  
Three Dimensions ◽  
Coefficient Of Determination ◽  
Yield Response ◽  
Efficient Production ◽  
Phosphorus And Potassium ◽  
Nitrogen Phosphorus ◽  
Rotatable Design

The response surface methodology (RSM) is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables, and the objective is to optimize the response. The objective of the study was to model the rose coco beans (Phaseolus vulgaris) through an existing A-optimum and D-efficient second order rotatable design of twenty four points in three dimensions in a greenhouse setting using three inorganic fertilizers, namely, nitrogen, phosphorus and potassium. Thus, the objective of the study was accomplished using the calculus optimum value of the free/letter parameter f=1.1072569. This was done by estimating the parameters via least square's techniques, by making available for the yield response of rose coco beans at calculus optimum value design for the first time. The results showed that, the three factors: nitrogen, phosphorus, and potassium contributed significantly on the yield of rose coco beans (p<0.05). In GP3G, the second-order model was adequate for 1% level of significance with p value of 0.0034. The analysis of variance (ANOVA) of response surface for rose coco yield showed that this design was adequate due to satisfactory level of a coefficient of determination, R2, 0.8066 and coefficient variation, CV was 10.30. This study demonstrated the importance of statistical methods in the optimal and efficient production of rose coco beans. We do recommend a randomize screening of all the fertilizer components with which it has influence on rose coco beans be done to ascertain the right initial amount of each fertilizer that could achieve maximum yield than this study realized.

scholarly journals A Sequential Third Order Rotatable Design of Eighty Points in Four Dimensions with an Hypothetical Case Study

2019 ◽  
pp. 1-9
Author(s):  
Nyakundi Omwando Cornelious ◽  
Matunde Nambilo Cruyff
Keyword(s):  
Response Surface ◽  
Continuous Cultivation ◽  
Mineral Elements ◽  
Hypothetical Case ◽  
Third Order ◽  
Four Dimensions ◽  
Design Variables ◽  
Controllable Factors ◽  
Rotatable Design

In research, experiments must be performed at pre determined levels of the controllable factors, meaning that an experimental design must be selected before the experiment takes place. Once an experimenter has chosen a polynomial model of suitable order, the problem arises on how best to choose the settings for the independent variables over which he has control. A particular selection of settings or factor levels at which observations are to be taken is called a design. A design may become inappropriate under special circumstances requiring an increase in factors or levels to make it more desirable. In agriculture for instance, continuous cultivation of crops may exhaust the previously available mineral elements necessitating a sequential appendage of the mineral elements which become deficient in the soil over time. In current study, an eighty  points four  dimensional  third order rotatable design is constructed by combining two, four dimensional second order rotatable  designs and a practical hypothetical case study is given by converting coded levels to natural levels. We present an illustration on how to obtain the mathematical parameters of the coded values and its corresponding natural levels for a third order rotatable design in four dimensions by utilizing response surface methodology to approximate the functional relationship between the performance characteristics and the design variables.  This design permits a response surface to be fitted easily and provides spherical information contours besides the economic use of scarce resources in relevant production processes.

scholarly journals Construction of twenty-six points specific optimum second order rotatable designs in three dimensions with a practical example

2020 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Tum Isaac Kipkosgei
Keyword(s):  
Response Surface ◽  
Second Order ◽  
Three Dimensions ◽  
Predictor Variables ◽  
Scarce Resources ◽  
Quadratic Response Surface ◽  
Quadratic Response ◽  
Free Parameters ◽  
Economic Use ◽  
Rotatable Design

This quadratic response surface methodology focuses on finding the levels of some (coded) predictor variables x = (x1u, x2u, x3u)' that optimize the expected value of a response variable yu from natural levels. The experiment starts from some best guess or “control” combination of the predictor variables (usually coded to x = 0 for this case x1u=30, x2u=25 and x3u =40) and experiment is performed varying them in a region around this center point.We go further to construct a specific optimum second order rotatable design of three factors in twenty-six points. The achievement of this is done with estimation of the free parameters using calculus in an existing second order rotatable design of twenty-six points. Such a design permits a response surface to be fitted easily and provides spherical information contours besides the realizations of optimum combination of ingredients in Agriculture, horticulture and allied sciences which results in economic use of scarce resources in relevant production processes. The expected second order rotatable design model in three dimensions is available where the responses would then facilitate the estimation of the linear and quadratic coefficients. An example involving Phosphate (x1u), Nitrogen (x2u) and Potassium (x3u) is used to represent the three factors in the coded level and converted into natural levels.  

scholarly journals Optimal and efficient production of rose coco beans through the twenty-four points second order rotatable design

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Isaac Tum ◽  
Joseph Koske ◽  
John Mutiso
Keyword(s):  
Response Surface ◽  
Maximum Yield ◽  
Response Surfaces ◽  
Second Order ◽  
Three Dimensions ◽  
Coefficient Of Determination ◽  
Efficient Production ◽  
Phosphorus And Potassium ◽  
Nitrogen Phosphorus ◽  
Rotatable Design

The yield results of the twenty four points response surface methodology (RSM) design permitted a response surface to be fitted easily and provided spherical information contours besides the realizations of an optimum combination of the fertilizers in rose coco beans, which resulted in economic use of scarce resources for optimal production of rose coco beans. In this study an existing A-optimum and D-efficient second order rotatable design in three dimensions was used to produce rose coco beans optimally and efficiently. The general objective of the study was to produce rose coco beans (Phaseolus vulgaris) optimally and efficiently using an existing A-optimum and D-efficient twenty four points second order rotatable design in three dimensions in a greenhouse setting using three inorganic fertilizers, namely, nitrogen, phosphorus and potassium. Thus the study was accomplished using the calculus optimum value of the free/letter parameter f=1.1072569. The specific objectives were to estimate the linear parameters, thereby making available for the yield response of rose coco beans at calculus optimum value design for the first time, fitted and tested the model adequacy via lack of fit test, and then found the setting of the experimental factors that produces optimal response using contour plots to assist visualizes the response surfaces. This study demonstrated the importance of statistical methods in the optimal and efficient production of rose coco beans. The results showed that the three factors: nitrogen, phosphorus, and potassium contributed significantly on the yield of rose coco beans (p<0.05). The regression coefficients were determined by employing least square's techniques to predict quadratic polynomial model for group 3 greenhouse (GP3G) for the three fertilizer combinations. In GP3G, the second-order model was adequate at 1% level of significance with a p-value of 0.0034. The analysis of variance (ANOVA) of response surface for rose coco yield showed that this design was adequate due to satisfactory level of a coefficient of determination, R2, 0.8066 (GP3G) and coefficient variation, CV was 10.30. The canonical analysis showed that there was the saddle point for GP3G, meaning there was no unique optimum; therefore, ridge analysis was used to overcome the saddle problem. The result from ridge analysis provided the maximum yield of 70.25grams for the three fertilizer combinations at radii of one. We, therefore, recommend the use of GP3G design since it gave the required coefficient of determination (R2=80.66) and the maximum yield (70. 25grams) was achieved.

scholarly journals Macroscopic and Multi-Scale Models for Multi-Class Vehicular Dynamics with Uneven Space Occupancy: A Case Study

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 102
Author(s):  
Maya Briani ◽  
Emiliano Cristiani ◽  
Paolo Ranut
Keyword(s):  
Road Network ◽  
Real Data ◽  
Second Order ◽  
Heavy Vehicles ◽  
First Order ◽  
Multi Scale ◽  
Stop And Go ◽  
Scale Models ◽  
Road Space

In this paper, we propose two models describing the dynamics of heavy and light vehicles on a road network, taking into account the interactions between the two classes. The models are tailored for two-lane highways where heavy vehicles cannot overtake. This means that heavy vehicles cannot saturate the whole road space, while light vehicles can. In these conditions, the creeping phenomenon can appear, i.e., one class of vehicles can proceed even if the other class has reached the maximal density. The first model we propose couples two first-order macroscopic LWR models, while the second model couples a second-order microscopic follow-the-leader model with a first-order macroscopic LWR model. Numerical results show that both models are able to catch some second-order (inertial) phenomena such as stop and go waves. Models are calibrated by means of real data measured by fixed sensors placed along the A4 Italian highway Trieste–Venice and its branches, provided by Autovie Venete S.p.A.

scholarly journals My Client Knows That He's About to Stutter: How Can We Address Stuttering Anticipation during Therapy with Young People Who Stutter?

2018 ◽  
Vol 39 (04) ◽  
pp. 356-370 ◽  
Author(s):  
Hope Gerlach ◽  
Naomi Rodgers ◽  
Patricia Zebrowski ◽  
Eric Jackson
Keyword(s):  
Decision Making ◽  
Young People ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Hypothetical Case ◽  
Clinical Tool ◽  
Goal Development ◽  
Internal Experience

AbstractStuttering anticipation is endorsed by many people who stutter as a core aspect of the stuttering experience. Anticipation is primarily a covert phenomenon and people who stutter respond to anticipation in a variety of ways. At the same time as anticipation occurs and develops internally, for many individuals the “knowing” or “feeling” that they are about to stutter is a primary contributor to the chronicity of the disorder. In this article, we offer a roadmap for both understanding the phenomenon of anticipation and its relevance to stuttering development. We introduce the Stuttering Anticipation Scale (SAS)—a 25-item clinical tool that can be used to explore a client's internal experience of anticipation to drive goal development and clinical decision making. We ground this discussion in a hypothetical case study of “Ryan,” a 14-year-old who stutters, to demonstrate how clinicians might use the SAS to address anticipation in therapy with young people who stutter.

scholarly journals Evacuation Decision-Making during Hurricane Matthew: An Assessment of the Effects of Social Connections

2017 ◽  
Vol 9 (4) ◽  
pp. 769-776 ◽  
Author(s):  
Jennifer Collins ◽  
Robin Ersing ◽  
Amy Polen
Keyword(s):  
Decision Making ◽  
Disaster Preparedness ◽  
Three Dimensions ◽  
Social Connections ◽  
Storm Event ◽  
Community Based ◽  
Memory Decay ◽  
Perceived Access ◽  
The Relationship

Abstract This study conducted in Florida examines the relationship between an individual’s social connections and their decision to evacuate during a hurricane warning. Using Hurricane Matthew in 2016 as a case study, a survey was conducted on two groups (those who evacuated and those who did not), assessing one’s social connections considering three dimensions: dependability, density, and diversity. These factors, in addition to socioeconomic variables (e.g., age, race, education), were used to better define a picture for what influences evacuation decision-making. To avoid memory decay, the surveys were completed at the time of the evacuation for those who evacuated and immediately after the passage of Matthew for those who did not evacuate. It was concluded, through statistical analyses, that the perceived dependability of a person’s social connections (i.e., their perceived access to resources and support) played a significant role in the decision to evacuate or not, with non-evacuees having more dependable relationships and having a tightknit community they can rely on during a storm event. On the other hand, the density and diversity of peoples’ social connections did not significantly impact the decision to evacuate. This study has important implications for adding to the knowledge base on community-based sustainable disaster preparedness and resilience.

scholarly journals Systemic Design for Second-Order Effects

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Evan Barba
Keyword(s):  
Organizational Change ◽  
Systems Approach ◽  
Core Competency ◽  
Second Order ◽  
Order Effects ◽  
Classroom Intervention ◽  
Second Order Effects ◽  
Design Skills ◽  
Systemic Design

Second-order effects refer to changes within a system that are the result of changes made somewhere else in the system (the first-order effects). Second-order effects can occur at different spatial, temporal, or organizational scales from the original interventions, and are difficult to control. Some organizational theorists suggest that careful management of feedback processes can facilitate controlled change from one organizational configuration to another. Recognizing that skill in managing feedback processes is a core competency of design suggests that design skills are potentially useful tools in achieving organizational change. This paper describes a case study in which a co-design methodology was used to control the second-order effects resulting from a classroom intervention to create organizational change. This approach is then theorized as the Instigator Systems approach.

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