Positionality of Income: An Exploration of the Influence of Cognition

Neoclassical economic theory asserts that individuals act independently in a utility maximizing manner. Recent literature modifies this theory and introduces a relative income or positional term in the utility function, making utility functions interdependent. The neoclassical theory views income as a non-positional good, whereas the modification views income as a positional good. Studies employing choice experiments through use of hypothetical surveys pose the Relative Income Question, which asks individuals to select between absolute or relative income. Many individuals exhibit a positional concern for income specifically. Individuals may consider various factors when evaluating their view on the positionality of income. A cognitive influence was first observed by Rand (2008), who found that individuals preferring absolute income scored significantly higher on cognitive ability tests compared to those preferring relative income. The focus of this study is to understand factors individuals may integrate in deciding whether they view income as a positional or non-positional good. The purpose of this paper is three-fold, to: (1) replicate the findings of the original relative income question experiment using new data; (2) evaluate whether relative income or absolute income is the more appropriate basis for economic models oriented towards policy making; (3) and investigate the relevance of a cognition argument in choosing between relative income and absolute income. The hypothesis is that individuals with relatively higher cognitive measure scores will select the absolute state as this is the more (economically) rational answer, as per the standard economic theory. It was found that the standard economic theory does not universally apply, supporting the importance and relevance of a relative income term in the utility function. However, individuals with higher scores on a measure of cognition tended view income as a non-positional good, complying with standard economic theory

ENGINEERING EMPOWERMENT: SCIENCE LITERACY THROUGH ENGINEERING DESIGN

Outcome A scientifically literate society capable of making effective decisions grounded in STEMinformed analyses of complex, real‐world challenges associated with food, fuel, water, landscape, and people issues. Finding Solutions for Life on a Small Planet Produce future STEM professionals Develop a science and technology literate society K-12 ENGINEERING: Exposure to Engineering Design; K‐12 Engineering Design Curricula; Undergrad Teaching Experience; K‐12 Educator Professional Developmen

Prairie Protector: student development of systems thinking habits in the context of agroecosystems

Introduction: The Great Plains ecosystem offers an opportunity for young people to gain knowledge about intricate systems through practical learning. The spread of woody plants into grasslands, known as the “Green Glacier,” poses a risk to biodiversity and animal production. Although effective management strategies exist, some land managers are reluctant to use them. It is critical to cultivate a scientifically literate population that can think systematically and make informed decisions based on STEM principles to address such complex agroecosystem problems. Methods: In this study, semi-structured focus group discussions with high school students were analyzed to determine whether Prairie Protector, an educational game, and its associated resources led to evidence of systems thinking habits in student conversation. Results: Analysis of the focus group transcripts revealed that the students developed systems thinking habits through their experience playing Prairie Protector while developing empathy for land managers and others involved in the Great Plains agroecosystem. In general, students found the game enjoyable and a useful tool for learning about agroecosystems, conservation land management, and the spread of invasive species. Discussion: Analysis of the student statements led to the development of a guiding framework to assess and analyze students development of systems thinking habits that could be used to scaffold student learning experiences to explore, understand, and interact with complex systems. Providing simulated environments for students to interact with complex systems should be explored in additional scenarios to support student development of systems thinking skills

Nebraska School Gardens and the Potential for Science, Technology, Engineering, and Math Learning

School-based growing spaces support student engagement in science, technology, engineering, and mathematics (STEM) learning through authentic agricultural pursuits. We conducted a survey of Nebraska schools to characterize existing school-based growing spaces and to identify challenges limiting garden-based STEM learning. Our findings confirm the use of school-based growing spaces for kindergarten through 12th-grade STEM instruction, especially in the sciences. Opportunities for technology and engineering experiences are currently limited, and additional professional development support is needed to broaden garden-based STEM learning efforts. Our findings are relevant to current and future Extension efforts supporting school gardens, especially in rural agricultural communities

SOURCES OF VARIABILITY AND UNCERTAINTY IN FOOD-ENERGY-WATER NEXUS SYSTEMS

A nexus approach contributes to the strategic allocation of resources to secure food, energy, and water for the world population. Integrated models considering the complex interactions across food, energy, and water (FEW) enhance decision-making and strategic planning towards resilience. However, a significant number of the existing integrated models leave unaddressed the inherent variability and uncertainty present in the FEW sectors. Here, we review the importance of characterizing variability over spatial and temporal scales and the importance of decreasing the uncertainty present within a FEW nexus systems. The review also discusses existing modeling tools that address variability and uncertainty on single and paired elements of the FEW nexus systems, as well as integrated tools that address the sources of variability and uncertainty across the nexus. Finally, the review highlights the opportunity to address the limitations of existing models through multidisciplinary approaches and the potential to integrate publicly available models, as has already been the case for single and coupled elements of the FEW nexus. Addressing variability and uncertainty would improve the robustness of a FEW systems modeling and would provide stakeholders with the capacity to make better-informed decisions

Exploring Elementary Students’ Scientific Knowledge of Agriculture Using Evidence-Centered Design

The public is more disconnected from agriculture than ever. Americans are now two to four generations removed from the farm with a majority of Americans having no direct experience in agriculture. As a result, the public lacks the knowledge and appreciation of the food, fuel, and fiber it demands. The National Agricultural Learning Objectives (NALOs) were recently developed to describe students’ agricultural knowledge but have, as yet, not been used to guide research into students’ agricultural literacy. The purpose of this project is to further understand students’ agricultural literacy through NALO-based assessment of students’ knowledge. This study focused on the NALOs in the areas of agriculture and the environment (AgE) and the STEM dimensions of agriculture (STEM) using a sequential exploratory mixed methods design. Thirty-five students participated in semi-structured interviews surrounding the NALOs. Interview data were coded and analyzed while using the evidence-centered design process to create empirically grounded assessments that were administered to a sample (n=400) of elementary students. Results suggest that students are more knowledgeable about the STEM dimensions of agriculture than the agricultural and environmental topics. Recommendations are provided to guide future research and development around the NALOs

SPHERICAL CRYSTALLISATION: A REVOLUTION IN THE FIELD OF PARTICLE ENGINEERING

Now-a-days direct tabletting technique is gaining more importance in Pharmaceutical manufacturing because it save money and time for tabletting but Good flowability and compressibility is prerequisite for drug to be prepared by direct compression. There are several techniques available to impart desired compressibility to drug, but the Spherical crystallization technique is the most promising one in which the drug crystals are modified using different solvents to directly compressible spherical agglomerates. Spherical agglomeration is particle engineering technique which involves the transformation of fine crystals into spherical shape which in turn enhances the powder properties such as particle size, shape, flow properties, solubility and bioavailability of pharmaceutical drug substances. The spherical crystallization further developed use with hydrophilic polymers to enhance dissolution rate characteristics of poorly water soluble drugs and can also be applied to sustain the drug release from solid dosage forms. The present review aims at the detailed comprehensive study about the technique, advantages and disadvantages, mechanism, different manufacturing methods of spherical agglomerates and characterization of spherical agglomerates

Invasive plant species as potential bioenergy producers and carbon contributors

Among recent advances in developing cellulosic and noncellulosic biofuel sources, corn, switchgrass, and others (e.g., camelina, canola) have risen to the top. In surveying the landscape, a potentially promising new area of bioenergy production exists—invasive plant species. The addition of invasive plant species as a bioenergy source will help to diversify the nation’s energy dependence and help in the reduction of the negative environmental and social impacts from energy crop production. In addition, belowground carbon stores may provide an opportunity to reduce the impacts associated with global climate change. In the agriculture sector, harvesting equipment is well advanced for facilitating efficient crop production on both small and large scales. In noncrop systems, the number of limitations and lack of need have slowed the progress of equipment development for biomass harvesting (Graneli 1984). The lack of economic return is an important reason for less intensive management in noncrop areas. With new markets emerging for cellulosic energy sources and advances in equipment technology, this increased incentive could help improve the level of management of invasive plant species in noncrop areas and subsequent harvest or removal of excess biomass. Using invasive plant species in bioenergy facilities would provide enticing opportunities for land managers and business developers. Since the primary focus of most invasive plant species management is on control of unwanted vegetation, the massive amounts of biomass from high cellulosic plants creates a challenge for disposal. Research has shown that piling or dispersing cut portions of many woody and herbaceous invasive plant species can result in the reestablishment by new plants from propagule segments (Boose and Holt 1999; Decruyenaere and Holt 2001). With advances in processing equipment, invasive plant species biomass could be processed into pellets for transporting to facilities that burn biomass or convert biomass to liquid biofuels. The processing of invasive plant species into a useable form (i.e., pellets) that does not promote the spread or introduction of unwanted vegetation minimizes the risks of environmental contamination and provides an economic opportunity for business development in rural communities. While corn and switchgrass are the leading plant candidates for biofuel production, they may not be the most sustainable. Alternatively, the removal of existing invasive plant species biomass and processing into pellets for combustion or liquid fuel conversion maybe more sustainable as it would comply with the US Executive Order 13112 on invasive species (Clinton 1999), support climate change initiatives (Crowl et al. 2008), and expand economic opportunities in rural areas by helping fulfill the mandate by the US Renewable Fuels Standards (USDA 2010)

Takagi–Sugeno Fuzzy Modeling of Skin Permeability

The skin is a major exposure route for many potentially toxic chemicals. It is, therefore, important to be able to predict the permeability of compounds through skin under a variety of conditions. Available skin permeability databases are often limited in scope and not conducive to developing effective models. This sparseness and ambiguity of available data prompted the use of fuzzy set theory to model and predict skin permeability. Using a previously published database containing 140 compounds, a rule-based Takagi–Sugeno fuzzy model is shown to predict skin permeability of compounds using octanol-water partition coefficient, molecular weight, and temperature as inputs. Model performance was estimated using a cross-validation approach. In addition, 10 data points were removed prior to model development for additional testing with new data. The fuzzy model is compared to a regression model for the same inputs using both R2 and root mean square error measures. The quality of the fuzzy model is also compared with previously published models. The statistical analysis demonstrates that the fuzzy model performs better than the regression model with identical data and validation protocols. The prediction quality for this model is similar to others that were published. The fuzzy model provides insights on the relationships between lipophilicity, molecular weight, and temperature on percutaneous penetration. This model can be used as a tool for rapid determination of initial estimates of skin permeability

Takagi–Sugeno Fuzzy Modeling of Skin Permeability

The skin is a major exposure route for many potentially toxic chemicals. It is, therefore, important to be able to predict the permeability of compounds through skin under a variety of conditions. Available skin permeability databases are often limited in scope and not conducive to developing effective models. This sparseness and ambiguity of available data prompted the use of fuzzy set theory to model and predict skin permeability. Using a previously published database containing 140 compounds, a rule-based Takagi–Sugeno fuzzy model is shown to predict skin permeability of compounds using octanol-water partition coefficient, molecular weight, and temperature as inputs. Model performance was estimated using a cross-validation approach. In addition, 10 data points were removed prior to model development for additional testing with new data. The fuzzy model is compared to a regression model for the same inputs using both R2 and root mean square error measures. The quality of the fuzzy model is also compared with previously published models. The statistical analysis demonstrates that the fuzzy model performs better than the regression model with identical data and validation protocols. The prediction quality for this model is similar to others that were published. The fuzzy model provides insights on the relationships between lipophilicity, molecular weight, and temperature on percutaneous penetration. This model can be used as a tool for rapid determination of initial estimates of skin permeability