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We present a novel method for soybean [Glycine max(L.) Merr.] yield estimation leveraging high-throughput seed counting via computer vision and deep learning techniques. Traditional methods for collecting yield data are labor-intensive, costly, and prone to equipment failures at critical data collection times and require transportation of equipment across field sites. Computer vision, the field of teaching computers to interpret visual data, allows us to extract detailed yield information directly from images. By treating it as a computer vision task, we report a more efficient alternative, employing a ground robot equipped with fisheye cameras to capture comprehensive videos of soybean plots from which images are extracted in a variety of development programs. These images are processed through the P2PNet-Yield model, a deep learning framework, where we combined a feature extraction module (the backbone of the P2PNet-Soy) and a yield regression module to estimate seed yields of soybean plots. Our results are built on 2 years of yield testing plot data—8,500 plots in 2021 and 650 plots in 2023. With these datasets, our approach incorporates several innovations to further improve the accuracy and generalizability of the seed counting and yield estimation architecture, such as the fisheye image correction and data augmentation with random sensor effects. The P2PNet-Yield model achieved a genotype ranking accuracy score of up to 83%. It demonstrates up to a 32% reduction in time to collect yield data as well as costs associated with traditional yield estimation, offering a scalable solution for breeding programs and agricultural productivity enhancement. 

Prior research emphasizes the benefits of university makerspaces, but overall, quantitative metrics to measure how a makerspace is doing have not been available. Drawing on an analogy to metrics used for the health of industrial ecosystems, this article evaluates changes during and after COVID-19 for two makerspaces. The COVID-19 pandemic disturbed normal life worldwide and campuses were closed. When students returned, campus life looked different, and COVID-19-related restrictions changed frequently. This study uses online surveys distributed to two university makerspaces with different restrictions. Building from the analysis of industrial ecosystems, the data were used to create bipartite network models with students and tools as the two interacting actor groups. Modularity, nestedness, and connectance metrics, which are frequently used in ecology for mutualistic ecosystems, quantified the changing usage patterns. This unique approach provides quantitative benchmarks to measure and compare makerspaces. The two makerspaces were found to have responded very differently to the disruption, though both saw a decline in overall usage and impact on students and the space’s health and had different recoveries. Network analysis is shown to be a valuable method to evaluate the functionality of makerspaces and identify if and how much they change, potentially serving as indicators of unseen issues. 

Analyzing interactions between actors from a systems perspective yields valuable information about the overall system's form and function. When this is coupled with ecological modeling and analysis techniques, biological inspiration can also be applied to these systems. The diagnostic value of three metrics frequently used to study mutualistic biological ecosystems (nestedness, modularity, and connectance) is shown here using academic engineering makerspaces. Engineering students get hands‐on usage experience with tools for personal, class, and competition‐based projects in these spaces. COVID‐19 provides a unique study of university makerspaces, enabling the analysis of makerspace health through the known disturbance and resultant regulatory changes (implementation and return to normal operations). Nestedness, modularity, and connectance are shown to provide information on space functioning in a way that enables them to serve as heuristic diagnostics tools for system conditions. The makerspaces at two large R1 universities are analyzed across multiple semesters by modeling them as bipartite student‐tool interaction networks. The results visualize the predictive ability of these metrics, finding that the makerspaces tended to be structurally nested in any one semester, however when compared to a “normal” semester the restrictions are reflected via a higher modularity. The makerspace network case studies provide insight into the use and value of quantitative ecosystem structure and function indicators for monitoring similar human‐engineered interaction networks that are normally only tracked qualitatively. 

The growing popularity of progressive education pedagogies combined with the continued rise of the maker movement has propelled knowledge and interest in makerspaces across education. As a result, makerspaces have become a common sight on college campuses around the world. These spaces offer students a unique opportunity to apply the hard and soft skills learned in the classroom to projects with real consequences. Students learn to take ownership of their work and experiment and iterate until they are proud of their results. Through this process they grow in design self-efficacy, innovation, and collaboration skills. Makerspaces are a powerful tool in the hands of university professors, but not all students benefit from them equally. Many students still face real or perceived barriers to entry caused in part by a lack of comfort and confidence in the space. This study seeks to examine students’ sense of belonging at a university makerspace and determine how gender, major, study locations, and classes affect this sense. Online surveys were distributed to students who used the makerspace in Fall 2022 and Spring 2023. Students answered a series of Likert style questions about how they feel in the space and statistical tests were used to determine correlation and significance of the results. It was found that sense of belonging in the space overall was high, but that females, non-mechanical engineering majors, and students who did not study in the space reported statistically lower sense of comfort. Suggestions are given to makerspace administrators of how to address and avoid these gaps in belonging and make the space more inclusive and welcoming to all students. 

Academic makerspaces have continued to rise in popularity as research shows the diverse benefits they provide to students. More and more engineering curriculums are incorporating makerspaces and as such there is a need to better understand how their student users can best be served. Surveys administered to makerspace users at a public research university in the Southwest United States during Fall 2020, Spring 2021, Spring 2022, and Fall 2022 tracked student tool usage trends with academic career stages. The survey asked questions about prior experience, motivation, tool usage, and demographics. Analyzed results for mechanical engineering student users provide insight into how curriculum and class year affect the specific tools used and the percentage of students who used a particular tool. The survey results also create a bipartite network model of students and tools, mimicking plant-pollinator type mutualistic networks in ecology. The bipartite network models the student interactions with the tools and visualizes how students interact with the tools. This network modeling enables ecological network analysis techniques to identify key makerspace actors quantitatively. Ecological modularity, for example, identifies divisions in the student-tool makerspace network that highlight how students from different majors (here we investigate mechanical) utilize the makerspace's tools. Modularity is also able to identify “hub” tools in the space, defined as tools central to a student's interaction within the space, based on student-tool connectivity data. The analysis finds that tools commonly used for class by mechanical engineering students, such as the 3D printer or laser cutter, act as gateway tools that bring users into the space and help spark interest in the space's other tools. Using the combined insights from the survey results and the network analysis, ecological network metrics are shown here to be a promising route for informing makerspace policy, tool purchases, and curriculum development. The results can help ensure that the space is set up to give students the best learning opportunities. 

Abstract As the popularity of makerspaces and maker culture has skyrocketed over the past two decades, numerous studies have been conducted to investigate the benefits of makerspaces for university students and how to best establish an inclusive, welcoming environment in these spaces on college campuses. However, unprecedented disruptions, such as the COVID-19 pandemic, have the potential to greatly affect the way that students interact with makerspaces and the benefits that result. In this study, a survey asking about prior makerspace involvement, tool usage, and student demographics was administered to students who use academic makerspaces at two large public universities. Survey data was collected for three semesters (Fall 2020, Spring 2021, and Spring 2022) and spanned both during and after the height of the COVID-19 pandemic. To quantify the differences between the semesters, nestedness and connectance metrics inspired by ecological plant-pollinator networks were utilized. These ecological metrics allow for the structure of the interactions of a network to be measured, with nestedness highlighting how students interact with tools and connectance with the quantity of student-to-tool interaction. The network analysis was used to better gauge the health of the makerspace and the type and frequency of interactions between tools. The raw survey data combined with the ecological metrics provided unique insight into the struggles the makerspaces encountered throughout the pandemic. It was found that nestedness, a measure of system stability, decreases with a decrease in tool usage. Additionally, the higher the connectance the more students interacted with the space. Utilizing metrics such as these and better understanding student tool interactions can aid makerspaces in monitoring their success and maintaining a healthy and welcoming space, as well as tracking the current health of the space. In combination with the survey results, a deep understanding of what challenges the space is facing can be captured. 

Globally, universities have heavily invested in makerspaces. This investment requires an understanding of how students use tools and how tools to aid in engineering education, as well as how the spaces can be improved. Network analysis of human systems can often yield valuable information about how the networks work and function. Applying network techniques to makerspaces can yield helpful information that is otherwise not visible. This thesis’s primary focus is the application of a variety of bio-inspired network techniques to improve the understanding of the makerspace. Several parallels can be drawn between makerspace networks and other mutualistic networks, such as plant-and-pollinator networks where the system’s success depends on the interaction between the two species. The ecological metrics would establish measurable values that the health and conditions of a network can be evaluated using. These three metrics are nestedness, modularity, and connectance, which can provide structural information about the network and act as diagnostics tools that can change depending on different system conditions. The makerspace at the universities went through several regulatory changes due to COVID-19, providing a unique opportunity to utilize the metrics to analyze the health of the space under higher regulatory restrictions and return to normal operations. The makerspace is converted into a bipartite network to allow for ecological analysis techniques where the spaces are modeled with students interacting with tools. Null models evaluate the significance of the nestedness and modularity results. Findings indicate that makerspaces tend to be structurally nested, but when compared to normal operating conditions, they can be seen to exhibit modularity during the higher restriction environment. The makerspace network and subsequent analysis provide insight into the use of ecological metrics in human systems and provide potential ideas for results to be used in various networks. The following network analysis also yields valuable information identifying essential hub tools and student interactions within the space, showcasing the capabilities the ecological study of human networks can have on human systems. 

When college campuses resumed in-person learning opportunities following initial lockdowns during the COVID-19 pandemic, many facets of campus life looked different. These differences continue to evolve from semester to semester because of changing health guidelines, school decisions, and personal convictions. Academic makerspaces were not exempt from these changes and have experienced fluctuating usage and usage barriers over the past several semesters. Better understanding the effects of COVID-19 on academic makerspaces can help ensure that students continue to draw maximum benefits from these learning spaces and also provides potential advice for administrators and educators for future disturbances. Data collected via tool usage surveys administered to makerspace users at a large public university during the three semesters following the start of the pandemic (Fall 2020, Spring 2021, and Spring 2022) is used here to investigate. COVID-19 restrictions present during Fall 2020 and Spring 2021 were mostly loosened in Spring 2022. The makerspace is modeled as a bipartite network, with student and tool interactions determined via end-of-semester surveys. The network is analyzed using nestedness, a metric primarily used in ecology to evaluate the stability of an ecosystem and proposed here as a quantitative method to evaluate makerspace health. The surveys used to create the network models also provide validation, as students were asked to share tools used during the semester in question. The results suggest that nestedness is linearly proportional to usage, both increases and decreases. As such, tracking the nestedness of a makespace over time can serve as a warning that unintended restrictions are in place, intentional restrictions and/or policies may be too severe, or whether a space has effectively recovered from temporary restrictions. 

To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption. More specifically, we provide a comprehensive review of key components of SCFs and crucial elements necessary for its success. It includes, high-speed connections, sensors for data collection, and edge, fog and cloud computing along with innovative wireless technologies to enable cyber agricultural system. We also cover the topic of adoption of these technologies that involves important considerations around data analysis, privacy, and the sharing of data on platforms. From a social science and economics perspective, we examine the net-benefits and potential barriers to data-sharing within agricultural communities, and the behavioral factors influencing the adoption of SCF technologies. The focus of this review is to cover the state-of-the-art in smart connected farms with sufficient technological infrastructure; however, the information included herein can be utilized in geographies and farming systems that are witnessing digital technologies and want to develop SCF. Overall, taking a holistic view that spans technical, social and economic dimensions is key to understanding the impacts and future trajectory of Smart and Connected Farms.