AJUR Volume 22 Issue 4 (December 2025)
Click on this link to download the full high-definition interactive pdf for AJUR Volume 22 Issue 4 (December 2025). The Crossref link for this issue is https://doi.org/10.33697/ajur.2025.155
Links to individual manuscripts, abstracts, and keywords are provided below.
p.3 Fraud Detection in Incentive-Based Online Surveys Through Follow-up Demographic Verification
Andy E. Lewis, Kevin B. Gittner, & Lauren M. Matheny
https://doi.org/10.33697/ajur.2025.156
ABSTRACT: Online survey platforms like Qualtrics, SurveyMonkey, and Amazon Mechanical Turk have become essential tools for fast, cost-effective data collection, but growing concerns over data quality and the risk of fraud have accompanied their rapid growth. The purpose of this study was to determine whether follow-up demographic verification surveys can identify fraudulent participation and improve data quality in survey research conducted through online survey platforms.
An orthopedic activity level survey with 35 embedded data quality checks was distributed using Amazon Mechanical Turk. A follow-up demographic verification survey was sent to 28 participants who contacted the survey administrator after completing the original survey. Five datasets were cleaned and merged using R, allowing for the identification of potential patterns of fraud through the direct comparison of demographic information between the original and follow-up surveys.
Participants who did not complete the follow-up survey exhibited signs of fraud and poor data quality, including one email being linked to multiple participant accounts and higher counts of failed data quality checks. Although the small sample size limited the ability to detect statistical significance, descriptive patterns indicate practically meaningful differences. In contrast, demographic discrepancies were minimal among those who completed the follow-up. Open-text box similarity detection was the most effective individual data quality check. Integrating follow-up verification surveys into study designs provides a practical and scalable approach to detect fraud and maintain data integrity before compensation is distributed, offering researchers a cost-effective method to protect incentive budgets and data quality within online survey research. KEYWORDS: Online Survey Platforms; Data Quality; Online Research; Fraud Detection; MTurk; Participant Verification; Demographic Consistency; Incentive Structures; Follow-Up Surveys; Crowdsourced Data
p.11 Siderophore Production by a Rhizosphere-Associated Streptomyces from Cyperus virens
Phoebe Dennison, Robert Samples, Riccardo Racicot, & Lesley-Ann Giddings
https://doi.org/10.33697/ajur.2025.158
ABSTRACT: Rhizosphere microbiomes produce iron chelators or siderophores to capture ferric iron, an essential nutrient for growth that is not always bioavailable. Plants with siderophore activity aid in phytoremediation by removing heavy metals and other pollutants. The Cyperus genus of plants has a high affinity for iron uptake in the phytoremediation of wastewater. Herein, we isolated a siderophore-producing microbe from the rhizosphere of Cyperus virens, an understudied member of this genus, using the Chrome Azurol S (CAS) assay. While this isolate has siderophore activity, it did not exhibit antimicrobial activity when plated on Aspergillus niger, A. flavus, or Bacillus subtilis. The isolate was identified as Streptomyces sp. PD-S100-1 through genomic sequencing and de novo assembly with a draft genome size of 8.2 Mbp and 73% GC content. antiSMASH analysis of the genome identified several siderophore biosynthetic gene clusters, including those involved in the production of mirubactin A and bacillibactin. Liquid chromatography-mass spectrometry (LC-MS) detected several siderophores, including mirubactin A–D, enterobactin, and other catecholates from these siderophore families. The positive CAS assay, siderophore gene cluster identification, and LC-MS/MS analyses show that the rhizosphere of C. virens contains siderophore-producing bacteria. Most detected metabolites, including enterobactin, increased in the presence of cerium, a lanthanide involved in the expression of secondary metabolites, whereas mirubactin production was reduced. The presence of rhizosphere siderophore-producing bacteria suggests that C. virens may have potential applications in environmental phytoremediation, targeting pollutants from wastewater, mining, agriculture, and energy production. KEYWORDS: Siderophore; Mirubactin A; Enterobactin; Bacillibactin; Rhizosphere; Cyperus virens; Streptomyces; CAS Assay
p.25 Utilizing a Low-Cost Telescope to Produce Color-Magnitude Diagrams of an Open Cluster
Clay Reece & Inseok Song
https://doi.org/10.33697/ajur.2025.159
ABSTRACT: Hands-on learning is critical for students’ education, especially for more complex subjects, such as astrophysics. Astrophysics students are often taught more theoretical concepts than practical research skills, and thus have trouble adjusting to learning methods that produce new astronomical discoveries. This is a complicated problem, as it is difficult to obtain astronomical equipment due to high costs; large telescopes and good camera equipment are expensive. Many schools simply do not have the budget to spend on costly telescopes and cameras. This is an enormous drawback for students’ education, as they cannot apply the knowledge they gain in the classroom setting to further their understanding of the astrophysics topics they learn. This paper aims to show that producing scientific data is possible even with low-cost, entry-level astronomical equipment, which can be utilized in classroom settings to help teach students about typical astronomical research methodology through hands-on learning. Many tools exist in astronomy that are either open source or relatively cheap, and this should not go unnoticed by students and educators with a low budget. This paper aims to utilize these tools to show that astronomical data can be retrieved with a great deal of success. KEYWORDS: Astronomy Education; Hands-On Learning; Education; Accessibility; Low-Cost; Telescope; Photometry; Color-Magnitude Diagram; Open Cluster; Pleiades
p. 35 Farm Ownership, Leverage, and Government Programs Impact on Net Farm Income
Thomas Moss, Annie Kinwa-Muzinga, & Lawrence Muzinga
https://doi.org/10.33697/ajur.2025.160
ABSTRACT: As agriculture faces increasing sustainability challenges, understanding the financial aspects of farm firms is critical for preparing future agricultural professionals. This research presents a simulation model to analyze multi-year farm profitability under different tenure structures, multiple leverage scenarios, and the presence or absence of government programs. The model considers crop prices, yields, government payments, and market fluctuations to assess the financial viability of the farm. By generating income statements, balance sheets, cash flow reports, and financial ratios, it evaluates farm stability across operations of 400, 800, and 1,200 acres. Preliminary findings suggest that government programs have a significant impact on financial resilience, risk management, and long-term profitability, with effects varying by farm size and market conditions. This study offers a valuable decision-making tool, enabling professionals to strike a balance between profitability and sustainability in an evolving agricultural landscape. KEYWORDS: Farm Financial Analysis; Farm Profitability; Farm Tenure Structures; Government Farm Programs; Agricultural Risk Management; Financial Resilience; Farm Size & Economic Stability; Farm Firm Financial Simulation Model; Agricultural Leverage; West Central Iowa Agriculture
p.49 A Statistical Look into how Common Soccer Metrics Influence Expected Goal Measures in the Professional Game
Tristan Rumsey & Shaha Alam Patwary
https://doi.org/10.33697/ajur.2025.161
ABSTRACT: The advent of sports analytics has ignited a fervor across all sporting disciplines, particularly soccer, where clubs are sprinting to harness vast data reserves to elevate team performance, spearhead effective marketing endeavors, and bolster financial gains crucial for club expansion. Much like Billy Beane’s transformative “Moneyball” approach, soccer clubs are in pursuit of innovative strategies to transcend financial limitations and achieve triumph. In soccer, where goals are scarce commodities, heightened offensive efficacy becomes imperative. Presently, one metric stands out as pivotal in gauging a team’s goal-scoring success: expected goals (xG). This metric quantifies the likelihood of a given shot or opportunity culminating in a goal, making it a linchpin in a team’s offensive strategy. Maximizing expected goals becomes paramount for teams aiming to capitalize on limited scoring opportunities during matches. Crucially, the first step in reshaping tactical approaches hinges on identifying the most influential variables in predicting expected goals. This study employs an array of machine learning methodologies, including Ridge, Lasso, Elastic Net, and Group Lasso models. The objective is to unveil the key predictor variables that significantly impact team (offensive) performance, often delineating the thin line between championship glory and defeat. With the aim of predicting xG, this research also incorporates modified bootstrap techniques to compute prediction intervals for the regularized machine learning models. By delving into the intricate fabric of soccer analytics, this study seeks to empower clubs with actionable insights, fostering a new era of strategy and competitive edge on the field. KEYWORDS: Soccer analytics; Expected goals; Managerial strategy; Statistical and machine learning methods; Bootstrap method; Prediction interval.