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Data and code used for the manuscript, Robust native aquatic plant propagule banks limit curly-leaved pondweed
No full textThe relationship between the invasive curly-leaf pondweed (CLP) and native aquatic plants was explored via a sixteen month mesocosm experiment and annual surveys of the passive revegetation of the newly restored Provo River Delta (Utah, USA) (PRD). These studies took place in 2023 and 2024
Impact of Thermodynamic Factors and Intrinsic Properties on Crystallization Behavior and Physical Properties of Edible Fats
No full textCrystallization behavior of three edible fats: interesterified soybean oil (IESBO), interesterified palm olein (IEPO), and fully hydrogenated palm kernel oil (FHPKO) were explored to understand how this process is affected by different factors. The study focused on how the chemical composition of these fats affects their behavior like how much time they require to be crystallized at a certain temperature. By using different scientific methods, how quickly each fat began to crystallize and how much energy it took to start the process was measured. Among the methods used, Gompertz equation, a mathematical model, gave the most consistent and reliable results to differentiate among the fats in respect to different variables.
Different properties of fats including solid fat content, crystal morphology, polymorphism, melting behavior, hardness, viscosity, surface tension, density and heat capacity were also measured at high and low temperatures. For instance, IEPO at high temperature showed significantly larger crystals than all other samples and FHPKO was the hardest and had the highest melting enthalpy at low temperature due to its higher concentration of saturated fatty acid. Also, some properties of fats such as density and heat capacity did not change with the change of temperature. These findings can be helpful to better understand how different fats work at different temperatures and can be used to improve the design and production of different fat-based food products such as margarine, chocolate etc., making them more appealing and stable for consumers
Cognitive Dynamics in Undergraduate Engineering Education: Effects of Need for Cognitive Closure and Achievement Goal Orientation on Cognitive Engagement
No full textCognitive and motivational factors play an important role in students’ engagement during problem-solving, which can lead to academic and professional success. Among these factors, Need for cognitive closure (NFCC) and achievement goal orientation (AGO) have been studied extensively individually, but their interaction and impact on engineering students’ problem-solving is still not explored. NFCC refers to the person’s desire for quick answers, and AGO is the student’s approach to learning and success. This research investigated the impact of NFCC on gender and engineering major, and the relation between NFCC (five facets) and AGO (Mastery, Performance) at their different levels. Additionally, this research also analyzed the engagement pattern and problem-solving behavior of students with different levels of NFCC and AGO.
In the first phase of this mixed-method study, engineering students completed surveys measuring their NFCC and AGO. The survey data were used to identify if there is a difference in the NFCC approach between men and women, and also based on the engineering major. In the second phase, 8 participants were selected based on their NFCC and AGO profiles for the problem-solving activity. The entire process was recorded and analyzed later to understand the engagement and problem-solving behavior of the participants with different profiles of NFCC(Low/high) and AGO (Performance or Mastery).
The results of this research have shown that there is no difference in students’ desire for quick answers across most genders, and NFCC and AGO are related to each other based on their levels only. The engagement pattern of students with different profiles of NFCC and AGO showed the dominance of AGO with mastery and performance goal orientation. This means if the person is having either high or low NFCC and Mastery goal orientation, their engagement behavior will be defined by their Mastery goal orientation, i.e., their approach to problem solving will be based on learning rather than competing with others. Students halt their NFCC behavior and align their engagement in problem solving with their goal orientation. These findings help educators better understand how to support diverse learners and promote deeper thinking in engineering classrooms
Software Developer Job Satisfaction: Interpretable Machine Learning Insights From the Stack Overflow Developer Survey
No full textMany researchers have investigated the factors influencing software developer workplace outcomes, such as job satisfaction, due to the central role of the tech industry in the global economy and the specialized expertise of software developers. Past research has often relied on small surveys and traditional analysis methods, with limited use of modern machine learning techniques. This study introduces an efficient and scalable approach to analyzing software developer job satisfaction using interpretable machine learning. We use data from the 2019 and 2024 Stack Overflow Developer Surveys, an annual survey of software developers worldwide that encompasses a broad range of topics, including demographics, technology usage, and workplace experiences. We experiment with machine learning classification models to predict job satisfaction and interpret results using SHAP (Shapley Additive Explanations), a method for understanding model decision-making. We find that key job satisfaction predictors for these datasets include how developers feel about their support from managers, work environment, and company resources, and that many factors interact in complex ways. We also find that demographic characteristics, such as gender, race, and years of experience, are often more impactful predictors for minority subgroups and that some of the same factors contribute to high job satisfaction predictions during one year and low job satisfaction predictions during the other year, which may be related to industry changes resulting from the COVID-19 pandemic. Overall, our results uncover actionable insights into software developer job satisfaction, highlight the potential for interpretable machine learning methods to support informed decision-making in the tech workplace, and identify patterns that could be the focus of future work
From Walking to Parkour: A Structured Survey of RL for Dynamic Skills in Legged Robots
No full textThis survey reviews recent advances in applying reinforcement learning (RL) to enable dynamic and ballistic motions in legged robots, including running, jumping, stair climbing, and parkour. Focusing on high-agility behaviors that challenge traditional control frameworks, we categorize foundational locomotion tasks and highlight the RL methods, such as Proximal Policy Optimization, curriculum learning, and hybrid model-based strategies that have proven effective. We discuss the key challenges in transferring learned policies to real-world robots, managing uncertainty, and integrating perception and proprioception. Drawing from over 150 recent works, we provide a structured taxonomy of objectives, algorithms, and platforms, and identify trends in simulation frameworks and deployment strategies. This review aims to serve as a resource for researchers developing next-generation legged robots that can achieve robust, adaptive, and high-performance motion in complex environments. We provide a companion repository that maps the collection of papers, their methods, tasks, and platforms. This repository can be found at: https://github.com/DIRECTLab/legged_revie
Design Approach for Labyrinth Weir: Case Study of La Laye Dam
No full textThis paper presents ARTELIA’s gradual approach for the design and construction of labyrinth weirs, from the preliminary concept design to the commissioning. It is illustrated with the case of La Laye dam, where an additional lateral spillway with labyrinth weir has been developed to increase the existing discharge capacity. The design process involved preliminary hydraulic calculations, 3D numerical modeling and a physical scale model. The paper elaborates on the technical solutions and optimizations which were considered to provide the Client with the most hydro-economical solution
TriCounty Vaccine Improvement Program (TC-VIP): Enhancing Vaccine Confidence and Uptake in Rural Utah
No full textThe Utah State University Extension TriCounty Vaccine Improvement Program addressed low rural vaccination rates across a three-county area through bilingual social media campaigns, health fairs, training in motivational interviewing, and neuromarketing. Evaluation results showed improved skills, broad community reach, and positive participant feedback, demonstrating a replicable model to reduce vaccine hesitancy and strengthen rural health systems
Developing a research agenda in partnership with parents of deaf/hard-of-hearing children
No full textBackground: Community-based participatory research (CBPR) is an excellent fit for understanding and addressing the unique and diverse needs of deaf or hard-of-hearing (DHH) children and their families.
Objectives: We describe lessons learned from a capacity building project initiating an academic-community partnership between a multidisciplinary research team and a small group of parents of DHH children. Trainings and focus group style discussions created a research agenda reflective of families’ priorities.
Methods: Processes are described related to the development and composition of a Family Advisory Council, training topics and methods, the process for generating and prioritizing topics, and evaluation procedures.
Lessons learned: Takeaways relate to the importance of flexibility, relationship building, CBPR mentoring, and navigating institutional barriers.
Conclusions: CPBR is an appropriate and effective framework for engaging families with DHH children in the research process
Utilizing Diatoms in Wastewater Treatment for Pollutant Removal
No full textWastewater treatment systems are increasingly challenged by a complex mix of pollutants including excess nutrients, organic dyes, and persistent chemicals like per- and polyfluoroalkyl substances (PFAS). Conventional treatment technologies often struggle to effectively and affordably remove these contaminants. This study explores the use of two diatom species, Phaeodactylum tricornutum and Navicula cryptocephala var. veneta, both single-celled microalgae with silica-based cell walls, as an environmentally sustainable approach to tertiary wastewater treatment. Diatoms offer dual functionality through surface adsorption properties and metabolic flexibilities, making them promising candidates for integrated treatment strategies.
To test the trophic flexibilities of diatoms, P. tricornutum was grown under mixotrophic (mix of metabolism for organic carbon in addition to photosynthesis) and photoautotrophic (photosynthesis only) conditions. Mixotrophic conditions were implemented through the addition of organic carbon using glucose, glycerol, sodium acetate, and sodium pyruvate, respectively. Cultures supplemented with glycerol and sodium pyruvate showed the greatest improvements in growth, nutrient uptake, and lipid production, demonstrating the potential of mixotrophic growth to enhance performance in carbon-rich waste waters. In contrast, naturally occurring non-axenic N. veneta cultures were analyzed to assess diatom-bacteria interactions under different lighting and carbon regimes. Although bacterial species associated with plant-like growth promotion were present, no cooperative benefits were observed. Instead, elevated carbon conditions favored rapid bacterial growth, which ultimately suppressed diatom abundance, highlighting the need for microbial community studies in mixed systems to assess diatom capabilities in tertiary wastewater treatment.
To assess pollutant removal, both live and dried diatom biomass were tested for their ability to adsorb organic dyes. Dried biomass, especially from P. tricornutum, achieved high dye removal efficiencies due to favorable surface interactions between the dye molecules and the silica-based frustules. N. veneta showed moderate performance, supporting its potential as an alternative biosorbent. However, when tested against perfluorooctanoic acid (PFOA), a representative PFAS compound, neither diatom species demonstrated measurable adsorption, indicating that additional surface engineering would be required to target these resistant contaminants.
This work demonstrates that diatoms can play a valuable role in sustainable wastewater treatment. Their ability to simultaneously assimilate nutrients and bind synthetic dyes makes them a cost-effective and low-energy option for tertiary treatment applications. While further development is needed to expand their capabilities to emerging pollutants like PFAS, the findings support continued research into diatom-based technologies as part of a broader push toward more circular and biologically integrated wastewater treatment systems