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Impacts of pea-canola intercropping and associated management practices on the soil microbiome
Full text linkIntercropping has begun to captivate the interests of agronomists and microbial ecologists alike due to its promise to reduce the environmental impacts of our agricultural systems while maintaining high yields and improving profit margins. Specifically, legume non-legume intercropping systems show exceptional promise due to the unique relationship legumes share with nitrogen fixing bacteria known as rhizobia. The pea-canola (Pisum sativum-Brassica napus) intercropping system (peaola) is one such intercropping system. Consistently, it has been observed to show improved yields from monoculture within the same land area with minimal to no nitrogen fertilizer addition. Agronomists had long hypothesized that plant associated microorganisms were responsible for this observation, but work had not been done to show this. Therefore, the goal of our study was to determine how pea-canola intercropping, and its management practices impact the structure and predicted function of soil and rhizosphere microbial communities. We also aimed to determine if changes to nitrogen availability and uptake were changed with intercropping and across management practices with the potential to link these changes to the microbial community. To do so, we collected soil and rhizosphere samples from field grown peaola which tested the impacts of intercropping, variation in nitrogen fertilizer application, and variation in pea and canola seeding rates. We performed 16S rRNA sequencing to allow for characterization of the microbial community, and we analyzed the soil and leaf nitrogen content. Regardless of intercropping and management practices, we saw that plants ultimately determined the structure of their associated microbial communities, and subsequently their predicted function. Evidence from our analyses of soil and leaf nitrogen content revealed that generally variation in peaola management practices did not cause any significant changes. This is likely due to the plant facilitated changes we observed compensating for changes in nutrient availability based on nitrogen fertilizer application and plant-plant interactions. Future studies on intercropping systems should focus on characterizing changes in plant-plant interactions across management practices in addition to validating and further investigating the function of the microbial community
Updates in assessing soil organic carbon and their implications for evaluating land use change emissions
No full textEmissions from land use changes are relevant for environmental policy analysis. Since the late 1990s and early 2000s many of these analyses have examined induced land use changes (ILUC) from biofuel production and policy as well as their associated greenhouse gas (GHG) emissions. These studies have often used the Harmonized World Soil Data (HWSD) to evaluate the corresponding changes in soil organic carbon (SOC) as a part of their assessments. However, those modeling efforts that used this data set have not necessarily implemented its latest version, and therefore, their results may not represent the most recent available SOC data sources. As an example, the AEZ-EF model, which has been frequently used in assessing ILUC emissions, is using the oldest version of this data set. To improve the quality and accuracy of ILUC estimates, this paper creates a new global data set of SOC by combining the latest version of the HWSD (V.2.0) with newly available national soil maps for the USA and Australia. Using this new data set, we then calculate the average SOC for each land cover type (cropland, pasture, and forest) by country and by agro-ecological zones (AEZs). Furthermore, we revised AEZ-EF model to adopt the new SOC data by land types. Finally, the revised AEZ-EF model is used to assess ILUC emissions for a few biofuel pathways to demonstrate the extent to which the new SOC data may affect ILUC emissions. The results of this paper indicate that the newest version of the HWSD represents a lower level of SOC at the global scale compared to its older version. The results also show that the revised AEZ-EF model calculates relatively lower ILUC emissions for the examined pathways compared to its older version
Large-Scale Surveys in Education (Research Proposal)
Full text linkDigital literacy, including computer and information literacy (CIL) and computational thinking (CT), is vital for thriving in today's digitalized educational and career environments. To enhance digital literacy among K–12 students, it is crucial to explore factors influencing their ICT learning, including their perceptions of ICT and self-efficacy. This study examines the mediating role of self-efficacy in the relationship between students' perceptions of societal values and risks associated with ICT and their achievement in CIL and CT. Using the ICILS 2018 U.S. sample dataset, we conducted a multilevel path analysis with ten plausible values to explore these relationships. The findings reveal that positive perceptions of ICT strongly predict higher CIL and CT achievement scores through the mediation of ICT self-efficacy. Self-efficacy related to the use of general applications explained 42% of the variation in CIL scores and 33% of the variation in CT scores. However, self-efficacy associated with specialist applications negatively predicted both outcomes. These results suggest that while general ICT self-efficacy facilitates academic achievement in digital literacy, overconfidence or challenges related to specialist applications may hinder performance. This study contributes to the growing body of research on digital literacy by highlighting the complex interplay between students' perceptions of ICT, their self-efficacy, and their academic outcomes. The findings provide actionable insights for designing educational practices, tools, and curricula that foster positive ICT perceptions and strengthen self-efficacy, thereby improving students' preparedness for the digital demands of the 21st century
Japan's Marshmallow World The Community, Effervescence, and Fashion of Plus-Size Women in Japan
Full text linkThis dissertation outlines the plus-size experience in Tokyo, Japan, focusing on themes of body positivity, shared experience of body shame and acceptance, infrastructure and influence on plus-size fashion, effervescence and community, and body positivity on social media. In this study I argue that (1) plus-size women are not outwardly ‘refusing’ through dress to conform to society as observed in classic subculture groups, but come together to produce a community which draws on approved models of collectivity already present within Japanese society, (2) Japanese plus-size women aim to fit in with general Japanese society and work to build an impression that they are just as trendy as their thinner, straight-sized counterparts, and (3) body positivity takes shape within the known parameters of Japanese social acceptability
Fault Resilient Smart Power Distribution System
Full text linkThis thesis presents novel approaches for fault location identification and post-fault reconfiguration in smart power distribution systems. The fault location identification methods are proposed in phasor domain and time domain and consider inverter-interfaced distributed generations (IIDGs). The proposed methods are not restricted to any type of data and can benefit from any type of available measurements in the distribution system including synchronized and unsynchronized measurements, active/reactive power data, and virtual measurements. These methods explicitly address data uncertainties and accommodates different load types. The proposed time domain fault location identification method is formulated based on differential equations of the system and accounts for the peculiarities of power distribution systems with distributed generations. It considers the presence of loads, multi-phase laterals and sub-laterals, heterogenous overhead and underground lines, and infeeds and remote-end fault current contributions of distributed generations. It implements a systematic approach to eliminate possible multiple fault location estimations to provide a single correct estimation of the actual location of the fault. Finally, an optimal post-fault reconfiguration method for active distribution systems, explicitly considering protection system constraints is proposed. Utilizing a generalized Benders decomposition algorithm, the method embeds the short-circuit program into the optimization problem. It effectively determines network topology and resolves power flow with protection system coordination constraints using second-order cone programming
Utilizing a Non-Destructive Approach to Improve Uniformity of Phase 2 Fruit Samples in the Washington State University Apple Breeding Program
Full text linkThe Washington State University Apple Breeding Program (WABP) is a conventional breeding program composed of three phases of selection. In the second phase (Phase 2), five replicate trees are evaluated for several years at three geographically diverse sites in the State of Washington. Phase 2 accessions are typically harvested over three picks using subjective maturity assessments that include change in the background color and starch degradation. Comprehensive evaluation of new apple (Malus domestica Borkh.) selections requires uniform samples of fruit and multiple years of data. Lack of information regarding optimal harvest date, accompanied by limited fruit and variable maturity throughout the canopy can lead to large within-sample variation of maturity in Phase 2 samples, as subjective methods do not ensure collection of uniform samples. The Delta Absorbance meter is a non-destructive portable device used to estimate maturity by measuring the index of absorbance difference (IAD) of fruit (IAD = A670nm – A720nm). Typically, IAD is applied in commercial varieties to identify variety-specific harvest windows, track fruit ripening, and sort fruit postharvest. With the rapid turnover and limited fruit availability in Phase 2 accessions, IAD was incorporated into the WABP harvest protocol to test whether IAD could be used to guide harvest without developing a model. With an IAD corresponding to a specific starch index rating, accessions were harvested using IAD-guided sampling. Coefficient of variation (CoV) was used to calculate within-sample variability. In 15 out of 26 samples, within-sample variability of IAD was reduced using IAD-guided sampling. Sample variability was also determined using the destructive trait measures of starch, soluble solids content, titratable acidity, and firmness. CoV of IAD-guided samples was reduced compared to non-IAD-guided samples in IAD at-harvest and three or more destructive trait measures after storage in six, nine, and seven out of 26 samples in in picks 1, 2, and 3, respectively. However, reductions of within-sample variability were accession, site, pick, and trait specific. A strong effect of accession and site was observed in all traits. IAD-guided sampling is currently being implemented as a supplementary measure during harvest of WABP Phase 2 accessions
Enhancing Distribution System Situational Awareness Using Smart Meters
Full text linkThe increasing levels of distributed energy resources (DERs) penetrations can resultin significant operational challenges at the power distribution level. This calls for enhancing distribution system level situational awareness to assist distribution system operators in making the best operational decisions under disturbances. In this work, enhancing situational awareness implies extending the visibility of the power distribution system beyond the substation. Extensive roll-out of smart meters allows utilities to collect significantly more real-time data, which can be leveraged to develop new monitoring tools. This work uses smart meter data available at the grid-edge to enhance the power distribution system’s situational awareness. Specifically, within a three-level SA framework that includes perception (level-1), comprehension (level-2) and projection (level-3), this work makes the following contributions with regard to level-2 SA tools. First, we evaluate the impacts of inadequate end-use load models and associated nonlinearities on system state variables. To this end, we compare the harmonicdistortions due to a detailed and approximated constant current model for nonlinear residential loads on North American residential distribution systems. North American residential distribution system with split-phase configurations is usually modeled as a single-phase equivalent model. The harmonic distortion analysis with the single-phase equivalent network model is found to underestimate the current distortions and overestimate the voltage distortions. The analysis with three different approximate models for an unbalanced split-phase residential distribution system shows that the balanced split-phase residential distribution system is a reasonably accurate representation of the unbalanced split-phase residential distribution system. Second, we evaluate the impacts of measurement non-idealities on system-level states. To this end, we evaluate the effects of the following smart-meter attributes: measurement interval, time synchronization error, meter bias, and measurement noise on distribution system voltage and total loss. The analysis shows that temporal aggregation of smart meter data can alter the data distribution. The time synchronization error for household smart meters does not follow Gaussian distribution and the incomplete data reporting creates uncertainty in system-level states. Third, to enhance the real-time SA of distribution systems, we develop a state estimation algorithm that provides an integrated monitoring of primary and secondary feeders. This approach enables us to use measurement information from the smart meters at the grid-edge by appropriately modeling the secondary feeder beyond the distribution-level service transformer. The accuracy of the state estimation algorithm is tested for different levels of measurement and model inaccuracies and for non-gaussian measurement error distributions. Finally, to enhance distribution-level SA during outage conditions, we develop a spectral clustering-based outage detection algorithm that is simple to implement and solves efficiently by leveraging the smart meter outage notifications and forecasted load data. In conclusion, this thesis highlights and develops tools to effectively use smart meters to enhance distribution-level SA beyond primary feeders to the customer level
Time-resolved spray characterization via unified optical flow and binarization technique
No full textThis work leverages an unsupervised machine learning and advanced image processing techniques to characterize the breakup of fuel sprays in a small-scale combustor under reacting conditions, providing valuable insights into near-nozzle flow phenomenology. The proposed methodology integrates an improved optical flow model on a convolutional neural network to extract flow vectors with a binarization technique to assess droplets’ size and shape across the region of interest. The velocimetry approach demonstrates superior performance compared to a state-of-the-art optical flow model when applied to high-speed X-ray phase contrast spray images, achieving more accurate and reliable flow predictions. Moreover, breakup processes are quantified by breakup length and sphericity in accordance with velocity estimations, allowing a more complete characterization of the flow. This study establishes a robust methodology for analyzing spray morphology and primary breakup in compact combustors, contributing valuable means of understanding and optimizing fuel spray behavior in advanced combustion systems.•Improved unsupervised machine learning enables accurate velocity estimations.•Spray characteristics for six fuel mass flow rates of a conventional jet fuel are visualized under reacting conditions.•Time-resolved droplet size, sphericity, velocity, and breakup length are obtained simultaneously
Investigating the Impact of Trauma-Informed Academic Advising and First-Year Seminar Programs on Student Academic Achievement
Full text linkTransitioning from K-12 to college can be daunting for most students, especially those without appropriate coping mechanisms. The first-year experience is a unique, determining factor that could impact students' academic engagement, mental health, academic progress, retention, and ultimate success (implied as graduation). First-year seminars have been adopted by many colleges/universities to support students' transition smoothly to college, fostering engagement and academic success. However, the alarming attrition rate among first-year students is a cause for concern. This study aims to make a unique contribution to research in first-year seminar by adapting the MUSIC (eMpowerment, Usefulness, Success, Interest, Care) Model of Academic Motivation Inventory, to investigates the effectiveness of first-year seminars from students' perspectives to enhance retention, academic engagement, and achievement among underrepresented college students. The MUSIC Model of Academic Motivation Inventory was selected due to its comprehensive framework for assessing various dimensions of academic motivation, including empowerment, usefulness, success, interest, and care. The underrepresented college students enrolled in various first-year seminar programs will be the target population. The mixed-method approach, including surveys, interview and focus group discussions, will be employed to collect data. The multivariate analysis and structural regression model will be used to examine the manifest factors that fit the model that will be developed based on the theoretical framework we established. The findings will contribute to the overall perception and adoption of first-year seminar and proffer improvement strategies to making first-year experience effective
Distribution Power System Resiliency in DER-Rich Environment
Full text linkOver the last decades, serious climate change has caused significantly increase in frequency and severity of extreme weather events, causing devastating impacts on people's lives and livelihoods, also unprecedented challenges to power system, especially islands or remote communities. Besides power supply reliability, power system resiliency is getting more and more important under this background. To prevent further climate change deterioration, an urgent energy system transition from fossil fuel to renewable energy is happening worldwide swiftly. With a substantially increase of distribution energy resources (DERs), energy coordination or energy management could play an important role in increasing power system resiliency. Motivated by the remarkable role DERs could play in energy transition and improving distribution power system resiliency, my research has mainly focused on following topics :Firstly, with a collective effort from former researchers, a complete set of resilience metrics is proposed for intuitively monitoring distribution power system resilience status and providing decision support in operation and planning. Secondly, a proactive energy resource coordination strategy is proposed for the economic and resilient operation of an islanded hydro-diesel-battery microgrid. Further, device failure uncertainty caused by extreme weather events is considered, and thus an uncertainty-involved microgrid energy management strategy is proposed. Additionally, a microgrid BESS sizing model is proposed to meet isolated operation duration requirements, an increasingly important concern of microgrids as extreme weather events getting more severe and frequent. Finally, a two-stage restoration strategy with the assistance of ESAT (Enhanced Situational Awareness Tool) is proposed