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Evaluation of Bioenergy Crops Impacted by Climate Change and Agricultural Practices
No full textSwitchgrass is considered a model bioenergy crop that has traditionally served as a forage feedstock in the U.S. and has a strong tolerance of environmental stresses. However, the impacts of current and future climate variation on switchgrass ecophysiology have not been well established. To accomplish this, we conducted three experiments on switchgrass. The first experiment we looked at a comparable bioenergy crop, eastern gamagrass, to evaluate eastern gamagrass’ ability as a bioenergy and forage feedstock in comparison to switchgrass. Both grasses were grown with varying nitrogen applications and cover crops. Our results showed that switchgrass produced more biomass by 29.5%, however forage quality measurements were higher in eastern gamagrass. The second experiment we looked at how different precipitation patterns effects switchgrass’ leaf photosynthesis with changes in light levels and CO2 concentrations. Increasing light levels enhanced the response of leaf photosynthesis in the +50% precipitation treatment. Increasing CO2 concentrations increased net photosynthetic rate until 600 ppm CO2 was reached, were it started to level off. In the same experiment we evaluated different models to determine their prediction accuracy for light and CO2 response curves for switchgrass. Lastly, in our final field experiment we evaluated switchgrass’ leaf ecophysiology under constant elevated temperatures. Ambient temperatures of switchgrass stands were increased by 1-3.3 ºC. Leaf photosynthesis and chlorophyll content measurements were taken. Constant heat stress caused photosynthesis to be reduced as well as chlorophyll content. Overall, we showed the potential benefits of the use of biofuels and how they are affected by climate change
Freeze-thaw damage assessment of engineered cementitious composites using the electrochemical impedance spectroscopy method
Get PDFThe mechanical properties of engineered cementitious composites (ECC) in service in cold regions can be significantly degraded by periodic freezing and thawing. In this work, the damage degree of freeze–thaw of ECC was systematically assessed by using the electrochemical impedance spectroscopy (EIS) technique. In addition, Nuclear Magnetic Resonance (NMR) Relaxometry measurements were also performed to obtain pore structure parameters, and the uniaxial tensile tests were also carried out to analyse the tensile performance after freeze–thaw cycles. From the acquired results, it was demonstrated that the EIS behaviour of ECC varied with the freeze–thaw cycles. The diameter of the Nyquist curve in high-frequency was gradually reduced by increasing the freeze–thaw cycles. Furthermore, the volume resistance of ECC after freeze–thaw gradually decreased with the increase in the number of freeze–thaw cycles. The simplified microstructure and conductive paths were used to describe the freeze–thaw damage mechanism of ECC. An equivalent circuit model of ECC exposed to freeze–thaw cycles was proposed, and the parameters of the equivalent circuit model were thoroughly analysed. The experimental findings clearly indicate that the EIS method is an appropriate technique for evaluating the damage degree of freeze–thaw of ECC
Challenges, Barriers, and the Underrepresentation of Black Women in Sustainable Global World Environment
Get PDFWomen make up at least 50.8% of the United States population, and 46.8% are in the professional workforce per Census Quick Facts from 2016. United States Department of Labor, n.d.). Despite making up half of the United States population, women only represent 26% of managing roles in the workforce. In the 2019 study, “Women in the Workplace 2019”, McKinsey & Company found women to still lag in corporate America in areas of salary gaps, promotions due to the broken rung, glass ceilings, lack of training and development, among other gender and racial barriers. Workplace Fairness”, a broken rung is a missing step in the “corporate ladder”, which prevents women in entry-level roles from being promoted into management. The broken rung is the more significant barrier for Black women navigating the workplace. To successfully navigate the workplace and ascend into management roles, Black women saw the need to use perseverance strategies due to underrepresentation and the influence of race and traditional privileged gendered roles. The study’s outcome addresses the challenges, barriers, and perseverance strategies Black women used to ascend into management roles. Mentorship and sponsorship are critical for helping Black women to advance within the workplace. The research study may be significant to Black women managers and future leaders. Without the critical influence of a mentor or sponsor, the Black woman will remain underrepresented in management positions. Further exploration of specific perseverance strategies and how they may have been demonstrated in their collegiate programs to prepare Black women for their professional careers
Prediction of Attentiveness of Students in a Traditional Teaching Environment
No full textThis research seeks to increase understanding of how people interact and learn with technology with respect to cyberlearning. This research will focus on understanding how college students interact and learn by the utilization of physiological sensors in a traditional teaching environment to capture metrics related to attentiveness. Using blink rate, blink duration, and gaze position parameter data regarding students viewing a traditional PowerPoint presentation related to the Internet of things (IoT), an attentiveness metric was derived for each parameter then an overall parameter was summed. A prediction model using was then trained and tested using Adaptive Neuro Fuzzy Inference System (ANFIS) to predict attentiveness Using blink rate, blink duration, and gaze position parameter data
Impact of Salt Modified Diet on Systemic IRAE in Breast Cancer Immunotherapy
No full textBreast cancer is the most common type of cancers in women worldwide and is one of the leading causes of cancer-related mortality in women in the United States. Over the past few decades, cancer immunotherapy has emerged as a promising treatment for different types of cancers. Immune checkpoint inhibitors (ICI) is a class of immunotherapy that has proven to be effective against breast cancer, as shown by data from various clinical trials with monoclonal antibodies targeting cytotoxic T lymphocyte antigen-4 (CTLA-4), programmed death1 (PD-1), and programmed death ligand-1 (PD-L1). However, development of unpredictable immune-related adverse events (irAEs) following ICI therapy poses a challenge to its clinical benefits. Previous research in our laboratory and others have shown that high-salt (HS) plays a role in inflammatory activation of CD4+T cells, thereby leading to anti-tumor responses. The goal of this study is to evaluate the impact of dietary salt modification on therapeutic and systemic outcomes in breast-tumor-bearing mice, subsequent to anti-CTLA4 monoclonal antibody (mAb) based ICI therapy. As both HS diet and ICI are associated with activation of CD4+T cells, we hypothesized that a combination of these two factors would induce enhanced irAE response. Murine breast tumor models were developed by injecting Py230 murine breast cancer cells into mammary fat pad of 12 week old C57Bl/6 mice. The tumor-bearing mice were divided into three cohorts to study the impact of dietary salt modification- mice on normal-salt (NS) diet; mice on high-salt (HS) diet; and mice on low-salt (LS) diet. Next, to investigate the effect of salt-modified diet on anti-tumor efficiency of anti-CTLA4 mAb based ICI therapy, the mice were injected with monoclonal antibodies on day 7, 10, and 13. The results indicated that HS diet cohort with anti-CTLA4 showed the worst survival outcome, out of the three cohorts. Also, the combination of high-salt and anti-CTLA4 mAbs exhibited increased lung infiltration and circulating levels of inflammatory cytokines than LS diet cohort. With anti-CTLA4 mAbs, LS diet cohort on the other hand had enhanced survival advantage compared with NS diet cohort in combination with anti-CTLA4 mAbs, and reduced tumor progression than isotype mAb. In conclusion, the data from our study suggests that LS diet not only reduced irAE response in breast cancer based- murine models when treated with anti-CTLA4 mAbs, but also retained the immunotherapeutic efficacy of anti-CTLA4 mAbs against cancer
Increasing racial diversity in the North American Plant Phenotyping Network through conference participation support
Get PDFA key goal of the North American Plant Phenotyping Network (NAPPN) annual conference is to cultivate a new generation of scientists from diverse backgrounds. As part of their effort to diversify the plant phenomics research community, NAPPN acquired funding to cover all attendance costs for participants from historically black colleges and universities (HBCU) for the 2022 annual meeting. Seven award recipients represented the first attendees from HBCUs in the conference\u27s 6-year history. In this commentary, we report on the impact of the conference awards, including lessons learned, and the future of the award
Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment
Get PDFAccurate estimation of reference evapotranspiration (ETr) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman–Monteith equation (ASCE-PM) estimates ETr across various timescales using ground weather station data. However, discrepancies persist between estimated ETr and measured ETr obtained from weighing lysimeters (ETr-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ETr estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996–1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ETr estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ETr across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning
Consistency of Instructional Coaching: Does It Impact Student Achievement?
No full textInstructional coaching has been associated with increased student achievement therefore much research has been devoted to the components of effective instructional coaching. Although some of the literature implies a need for consistency in instructional coaching, no research had been done to determine if student achievement was impacted by the consistency of an instructional coach. Therefore, a quantitative causal comparative study was conducted to compare teachers who had a consistent instructional coach with teachers who had an inconsistent instructional coach, and teachers who had no instructional coach in terms of ELA student achievement scores of third, fourth, and fifth grade teachers in a school district in the southeast. The conceptual framework of this study demonstrated how a consistent instructional coach leads to greater teacher efficacy and student achievement but was debunked based on the findings. Convenience sampling was used to obtain participants who were categorized into three groups based on the independent variable. The one-way ANOVA results of some of the models examined in this study showed statistically significant differences among teachers who were consistently coached, inconsistently coached, and not coached. However, the model measuring change in teacher classroom averages over a two-year period did not have statistically significant differences (p = .54). Therefore, the differences in instructional coaching practices should be examined to determine areas which improve student achievement. These findings provide school leaders with information that may help in decision making about retainment, promotion, and the transferring of instructional coaches