Scholarly Works, Biological Systems Engineering

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  • When does a stream become a river?
    Czuba, Jonathan A.; Allen, George H. (Wiley, 2023-07-13)
    The distinction between a “stream” and “river” is imprecise and vague despite the popular usage of the terms across disciplines for describing flowing waterbodies. Based on an analysis of named flowing waterbodies in the continental United States, we suggest a bank-to-bank channel width of 15 m as a working threshold in defining smaller “streams” from larger “rivers.”.
  • Load-Out and Hauling Cost Increase with Increasing Feedstock Production Area
    Cundiff, John S.; Grisso, Robert D.; Resop, Jonathan P.; Ignosh, John (MDPI, 2023-09-29)
    The impact of average delivered feedstock cost on the overall financial viability of biorefineries is the focus of this study, and it is explored by modeling the efficient delivery of round bales of herbaceous biomass to a hypothetical biorefinery in the Piedmont, a physiographic region across five states in the Southeastern USA. The complete database (nominal 150,000 Mg/y biorefinery capacity) had 199 satellite storage locations (SSLs) within a 50-km radius of Gretna, a town in South Central Virginia USA, chosen as the biorefinery location. Two additional databases, nominal 50,000 Mg/y (29.1-km radius, 71 SSLs) and nominal 100,000 Mg/y (40-km radius, 133 SSLs) were created, and delivery was simulated for a 24/7 operation, 48 wk/y. The biorefinery capacities were 15.5, 31.1, and 47.3 bales/h for the 50,000, 100,000, and 150,000 Mg/y databases, respectively. Three load-outs operated simultaneously to supply the 15.5 bale/h biorefinery, six for the 31.1 bale/h biorefinery, and nine for the 47.3 bale/h biorefinery. The required truck fleet was three, six, and nine trucks, respectively. The cost for load-out and delivery was 11.63 USD/Mg for the 50,000 Mg/y biorefinery. It increased to 12.46 and 12.99 USD/Mg as the biorefinery capacity doubled to 100,000 Mg/y and tripled to 150,000 Mg/y. Most of the cost increase was due to an increase in truck cost as haul distance increased with the radius of the feedstock supply area. There was a small increase in load-out cost due to an increased cost for travel to support the load-out operations. The less-than-expected increase in average hauling cost for the increase in feedstock production area highlights the influence of efficient scheduling achieved with central control of the truck fleet.
  • Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques
    Jjagwe, Pius; Chandel, Abhilash K.; Langston, David (MDPI, 2023-12-18)
    Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.
  • Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding
    Nourali, Zahra; Shortridge, Julie E.; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L. (MDPI, 2024-01-11)
    Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding.
  • Cyberbiosecurity Workforce Preparation: Education at the Convergence of Education at the Convergence of Cybersecurity and Biosecurity
    Adeoye, Samson; Lindberg, Heather; Bagby, B.; Brown, Anne M.; Batarseh, Feras; Kaufman, Eric K. (2024-01)
    Cyberbiosecurity is an emerging field at the convergence of life sciences and the digital world. As technological advances improve operational processes and expose them to vulnerabilities in agriculture and life sciences, cyberbiosecurity has become increasingly important for addressing contemporary concerns. Unfortunately, at this time, educational opportunities for cyberbiosecurity workforce preparation are limited. Stakeholders’ perceptions may help guide cyberbiosecurity workforce preparation efforts and bridge the gap from the classroom to the field. Toward this end, we identified stakeholders in education, private industry, and state agencies in [State] and sought their input through both an online survey and focus groups. Findings suggest limited awareness and understanding of cyberbiosecurity. Results indicate that both formal and non-formal learning components—including short modules and comprehensive standalone courses—are important for cyberbiosecurity education programming. Stakeholders tied potential success of education programming to systems thinking and collaborative designs. Moreover, results reveal insights into concerns at the convergence of information technology (IT) and operational technology (OT), which is central to effective workforce preparation for today’s agriculture and life sciences professionals. Continuous interdisciplinary collaboration and academia-industry partnerships will be critical for developing robust cyberbiosecurity education and securing the future of agriculture.
  • Towards Estimating the Stiffness of Soft Fruits using a Piezoresistive Tactile Sensor and Neural Network Schemes
    Erukainure, Frank Efe; Parque, Victor; Hassan, Mohsen A.; FathElbab, Ahmed M. R. (IEEE, 2022)
    Measuring the ripeness of fruits is one of the key challenges to enable optimal and just-in-time strategies across the fruit supply chain. In this paper, we study the performance of a tactile sensor to estimate the ground truth of the stiffness of fruits, with kiwifruit as a case study. Our sensor configuration is based on a three-beam cantilever arrangement with piezoresistive elements, enabling the stable acquisition of sensor readings over independent trials. Our estimation scheme is based on the com-pact feed-forward neural networks, allowing us to find effective nonlinear relationships between instantaneous sensor readings and the ground truth of stiffness of fruits. Our experiments using several kiwifruit specimens show the competitive performance frontiers of stiffness approximation using 25 compact feed-forward neural networks, converging to MSE loss at 10-5 across training-validation-testing in most of the cases, and the utmost predictive performance of a pyramidal class of feed-forward architectures. Our results pinpoint the potential to realize robust fruit ripeness measurement with intelligent tactile sensors.
  • Design and experimental testing of a tactile sensor for self-compensation of contact error in soft tissue stiffness measurement
    Erukainure, Frank Efe; Parque, Victor; Hassan, Mohsen A.; FathEl-Bab, Ahmed M. R. (Korean Society for Mechanical Engineers, 2022-10)
    The measurement of viscoelastic properties of soft tissues has become a research interest with applications in the stiffness estimation of soft tissues, sorting and quality control of postharvest fruit, and fruit ripeness estimation. This paper presents a tactile sensor configuration to estimate the stiffness properties of soft tissues, using fruit as case study. Previous stiffness-measuring tactile sensor models suffer from unstable and infinite sensor outputs due to irregularities and inclination angles of soft tissue surfaces. The proposed configuration introduces two low stiffness springs at the extreme ends of the sensor with one high stiffness spring in-between. This study also presents a closed form mathematical model that considers the maximum inclination angle of the tissue’s (fruit) surface, and a finite element analysis to verify the mathematical model, which yielded stable sensor outputs. A prototype of the proposed configuration was fabricated and tested on kiwifruit samples. The experimental tests revealed that the sensor’s output remained stable, finite, and independent on both the inclination angle of the fruit surface and applied displacement of the sensor. The sensor distinguished between kiwifruit at various stiffness and ripeness levels with an output error ranging between 0.18 % and 3.50 %, and a maximum accuracy of 99.81 %, which is reasonable and competitive compared to previous design concepts.
  • Estimating the stiffness of kiwifruit based on the fusion of instantaneous tactile sensor data and machine learning schemes
    Erukainure, Frank Efe; Parque, Victor; Hassan, M. A.; FathEl-Bab, Ahmed M. R. (Elsevier, 2022-10)
    Measuring the ripeness of fruits is one of the critical factors in achieving real-time quality control and sorting of fruit by growers and postharvest managers. However, recent tactile sensing approaches for fruit ripeness detection have suffered setbacks due to: (1) the nonlinear relationship between the sensor output and the true stiffness of fruits; and (2) the angle of contact, referred to as the inclination angle, between the sensor and the outer surface of the fruit. In this paper, we propose a non-destructive tactile sensing approach for estimating the stiffness of fruits, using kiwifruit as a case study. Our sensor configuration is based on a three-probe piezoresistive cantilever beam, allowing us to obtain relatively stable sensor outputs that are independent of the inclination angle of the fruit surface. Our stiffness estimation approach is based on the combination of instantaneous sensor outputs with 63 regression-based machine learning models comprising of neural networks, Gaussian process, support vector machines, and decision trees. For experiments, we used several kiwifruit samples at diverse ripeness levels. The extracted sensor data was used to train the learning models over a 10-fold cross-validation technique, allowing us to find the nonlinear relationships between the instantaneous sensor outputs and the ground truth stiffness of the fruit. Our pairwise statistical comparison by the Wilcoxon test at 5% significance revealed the competitive performance frontiers of our approach for stiffness prediction; the Gaussian process kernel functions and the binary trees outperformed other models at a mean squared error (MSE) of 1.0 and 2×10−23, respectively. Most neural network models achieved competitive learning performance at MSE less than 10−5 and the utmost performance being a pyramidal class of feed-forward neural architectures. The results portray the potential of achieving accurate ripeness estimation of fruit using intelligent tactile sensors with fast machine learning schemes across the supply chain.
  • Review—Prospects in Cancer Diagnosis: Exosome-Chip for Liquid Biopsy
    Khondakar, Kamil Reza; Ataei Kachouei, Matin; Erukainure, Frank Efe; Ali, Md Azahar (The Electrochemical Society, 2023-12-01)
    A liquid biopsy combined with an exosome-chip (EC) is an important detection tool for early cancer diagnosis. Exosomes have a crucial function in the exchange of information between cells and are present in biological fluids. ECs are miniaturized microfluidic devices designed to isolate, capture, and analyze exosomes for analysis of patient samples. Such devices offer on-chip detection, high-throughput analysis, and multiplex measurements. Further, these chips can integrate with electrochemical and optical detectors, and mass spectrometry enabling comprehensive studies of diseases. This review will cover the outlook on chip-based diagnostics for liquid biopsy, detection, and isolation of exosomes to support cancer diagnostics.
  • Prevalence of malaria parasite and its effects on some hematological parameters amongst pregnant women in Yola, Nigeria
    Emmanuel, Blessing Nkechi; Chessed, Godly; Erukainure, Frank Efe; Ekeuhie, Jerry Chima; Philips, Vandi (Springer Nature, 2023-11-15)
    Background: Malaria infection during pregnancy presents a substantial health threat, adversely impacting both the mother and fetus. Its pathogenesis and clinical consequences further complicate diagnosis, treatment, and prevention, particularly in endemic regions. The precise impact of malaria infection on hematological profiles needs to be clearly elucidated, and the occurrence of malaria in expectant mothers still needs to be explored. Consequently, this study aims to assess the prevalence of malaria infection among pregnant women as well as to investigate and correlate the effects of this infection on the hematological parameters of pregnant women in Yola, Nigeria. Methods: A structured hybrid questionnaire was used to gather socio-demographic, clinical, and obstetric data from 100 pregnant women aged 15–45 years. Malaria parasitemia was determined and confirmed using a light microscope, blood smear-staining techniques, and rapid diagnostic tests (RDT). At the same time, the packed cell volume (PCV) was measured using a microhematocrit reader. Also, the complete blood count was determined using Turk’s solution and Neubauer’s counting chamber (hemocytometer). Results: Out of the 100 participants in the study, 76 tested positive for malaria, resulting in a prevalence rate of 76%. The age group between 30 and 34 years and multigravida recorded high values of malaria-infected women, accounting for 18 (23.7%) and 49%, respectively. Also, the study’s findings indicate that malaria-infected pregnant women had a significantly higher occurrence of anemia than those not infected (P = .045). In addition, eosinophil counts, total white blood cells (WBC), and neutrophil count were notably higher in pregnant women infected by malaria compared to those not infected (P < .05). Conversely, lymphocyte count, basophil count, and monocyte count were significantly lower in pregnant women infected by malaria compared to uninfected pregnant women. Conclusion: Pregnant women participating in prenatal care at the Specialist Hospital in Yola, Nigeria, exhibited a relatively high occurrence of malaria parasite infection, and these infected pregnant women displayed a notable change in specific hematological parameters. The findings of this study offer valuable insights into the pathogenesis of malaria during pregnancy and contribute to improved diagnostic and management strategies for pregnant women at risk of malaria infection.
  • Profiling renal dysfunction using Raman chemometric urinalysis, with special reference to COVID19, lupus nephritis, and diabetic nephropathy
    Robertson, John L.; Issa, Amr Sayed; Gomez, Mariana; Sullivan, Kathleen; Senger, Ryan S. (Knowledge Enterprise Journals, 2023-09-30)
    Background: Many systemic and urinary tract diseases alter renal structure and function, including changing the composition of urine. While routine urinalysis (physical properties, sediment evaluation, urine chemistry analytes) is useful in screening, it has limitations on separating disease processes, structural changes, and functional abnormalities. Likewise, while many individual ‘biomarkers’ have been used to screen for disease, they have not met with widespread clinical adoption. The recent COVID19 Pandemic and the recognition of post-acute sequelae SARS-CoV-2 infection (PASC) have highlighted the need for rapid, scalable, economical, and accurate screening tools for managing disease. Aims: Validate a Raman spectroscopy-based screening technology for urine analysis that could be used for recognition and quantification of systemic and renal effects of acute and PASC COVID19 disease. Methods: One hundred ten (110) urine specimens were obtained from consented adults diagnosed with COVID19 disease by RT-PCR and/or proximate (household) contact With RT-PCR-confirmed COVID19 disease. Samples were analyzed using Raman chemometric urinalysis, a technology that detects hundreds of discrete chemicals in urine and applies computational comparison-machine learning to detect COVID19-associated molecular patterns (‘fingerprints’). Results: When compared with the urine multimolecular ‘fingerprints’ of healthy individuals and patients with known systemic diseases (diabetes mellitus, lupus) that alter renal structure and function, patients with acute and PASC COVID19 had unique ‘fingerprints’ indicative of alterations in renal function (i.e. – infection altered urine composition). Differences in disease severity (mild to severe) were reflected by different ‘fingerprints’ in urine. Roughly 20% of hospitalized patients developed a degree of renal dysfunction (decrements in eGFR) that were correlated with distinct changes in urine fingerprints. Conclusion: Raman chemometric urinalysis may be a useful tool in management of patients with COVID19 disease, particularly in detecting patients with evolving renal dysfunction for whom there should be attention to medication use and renal health restoration/preservation.
  • The improvement of wavelet-based multilinear regression for suspended sediment load modeling by considering the physiographic characteristics of the watershed
    Nejatian, Niloofar; Yavary Nia, Mohsen; Yousefyani, Hooshyar; Shacheri, Fatemeh; Yavari Nia, Melika (IWA Publishing, 2023-04)
    The aim of this study is to model a relationship between the amount of the suspended sediment load by considering the physiographic characteristics of the Lake Urmia watershed. For this purpose, the information from different stations was used to develop the sediment estimation models. Ten physiographic characteristics were used as input parameters in the simulation process. The M5 model tree was used to select the most important features. The results showed that the four factors of annual discharge, average annual rainfall, form factor and the average elevation of the watershed were the most important parameters, and the multilinear regression models were created based on these factors. Furthermore, it was concluded that the annual discharge was the most influential parameter. Then, the stations were divided into two homogeneous classes based on the selected features. To improve the efficiency of the M5 model, the non-stationary rainfall and runoff signals were decomposed into sub-signals by the wavelet transform (WT). By this technique, the available trends of the main raw signals were eliminated. Finally, the models were developed by multilinear regressions. The model using all four factors had the best performance (DC = 0.93, RMSE = 0.03, ME = 0.05 and RE = 0.15).
  • ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems
    Batarseh, Feras; Kulkarni, Ajay; Sreng, Chhayly; Lin, Justice; Maksud, Siam (2023-10-05)
    This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water resources’ management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources’ management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios.
  • Modelling Specific Energy Requirement for a Power-Operated Vertical Axis Rotor Type Intra-Row Weeding Tool Using Artificial Neural Network
    Kumar, Satya Prakash; Tewari, V. K.; Chandel, Abhilash Kumar; Mehta, C. R.; Pareek, C. M.; Chethan, C. R.; Nare, Brajesh (MDPI, 2023-09-07)
    Specific energy prediction is critically important to enhance field performance of agricultural implements. It enables optimal utilization of tractor power, reduced inefficiencies, and identification of comprehensive inputs for designing energy-efficient implements. In this study, A 3-5-1 artificial neural network (ANN) model was developed to estimate specific energy requirement of a vertical axis rotor type intra-row weeding tool. The depth of operation in soil bed, soil cone index, and forward/implement speed ratio (u/v) were selected as the input variables. Soil bin investigations were conducted using the vertical axis rotor (RVA), interfaced with draft, torque, speed sensors, and data acquisition system to record dynamic forces employed during soil–tool interaction at ranges of different operating parameters. The depth of operation (DO) had the maximum influence on the specific energy requirement of the RVA, followed by the cone index (CI) and the u/v ratio. The developed ANN model was able to predict the specific energy requirements of RVA at high accuracies as indicated by high R2 (0.91), low RMSE (0.0197) and low MAE (0.0479). Findings highlight the potential of the ANN as an efficient technique for modeling soil–tool interactions under specific experimental conditions. Such estimations will eventually optimize and enhance the performance efficiency of agricultural implements in the field.
  • Metagenomic Analysis of a Continuous-Flow Aerobic Granulation System for Wastewater Treatment
    Gomeiz, Alison T.; Sun, Yewei; Newborn, Aaron; Wang, Zhi-Wu; Angelotti, Bob; Van Aken, Benoit (MDPI, 2023-09-15)
    Aerobic granulation is an emerging process in wastewater treatment that has the potential to accelerate sedimentation of the microbial biomass during secondary treatment. Aerobic granulation has been difficult to achieve in the continuous flow reactors (CFRs) used in modern wastewater treatment plants. Recent research has demonstrated that the alternation of nutrient-abundant (feast) and nutrient-limiting (famine) conditions is able to promote aerobic granulation in a CFR. In this study, we conducted a metagenomic analysis with the objective of characterizing the bacterial composition of the granular biomass developed in three simulated plug flow reactors (PFRs) with different feast-to-famine ratios. Phylogenetic analyses revealed a clear distinction between the bacterial composition of aerobic granules in the pilot simulated PFRs as compared with conventional activated sludge. Larger and denser granules, showing improved sedimentation properties, were observed in the PFR with the longest famine time and were characterized by a greater proportion of bacteria producing abundant extracellular polymeric substances (EPS). Functional metagenomic analysis based on KEGG pathways indicated that the large and dense aerobic granules in the PFR with the longest famine time showed increased functionalities related to secretion systems and quorum sensing, which are characteristics of bacteria in biofilms and aerobic granules. This study contributes to a further understanding of the relationship between aerobic granule morphology and the bacterial composition of the granular biomass.
  • Hyper-Progressive Single Shot Detector (HPSSD) Algorithm for Door Panel Type-B Detection
    Yuan, Hao; Okpor, Samuel Ita; Kong, Xiao; Erukainure, Frank Efe; Wilson, Samuel Britwum (IEEE, 2023-09)
    In an automobile, the door panel type-B constitutes the interior compartment of the door, primarily composed of screws and white installations forming its structural framework. However, automated manufacturing and maintenance procedures often struggle to accurately detect these components due to their pronounced resemblance to other elements on the panel. Computer vision techniques present a viable solution to this challenge. In this paper, we propose the Hyper-Progressive Single Shot Detector (HPSSD), an object detection algorithm designed to address the aforementioned challenge. Our proposed HPSSD builds on the Single Shot Detector (SSD) algorithm and introduces several enhancements to improve its detection capabilities. The first modification involves replacing the VGG-16 backbone with a ResNet-50 module. Furthermore, we incorporated the Residual Convolutional Block Attention Mechanism (RCBAM) to boost the algorithm’s functionality. To enlarge the receptive fields of each pixel–an essential step for enhancing detection accuracy–we executed multi-dilated convolutions. In the final stage of our development process, we embedded a three-stage progressive attention mechanism (PAM). The PAM is instrumental in generating refined feature maps, which serve as the foundation for precise object detection on the door panel dataset comprising 1200 images. After running 50k iterations on the door panel dataset, the HPSSD displayed a promising mean average precision of 98.2% at a speed of 21 frames per second (FPS). Our results suggest that the HPSSD, with its ability to deliver real-time, accurate detection, is an ideal tool for improving the quality inspection of door panels in smart factories.
  • The importance of time and space in biogeochemical heterogeneity and processing along the reservoir ecosystem continuum
    Woelmer, Whitney M.; Hounshell, Alexandria G.; Lofton, Mary E.; Wander, Heather L.; Lewis, Abigail S. L.; Scott, Durelle T.; Carey, Cayelan C. (Springer, 2023-04)
    Globally significant quantities of carbon (C), nitrogen (N), and phosphorus (P) enter freshwater reservoirs each year. These inputs can be buried in sediments, respired, taken up by organisms, emitted to the atmosphere, or exported downstream. While much is known about reservoir-scale biogeochemical processing, less is known about spatial and temporal variability of biogeochemistry within a reservoir along the continuum from inflowing streams to the dam. To address this gap, we examined longitudinal variability in surface water biogeochemistry (C, N, and P) in two small reservoirs throughout a thermally stratified season. We sampled total and dissolved fractions of C, N, and P, as well as chlorophyll-a from each reservoir's major inflows to the dam. We found that heterogeneity in biogeochemical concentrations was greater over time than space. However, dissolved nutrient and organic carbon concentrations had high site-to-site variability within both reservoirs, potentially as a result of shifting biological activity or environmental conditions. When considering spatially explicit processing, we found that certain locations within the reservoir, most often the stream-reservoir interface, acted as "hotspots" of change in biogeochemical concentrations. Our study suggests that spatially explicit metrics of biogeochemical processing could help constrain the role of reservoirs in C, N, and P cycles in the landscape. Ultimately, our results highlight that biogeochemical heterogeneity in small reservoirs may be more variable over time than space, and that some sites within reservoirs play critically important roles in whole-ecosystem biogeochemical processing.
  • The impact of nitrogen treatment and short-term weather forecast data in irrigation scheduling of corn and cotton on water and nutrient use efficiency in humid climates
    Sangha, Laljeet; Shortridge, Julie; Frame, William (Elsevier, 2023-06)
    Irrigation adoption is increasing in humid regions to offset short-term dry periods, especially at the peak of the growing season. Low soil moisture at the peak growth stage impacts yield and limits the plant's capacity to uptake nitrogen, resulting in low nutrient use efficiency (NUE). However, heavy rainfall on fields with supple-mental irrigation may result in waterlogging and surface runoff, leading to nutrient leaching and runoff. This ultimately can lead to lower NUE, poor water use efficiency (WUE), reduced yields, and water quality impacts. This makes irrigation management challenging in humid regions, as irrigators must avoid both limited and excess water conditions. This field study aimed to develop and test an irrigation management methodology using real-time soil water availability, crop physiological status, water needs, and short-term weather forecasts information from National Weather Service. A rule-based approach determined by soil moisture depletion and short-term weather forecasts was used to trigger irrigation to avoid both stress and excess water conditions. This method was tested in two years of field trials in Suffolk, Virginia to quantify its impacts on yield, NUE, WUE, and financial returns in corn and cotton under four nitrogen application treatments. The relative impact of irrigation and nitrogen treatment was quantified using mixed effects models. The yield, NUE and WUE were impacted by both precipitation and irrigation patterns. Significantly different yields were observed under Nrates treatments for both corn and cotton. The trends of economic returns were similar to yield and were significantly different between recent and historic prices. This study also discusses the impacts of reliability and practical challenges of using Weather Informed irrigation in a field study.
  • Engineering the Metabolic Profile of Clostridium cellulolyticum with Genomic DNA Libraries
    Freedman, Benjamin G.; Lee, Parker W.; Senger, Ryan S. (MDPI, 2023-06-27)
    Clostridium cellulolyticum H10 (ATCC 35319) has the ability to ferment cellulosic substrates into ethanol and weak acids. The growth and alcohol production rates of the wild-type organism are low and, therefore, targets of metabolic engineering. A genomic DNA expression library was produced by a novel application of degenerate oligonucleotide primed PCR (DOP-PCR) and was serially enriched in C. cellulolyticum grown on cellobiose in effort to produce fast-growing and productive strains. The DNA library produced from DOP-PCR contained gene-sized DNA fragments from the C. cellulolyticum genome and from the metagenome of a stream bank soil sample. The resulting enrichment yielded a conserved phage structural protein fragment (part of Ccel_2823) from the C. cellulolyticum genome that, when overexpressed alone, enabled the organism to increase the ethanol yield by 250% compared to the plasmid control strain. The engineered strain showed a reduced production of lactate and a 250% increased yield of secreted pyruvate. Significant changes in growth rate were not seen in this engineered strain, and it is possible that the enriched protein fragment may be combined with the existing rational metabolic engineering strategies to yield further high-performing cellulolytic strains.
  • Effects of Nitrate Recycle on the Sludge Densification in Plug-Flow Bioreactors Fed with Real Domestic Wastewater
    Wang, Jie-Fu; An, Zhao-Hui; Zhang, Xue-Yao; Angelotti, Bob; Brooks, Matt; Wang, Zhi-Wu (MDPI, 2023-06-22)
    The impact of adding a modified Ludzack–Ettinger (MLE) configuration with Nitrate Recycle (NRCY) on continuous-flow aerobic granulation has yet to be explored. The potential negative effects of MLE on sludge densification include that: (1) bioflocs brought by NRCY could compete with granules in feast zones; and (2) carbon addition to anoxic zones could increase the system organic loading rates and lead to higher feast-to-famine ratios. Two pilot-scale plug flow reactor (PFR) systems fed with real domestic wastewater were set up onsite to test these hypotheses. The results showed that MLE configuration with NRCY could hinder the sludge granulation, but the hindrance could be alleviated by the NRCY location change which to some extent also compensates for the negative effect of higher feast-to-famine ratios due to carbon addition in MLE. This NRCY location change can be advantageous to drive sludge densification without a radical washout of the sludge inventory, and had no effects on the chemical oxygen demand (COD) and nitrogen removal efficiencies. The PFR pilot design for the MLE process with a modified NRCY location tested in this study could be developed as an alternative to hydrocyclones for full-scale, greenfield, continuous sludge densification applications.