Scholarly Works, Biological Systems Engineering

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Research articles, presentations, and other scholarship

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The State of the Science and Practice of Stream Restoration in the Chesapeake: Lessons Learned to Inform Better Implementation, Assessment, and Outcomes
Noe, Gregory; Law, Neely; Berg, Joe; Filoso, Solange; Drescher, Sadie; Fraley-McNeal, Lisa; Hayes, Ben; Mayer, Paul; Ruck, Chris; Stack, Bill; Starr, Rich; Stranko, Scott; Thompson, Theresa M. (2024-11-04)
Physical Cell Disruption Technologies for Intracellular Compound Extraction from Microorganisms
Zhao, Fujunzhu; Wang, Zhiwu; Huang, Haibo (MDPI, 2024-09-24)
This review focuses on the physical disruption techniques in extracting intracellular compounds, a critical step that significantly impacts yield and purity. Traditional chemical extraction methods, though long-established, face challenges related to cost and environmental sustainability. In response to these limitations, this paper highlights the growing shift towards physical disruption methods—high-pressure homogenization, ultrasonication, milling, and pulsed electric fields—as promising alternatives. These methods are applicable across various cell types, including bacteria, yeast, and algae. Physical disruption techniques achieve relatively high yields without degrading the bioactivity of the compounds. These techniques, utilizing physical forces to break cell membranes, offer promising extraction efficiency, with reduced environmental impacts, making them attractive options for sustainable and effective intracellular compound extraction. High-pressure homogenization is particularly effective for large-scale extracting of bioactive compounds from cultivated microbial cells. Ultrasonication is well-suited for small to medium-scale applications, especially for extracting heat-sensitive compounds. Milling is advantageous for tough-walled cells, while pulsed electric field offers gentle, non-thermal, and highly selective extraction. This review compares the advantages and limitations of each method, emphasizing its potential for recovering various intracellular compounds. Additionally, it identifies key research challenges that need to be addressed to advance the field of physical extractions.
Ajna: A Wearable Shared Perception System for Extreme Sensemaking
Wilchek, Matthew; Luther, Kurt; Batarseh, Feras A. (ACM, 2024)
This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users? relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.
Studying the Relationship between Satellite-Derived Evapotranspiration and Crop Yield: A Case Study of the Cauvery River Basin
Anand, Anish; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2024-08-05)
Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management.
Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning
Jjagwe, Pius; Chandel, Abhilash K.; Langston, David B. (MDPI, 2024-06-24)
Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = −0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59–0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train–test data split ratios (50:50–95:5). The ML models and train–test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84–0.97, rRMSE: 8.72–20%), while a higher train–test split ratio lowered the performances of the ML models. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity.
Thermal-RGB Imagery and Computer Vision for Water Stress Identification of Okra (Abelmoschus esculentus L.)
Rajwade, Yogesh A.; Chandel, Narendra S.; Chandel, Abhilash K.; Singh, Satish Kumar; Dubey, Kumkum; Subeesh, A.; Chaudhary, V. P.; Ramanna Rao, K. V.; Manjhi, Monika (MDPI, 2024-06-27)
Crop canopy temperature has proven beneficial for qualitative and quantitative assessment of plants' biotic and abiotic stresses. In this two-year study, water stress identification in okra crops was evaluated using thermal-RGB imaging and AI approaches. Experimental trials were developed for two irrigation types, sprinkler and flood, and four deficit treatment levels (100, 50, 75, and 25% crop evapotranspiration), replicated thrice. A total of 3200 thermal and RGB images acquired from different crop stages were processed using convolutional neural network architecture-based deep learning models (1) ResNet-50 and (2) MobileNetV2. On evaluation, the accuracy of water stress identification was higher with thermal imagery inputs (87.9% and 84.3%) compared to RGB imagery (78.6% and 74.1%) with ResNet-50 and MobileNetV2 models, respectively. In addition, irrigation treatment and levels had significant impact on yield and crop water use efficiency; the maximum yield of 10,666 kg ha⁻¹ and crop water use efficiency of 1.16 kg m⁻³ was recorded for flood irrigation, while 9876 kg ha⁻¹ and 1.24 kg m⁻³ were observed for sprinkler irrigation at 100% irrigation level. Developments and observations from this study not only suggest applications of thermal-RGB imagery with AI for water stress quantification but also developing and deploying automated irrigation systems for higher crop water use efficiency.
Valorization of Seafood Waste for Food Packaging Development
Zhan, Zhijing; Feng, Yiming; Zhao, Jikai; Qiao, Mingyu; Jin, Qing (MDPI, 2024-07-03)
Packaging plays a crucial role in protecting food by providing excellent mechanical properties as well as effectively blocking water vapor, oxygen, oil, and other contaminants. The low degradation of widely used petroleum-based plastics leads to environmental pollution and poses health risks. This has drawn interest in renewable biopolymers as sustainable alternatives. The seafood industry generates significant waste that is rich in bioactive substances like chitin, chitosan, gelatins, and alginate, which can replace synthetic polymers in food packaging. Although biopolymers offer biodegradability, biocompatibility, and non-toxicity, their films often lack mechanical and barrier properties compared with synthetic polymer films. This comprehensive review discusses the chemical structure, characteristics, and extraction methods of biopolymers derived from seafood waste and their usage in the packaging area as reinforcement or base materials to guide researchers toward successful plastics replacement and commercialization. Our review highlights recent advancements in improving the thermal durability, mechanical strength, and barrier properties of seafood waste-derived packaging, explores the mechanisms behind these improvements, and briefly mentions the antimicrobial activities and mechanisms gained from these biopolymers. In addition, the remaining challenges and future directions for using seafood waste-derived biopolymers for packaging are discussed. This review aims to guide ongoing efforts to develop seafood waste-derived biopolymer films that can ultimately replace traditional plastic packaging.
Assessing the Efficacy of Stream Restoration and SCM Retrofitting for Channel Stability in Urbanized Catchments
Towsif Khan, Sami; Thompson, Theresa M.; Sample, David J. (2024-05-29)
The hydrological benefits of catchment-scale implementation of stormwater control measures (SCMs) in mitigating the adverse effects of urbanization are well established. Nevertheless, recent studies indicate that Maryland's stormwater regulations, mandating the combined use of distributed and end-of-pipe SCMs, fall short in maintaining channel stability, despite their effectiveness in reducing runoff from impervious surfaces. The study objective was to evaluate the incremental impact of SCM retrofits and stream restoration on channel stability in a small, urbanized catchment (0.9 sq. km) in Montgomery County, Maryland, USA. This study employed a refined, well-calibrated, coupled hierarchical modeling approach, integrating a watershed-scale Storm Water Management Model (SWMM) with the Hydrologic Engineering Centers River Analysis System (HEC-RAS). A comprehensive methodology was developed using the calibrated SWMM and HEC-RAS models. The modeling results revealed that only retrofitting SCMs with multi-stage outlet structures designed to maintain the pre-development mobility of bed particles may not effectively reduce channel degradation. Conversely, stream restoration practices, including the removal of legacy sediments from the floodplain, significantly mitigated channel instability. Notably, the combination of SCM retrofitting, aimed at matching the sediment transport capacity of the predevelopment state, and stream restoration practices did not yield better results compared to stream restoration alone. This finding suggests that for streams impacted by legacy sediments, floodplain restoration alone might suffice to achieve channel stability, eliminating the need to retrofit SCMs designed under existing regulations.
Cost Comparison for Emerging Technologies to Haul Round Bales for the Biorefinery Industry
Cundiff, John S.; Grisso, Robert D.; Webb, Erin G. (MDPI, 2024-05-30)
Between 20 and 30% of the feedstock delivered cost is the highway hauling. In order to achieve maximum truck productivity, and thus minimize hauling cost, the hauling technology needs to provide for rapid loading and unloading. Three prototype technologies have been proposed to address the hauling issue. The first was developed by Stinger to secure a load of large rectangular bales, and it is identified as the Advanced Load Securing System (ALSS). For this study, the ALSS technology is applied on two trailers hooked in tandem (ALSS-2) loaded with 20 bales each. The second technology (Cable), is a cable system for securing a load of bales (round or rectangular) on a standard flatbed trailer. With the third technology (Rack), bales are loaded into a 20-bale rack at an SSL, and this rack is unloaded as a unit at the biorefinery. Bales remain in the rack until processed, thus avoiding single-bale handling at the receiving facility. A cost comparison, which begins with bales in single-layer ambient storage in SSLs and ends with bales in single file on a conveyor into the biorefinery, was done for the three hauling technologies paired with three load-out technologies. Cost for the nine options ranged from 48.56 USD/Mg (11 load-outs, Cable hauling) to 34.90 USD/Mg (8 loads-outs, ALSS-2 hauling). The most significant cost issue was the reduction in truck cost; 25.54 USD/Mg (20 trucks, Cable) and 15.15 USD/Mg (10 trucks, Rack).
Do Maryland's Stormwater Management Regulations Protect Channel Stability?
Thompson, Theresa M.; Sample, David J.; Al-Samdi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew; Butcher, Jon (2024-06-20)
Webinar for the Maryland Stream Restoration Association. 84 participants
Effectiveness of stormwater management practices in protecting stream channel stability
Thompson, Theresa M.; Sample, David J.; Al-Smadi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew (2024-06-11)
Presentation made as part of the Stream Restoration Webinar Series: Finding Common Ground. Webinar had 284 participants.
Cost-Effective Methods for Reducing Sediment Loads in the Lick Run Watershed
Thompson, Theresa M.; Sample, David J.; Stephenson, Stephen Kurt; Towsif Khan, Sami; Macdonald, Kiara (2024-05-15)
Potential for juvenile freshwater mussels to settle onto riverbeds from field investigation
Sumaiya, Sumaiya; Czuba, Jonathan A.; Russ, William T.; Hoch, Rachael (Taylor & Francis, 2024-05-02)
Freshwater mussel populations have been declining at an alarming rate around the world. Herein, we investigate whether changing flow conditions, as they affect juvenile freshwater mussel settling, could be a potential causative factor for this decline in the Dan River, North Carolina, USA. We deployed two uplooking velocity sensors on the riverbed between May and November 2019: one where mussels reside and another where they do not. From this data, we calculated shear velocity, which is a measure of the turbulence that acts to lift particles into suspension and acts against particle settling. We determined that a shear velocity less than 0.66 cm/s would be required to settle relatively large and dense juvenile mussels onto the riverbed; however, the lowest shear velocity we measured was 0.9 cm/s. Additionally, we estimated that juvenile freshwater mussels as large as 280-510 µm could always be suspended and not be able to settle onto the riverbed at these two locations. Therefore, the flow during May-November 2019 was high enough to prohibit recruitment of juvenile freshwater mussels at the sensor locations. Furthermore, we have identified that the magnitude of the lowest flows has increased since 2000, which may be exacerbating the decline in freshwater mussels.
Channel Morphology Change after Restoration: Drone Laser Scanning versus Traditional Surveying Techniques
Resop, Jonathan P.; Hendrix, Coral; Wynn-Thompson, Theresa; Hession, W. Cully (MDPI, 2024-04-10)
Accurate and precise measures of channel morphology are important when monitoring a stream post-restoration to determine changes in stability, water quality, and aquatic habitat availability. Practitioners often rely on traditional surveying methods such as a total station for measuring channel metrics (e.g., cross-sectional area, width, depth, and slope). However, these methods have limitations in terms of coarse sampling densities and time-intensive field efforts. Drone-based lidar or drone laser scanning (DLS) provides much higher resolution point clouds and has the potential to improve post-restoration monitoring efforts. For this study, a 1.3-km reach of Stroubles Creek (Blacksburg, VA, USA), which underwent a restoration in 2010, was surveyed twice with a total station (2010 and 2021) and twice with DLS (2017 and 2021). The initial restoration was divided into three treatment reaches: T1 (livestock exclusion), T2 (livestock exclusion and bank treatment), and T3 (livestock exclusion, bank treatment, and inset floodplain). Cross-sectional channel morphology metrics were extracted from the 2021 DLS scan and compared to metrics calculated from the 2021 total station survey. DLS produced 6.5 times the number of cross sections over the study reach and 8.8 times the number of points per cross section compared to the total station. There was good agreement between the metrics derived from both surveying methods, such as channel width (R² = 0.672) and cross-sectional area (R² = 0.597). As a proof of concept to demonstrate the advantage of DLS over traditional surveying, 0.1 m digital terrain models (DTMs) were generated from the DLS data. Based on the drone lidar data, from 2017 to 2021, treatment reach T3 showed the most stability, in terms of the least change and variability in cross-sectional metrics as well as the least erosion area and volume per length of reach.
Sediment Pollution in Sinking Creek from MVP activities
Czuba, Jonathan A.; Pitt, Donna; Nelson, Amy; Malbon, Elizabeth S. (New River Symposium, 2024-04-12)
For over 10 days, sediment from a highly turbid spring, affected by activities for the Mountain Valley Pipeline (MVP), entered into Sinking Creek, a tributary of the New River. This presentation will describe what is known about the incident, to what extent the impact on Sinking Creek can be assessed with available information, and what is unknown that limits a full impact assessment. This presentation will mostly focus on quantifying the transport and fate of sediment delivered to Sinking Creek between January 27th and February 6th prior to sediment control efforts. This presentation will also highlight what is not known and what limits a full impact assessment.
Assessment of Recycled and Manufactured Adsorptive Materials for Phosphate Removal from Municipal Wastewater
Drummond, Deja; Brink, Shannon; Bell, Natasha (UCOWR, 2024)
Elevated concentrations of phosphorus (P) and other nutrients common in wastewater treatment plant (WWTP) effluent have been shown to contribute to the proliferation of harmful algal blooms, which may lead to fish kills related to aquatic hypoxia. Increased understanding of the negative effects associated with elevated P concentrations have prompted more strict regulation of WWTP effluent in recent years. The use of low-cost and potentially regenerative adsorptive phosphate filters has the potential to decrease P concentrations in WWTP effluent released to natural waters. This research focuses on assessing the capacities of recycled concrete aggregate (RCA), expanded slate, and expanded clay to remove phosphate from P-amended WWTP effluent. Results from a flow-through column study indicate that RCA consistently removed an average of 97% of phosphate over 20 weeks of continuous flow at an 8-hour hydraulic retention time (HRT). Expanded clay removed an average of 63% of introduced phosphate but decreased in removal capacity from 91 to 42% over the 20-week duration. Sorption data from batch studies were fitted to Langmuir models and RCA was shown to have the highest maximum sorption capacity (6.16 mg P/g), followed by expanded clay (3.65 mg P/g). RCA and expanded clay are promising options for use in passive filters for further reduction of phosphate from WWTP effluent.
Incidence of Per-And Polyfluoroalkyl Substances (PFAS) in Private Drinking Water Supplies in Southwest Virginia, USA
Hohweiler, Kathleen; Krometis, Leigh-Anne H.; Ling, Erin; Xia, Kang (2024)
Per- and polyfluoroalkyl substances (PFAS) are a class of man-made contaminants of increasing human health concern due to their resistance to degradation, widespread environmental occurrence, bioaccumulation in organ tissue, and potential negative health impacts. Private drinking water supplies may be uniquely vulnerable to PFAS contamination, as these systems are not subject to federal regulations and often include limited treatment prior to use. The goal of this study was to determine the incidence of PFAS contamination in private drinking water supplies in two counties in Southwest Virginia, USA (Floyd and Roanoke), and to examine the potential for reliance on citizen-science based strategies for sample collection in subsequent broader efforts. Samples for inorganic ions, bacteria, and PFAS analysis were collected on separate occasions by participants and experts at the home drinking water point of use (POU) for comparison. Experts also collected outside tap samples for PFAS analysis. At least one PFAS was detectable in 88% of POU samples collected (n=60), with an average total PFAS concentration of 23.5±30.8 ppt. PFOA and PFOS, two PFAS compounds which presently have EPA health advisories, were detectable in 13% and 22% of POU samples, respectively. Of the 31 compounds targeted, 15 were detectable in at least one sample. On average, each POU sample contained approximately 3.3 PFAS compounds, and one sample contained as many as 8 compounds, indicating that exposure to a mixture of PFAS in drinking water may be occurring. Although there were significant differences in total PFAS concentrations between expert and participant collected samples (Wilcoxon, alpha = 0.05), collector bias was inconsistent, and may be due to the time of day of sampling (i.e. morning, afternoon) or specific attributes of a given home. Future studies reliant on participant collection of samples appear possible given proper training, coordination, and instruction.
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 B. (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 (r_train: 0.97, rRMSE_train: 1.17%, r_entire: 0.95, rRMSE_entire: 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.

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