Scholarly Works, Biological Systems Engineering

Permanent URI for this collection

https://hdl.handle.net/10919/24302

Research articles, presentations, and other scholarship

Browse

Now showing 1 - 20 of 445

Automated Calibration of SWMM for Improved Stormwater Model Development and Application
Ahmadi, Hossein; Scott, Durelle T.; Sample, David J.; Shahed Behrouz, Mina (MDPI, 2025-05-25)
The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. The Storm Water Management Model (SWMM) is widely used for simulating runoff in urban watersheds. However, calibration of SWMM, as with all hydrologic models, is often plagued with issues such as subjectivity, and an abundance of model parameters, leading to delays and inefficiencies in model development and application. Further development of modeling and simulation tools to aid in design is critical in improving the function of stormwater management systems. To address these issues, we developed an integration of PySWMM (a Python wrapper (tool) for SWMM) and Pymoo (a Python package for multi-objective optimization) to automate the SWMM calibration process. The tool was tested using a case study urban watershed in Fredericksburg, VA. This tool can employ either a single-objective or multi-objective approach to calibrate a SWMM model by minimizing the error between prediction and observed values. This tool uses performance metrics including Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Error (RMSE) Standardized Ratio (RSR) for both single-event and long-term continuous rainfall-runoff processes. During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. Additionally, in the single-objective optimization test case, the model yielded NSE values of 0.68 and 0.73 for the calibration and validation, respectively. The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. The tool successfully calibrated the SWMM model, delivering reliable results with suitable computational performance.
Implementation of Modular Depot Concept for Switchgrass Pellet Production in the Piedmont
Resop, Jonathan P.; Cundiff, John S.; Sokhansanj, Shahabaddine (MDPI, 2025-06-12)
In the bioenergy industry, highway hauling cost is typically 30%, or more, of the average cost of feedstock delivered to a biorefinery. Thus, truck productivity, in terms of Mg/d/truck, is a key issue in the design of a logistics system. One possible solution to this problem that is being explored is the utilization of modular pellet depots. In such a logistics system, raw biomass (i.e., low-bulk-density product) is converted into pellets (i.e., high-bulk-density product) by several smaller-scale modular pellet depots instead of by a single larger-capacity pellet depot. A truckload of raw biomass (e.g., round bales) is 16 Mg and a load of pellets is 34 Mg. The distribution of depots across a feedstock production area can potentially have an impact on the total truck operating hours (i.e., raw biomass hauling to a depot + pellet hauling from the depot to the biorefinery) required to deliver feedstock for annual operation of a biorefinery. This study examined three different distributions of depots across five feedstock production areas. The numbers of depots were one, two, and four per production area for totals of five, ten, and twenty depots. Increasing the number of depots from five to ten reduced raw biomass hauling hours by 12%, and increasing from five to twenty reduced these hours by 30%. Total hauling hours (raw biomass + pellets) were reduced by less than 1% with an increase from five to ten and by about 11% with an increase from five to twenty. The modular pellet depot concept demonstrated potential for providing improvements to biorefinery logistics systems, but more research is needed to optimize this balance.
Hydrologic and Hydraulic Modeling for Flood Risk Assessment: A Case Study of Periyar River Basin, Kerala, India
Renu, S.; Reddy, Beeram Satya Narayana; Santhosh, Sanjana; Sreelekshmi,; Lekshmi, V.; Pramada, S. K.; Sridhar, Venkataramana (MDPI, 2025-06-18)
Floods pose a substantial threat to both life and property, with their frequency and intensity escalating due to climate change. A comprehensive hydrological and hydraulic modeling approach is essential for understanding flood dynamics and developing effective future flood risk management strategies. The accuracy of Digital Elevation Models (DEMs) directly impacts the reliability of hydrologic simulations. This study focuses on evaluating the efficacy of two DEMs in hydrological modeling, specifically investigating their potential for daily discharge simulation in the Periyar River Basin, Kerala, India. Recognizing the limitations of the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) with the available dataset, a novel hybrid model was developed by integrating HEC-HMS outputs with an Artificial Neural Network (ANN). While precipitation, lagged precipitation, and lagged discharge served as inputs to the ANN, the hybrid model also incorporated HEC-HMS simulations as an additional input. The results demonstrated improved performance of the hybrid model in simulating daily discharge. The Hydrologic Engineering Center’s River Analysis System (HEC-RAS) was employed to predict flood inundation areas for both historical and future scenarios in the Aluva region of the Periyar River Basin, which was severely impacted during the 2018 Kerala floods. By integrating hydrological and hydraulic modeling approaches, this study aims to enhance flood prediction accuracy and contribute to the development of effective flood mitigation strategies.
Canopy Transpiration Mapping in an Apple Orchard Using High-Resolution Airborne Spectral and Thermal Imagery with Weather Data
Chandel, Abhilash K.; Khot, Lav R.; Stöckle, Claudio O.; Kalcsits, Lee; Mantle, Steve; Rathnayake, Anura P.; Peters, Troy R. (MDPI, 2025-05-14)
Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management.
Performance Evaluation of Numerical Weather Prediction Models in Forecasting Rainfall Events in Kerala, India
Nitha, V.; Pramada, S. K.; Praseed, N. S.; Sridhar, Venkataramana (MDPI, 2025-03-25)
Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for minimizing the impact of floods on life, infrastructure, and agriculture. For accurate forecasting of heavy rainfall events in this region, region-specific evaluations of NWP model performance are very important. This study evaluated the performance of six Numerical Weather Prediction (NWP) models—NCEP, NCMRWF, ECMWF, CMA, UKMO, and JMA—in forecasting heavy rainfall events in Kerala. A comprehensive assessment of these models was performed using traditional performance metrics, categorical precipitation metrics, and Fractional Skill Scores (FSSs) across different forecast lead times. FSSs were calculated for different rainfall thresholds (100 mm, 50 mm, 5 mm). The results reveal that all models captured rainfall patterns well for the lower threshold of 5 mm, but most of the models struggled to accurately forecast heavy rainfall, especially for longer lead times. JMA performed well overall in most of the metrics except False Alarm Ratio (FAR). It showed high FAR, which revealed that it may predict false rainfall events. ECMWF demonstrated consistent performance. NCEP and UKMO performed moderately well. CMA, and NCMRWF had the lowest accuracy either due to more errors or biases. The findings underscore the trade-offs in model performance, suggesting that model selection should depend on the accuracy required or rainfall event prediction capability. This study recommends the use of Multi-Model Ensembles (MME) to improve forecasting accuracy, integrate the strengths of the best-performing models, and reduce biases. Future research can also focus on expanding observational networks and employing advanced data assimilation techniques for more reliable predictions, particularly in regions with complex terrain such as Kerala.
Feasibility of Little Cherry/X-Disease Detection in Prunus avium Using Field Asymmetric Ion Mobility Spectrometry
Kothawade, Gajanan S.; Khot, Lav R.; Chandel, Abhilash K.; Molnar, Cody; Harper, Scott J.; Wright, Alice A. (MDPI, 2025-03-25)
Little cherry disease (LCD) and X-disease have critically impacted the Pacific Northwest sweet cherry (Prunus avium) industry. Current detection methods rely on laborious visual scouting or molecular analyses. This study evaluates the suitability of field asymmetric ion mobility spectrometry (FAIMS) for rapid detection of LCD and X-disease infection in three sweet cherry cultivars (‘Benton’, ‘Cristalina’, and ‘Tieton’) at the post-harvest stage. Stem cuttings with leaves were collected from commercial orchards and greenhouse trees. FAIMS operated at 1.5 L/min and 50 kPa, was used for headspace analysis. Molecular analyses confirmed symptomatic and asymptomatic samples. FAIMS data were processed for ion current sum (I_sum), maximum ion current (I_max), and area under the curve (I_AUC). Symptomatic samples showed higher ion currents in specific FAIMS regions (p < 0.05), with clear differences between symptomatic and asymptomatic samples across compensation voltage and dispersion field ranges. Cultivar-specific variation was also observed in the data. FAIMS spectra for LCD/X-disease symptomatic samples differed from those for asymptomatic samples in other Prunus species, such as peach and nectarines. These findings support FAIMS as a potential diagnostic tool for LCD/X disease. Further studies with controlled variables and key growth stages are recommended to realize early-stage detection.
KHAIT: K-9 Handler Artificial Intelligence Teaming for Collaborative Sensemaking
Wilchek, Matthew; Wang, Linhan; Dickinson, Sally; Feuerbacher, Erica N.; Luther, Kurt; Batarseh, Feras A. (ACM, 2025-03-24)
In urban search and rescue (USAR) operations, communication between handlers and specially trained canines is crucial but often complicated by challenging environments and the specific behaviors canines are trained to exhibit when detecting a person. Since a USAR canine often works out of sight of the handler, the handler lacks awareness of the canine’s location and situation, known as the “sensemaking gap.” In this paper, we propose KHAIT, a novel approach to close the sensemaking gap and enhance USAR effectiveness by integrating object detection-based Artificial Intelligence (AI) and Augmented Reality (AR). Equipped with AI-powered cameras, edge computing, and AR headsets, KHAIT enables precise and rapid object detection from a canine’s perspective, improving survivor localization. We evaluate this approach in a real-world USAR environment, demonstrating an average survival allocation time decrease of 22%, enhancing the speed and accuracy of operations.
Ground–Surface Water Assessment for Agricultural Land Prioritization in the Upper Kansai Basin, India: An Integrated SWAT-VIKOR Framework Approach
Halder, Sudipto; Banerjee, Santanu; Youssef, Youssef M.; Chandel, Abhilash; Alarifi, Nassir; Bhandari, Gupinath; Abd-Elmaboud, Mahmoud E. (MDPI, 2025-03-19)
Prioritizing agricultural land use is a significant challenge for sustainable development in the rapidly urbanizing, semi-arid riverine basins of South Asia, especially under climate variability and water scarcity. This study introduces a systematic framework combining remote sensing and geospatial data with the Soil and Water Assessment Tool (SWAT) model, morphometric analysis, and VIKOR-based Multi-Criteria Decision Analysis (MCDA) to effectively identify Agricultural Land Prioritization (AgLP) areas in the Upper Kansai Basin, India, while reducing the environmental impact, in line with Sustainable Development Goals (SDGs). The SWAT model simulation reveals varied hydrological patterns, with basin water yields from 965.9 to 1012.9 mm and a substantial baseflow (~64% of total flow), emphasizing essential groundwater–surface water interactions for sustainable agriculture. However, the discrepancy between percolation (47% of precipitation) and deep recharge (2% of precipitation) signals potential long-term groundwater challenges. VIKOR analysis offers a robust prioritization framework, ranking SW4 as the most suitable (Qi = 0.003) for balanced hydrological and morphometric features, in agreement with the SWAT outcomes. SW4 and SW5 display optimal agricultural conditions due to stable terrain, effective water retention, and favorable morphometric traits (drainage density 3.0–3.15 km/km²; ruggedness 0.3–0.4). Conversely, SW2, with high drainage density (5.33 km/km²) and ruggedness (2.0), shows low suitability, indicating risks of erosion and poor water retention. This integrated AgLP framework advances sustainable agricultural development and supports SDGs, including SDG 2 (Zero Hunger), SDG 6 (Clean Water), SDG 13 (Climate Action), and SDG 15 (Life on Land). Incorporating hydrological dynamics, land use, soil properties, and climate variables, this approach offers a precise assessment of agricultural suitability to address global sustainability challenges in vulnerable riverine basins of developing nations.
Functionalized Graphene-Based Biosensors for Early Detection of Subclinical Ketosis in Dairy Cows
Chick, Shannon; Kachouei, Matin Ataei; Knowlton, Katharine; Ali, Md Azahar (American Chemical Society, 2024-08-22)
Precision livestock farming utilizing advanced diagnostic tools, including biosensors, can play a key role in the management of livestock operations to improve the productivity, health, and well-being of animals. Detection of ketosis, a metabolic disease that occurs in early lactation dairy cows due to a negative energy balance, is one potential on-farm use of biosensors. Beta-hydroxybutyrate (βHB) is an excellent biomarker for monitoring ketosis in dairy cows because βHB is one of the main ketones produced during this metabolic state. In this report, we developed a low-cost, Keto-sensor (graphene-based sensor) for the detection of βHB concentrations in less than a minute. On this device, graphene nanosheets were layered onto a screen-printed electrode (SPE), and then, a stabilized enzyme (beta-hydroxybutyrate dehydrogenase, NAD+, and glycerol) was used to functionalize the graphene surface enabled by EDC-NHS conjugation chemistry. The Keto-sensor offers an analytical sensitivity of 10 nm and a limit of detection (LoD) of 0.24 nm within a detection range of 0.01 μm-3.00 mm. Spike testing indicates that the Keto-sensor can detect βHB in serum samples from bovines with subclinical ketosis. The Keto-sensor developed in this study shows promising results for early detection of subclinical ketosis on farms.
Unregulated drinking water contaminants and adverse birth outcomes in Virginia
Young, Holly A.; Kolivras, Korine N.; Krometis, Leigh-Anne H.; Marcillo, Cristina E.; Gohlke, Julia M. (PLOS, 2024-05-01)
Through the Unregulated Contaminant Monitoring Rule (UCMR), the Environmental Protection Agency monitors selected unregulated drinking water contaminants of potential concern. While contaminants listed in the UCMR are monitored, they do not have associated health-based standards, so no action is required following detection. Given evolving understanding of incidence and the lack of numeric standards, previous examinations of health implications of drinking water generally only assess impacts of regulated contaminants. Little research has examined associations between unregulated contaminants and fetal health. This study individually assesses whether drinking water contaminants monitored under UCMR 2 and, with a separate analysis, UCMR 3, which occurred during the monitoring years 2008–2010 and 2013–2015 respectively, are associated with fetal health outcomes, including low birth weight (LBW), term-low birth weight (tLBW), and preterm birth (PTB) in Virginia. Singleton births (n = 435,449) that occurred in Virginia during UCMR 2 and UCMR 3 were assigned to corresponding estimated water service areas (n = 435,449). Contaminant occurrence data were acquired from the National Contaminant Occurrence Database, with exposure defined at the estimated service area level to limit exposure misclassification. Logistic regression models for each birth outcome assessed potential associations with unregulated drinking water contaminants. Within UCMR 2, N-Nitrosodimethylamine was positively associated with PTB (OR = 1.08; 95% CI: 1.02, 1.14, P = 0.01). Molybdenum (OR = 0.92; 95% CI: 0.87, 0.97, P = 0.0) and vanadium (OR = 0.96; 95% CI: 0.92, 1.00, P = 0.04), monitored under UCMR 3, were negatively associated with LBW. Molybdenum was also negatively associated (OR = 0.90; 95% CI: 0.82, 0.99, P = 0.03) with tLBW, though chlorodifluoromethane (HCFC-22) was positively associated (OR 1.18; 95% CI: 1.01, 1.37, P = 0.03) with tLBW. These findings indicate that unregulated drinking water contaminants may pose risks to fetal health and demonstrate the potential to link existing health data with monitoring data when considering drinking water regulatory determinations at the national scale.
Heavy Rainfall Impact on Agriculture: Crop Risk Assessment with Farmer Participation in the Paravanar Coastal River Basin
Muthiah, Krishnaveni; Arunya, K. G.; Sridhar, Venkataramana; Patakamuri, Sandeep Kumar (MDPI, 2025-02-24)
Heavy rainfall significantly impacts agriculture by damaging crops and causing substantial economic losses. The Paravanar River Basin, a coastal river basin in India, experiences heavy rainfall during the monsoon season. This study analyzed both ground-level rainfall measurements and farmers’ experiences to understand the effects of heavy rainfall on agriculture. Rainfall data from nine rain gauge locations were analyzed across three cropping seasons: Kharif 1 (June to August), Kharif 2 (September to November), and Rabi (December to May). To determine the frequency of heavy rainfall events, a detailed analysis was conducted based on the standards set by the India Meteorological Department (IMD). Villages near stations showing increasing rainfall trends and a higher frequency of heavy rainfall events were classified as vulnerable. The primary crops cultivated in these vulnerable areas were identified through a questionnaire survey with local farmers. A detailed analysis of these crops was conducted to determine the cropping season most affected by heavy rainfall events. The impacts of heavy rainfall on the primary crops were assessed using the Delphi technique, a score-based crop risk assessment method. These impacts were categorized into eight distinct types. Among them, yield reduction, waterlogging, crop damage, soil erosion, and crop failure emerged as the most significant challenges in the study area. Additional impacts included nutrient loss, disrupted microbial activity, and disease outbreaks. Based on this evaluation, risks were classified into five categories: low risk, moderate risk, high risk, very high risk, and extreme risk. This categorization offers a framework for understanding potential consequences and making informed decisions. To address these challenges, the study recommended mitigation measures such as crop management, soil management, and drainage management. Farmers were also encouraged to conduct a cause-and-effect analysis. This bottom-up approach raised awareness among farmers and provided practical solutions to reduce crop losses and mitigate the effects of heavy rainfall.
Ajna: A Wearable Shared Perception System for Extreme Sensemaking
Wilchek, Matthew; Luther, Kurt; Batarseh, Feras (ACM, 2025-01-16)
This article 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.
Analyzing multiple-source water usage patterns and affordability in rural central Appalachia
Dudzinski, Emerald; Ellis, Kimberly P.; Krometis, Leigh-Anne H.; Albi, Kate; Cohen, Alasdair (2024-07-18)
Nearly 500,000 American households lack complete plumbing, and more than 21 million Americans are reliant on public drinking water systems with at least one annual health-based drinking water violation. Rural, low-income, and minority communities are significantly more likely to be burdened with unavailable or unsafe in-home drinking water. Lack of access and distrust of the perceived quality of municipally supplied water are leading an increasing number of Americans to rely instead on less regulated, more expensive, and potentially environmentally detrimental water sources, such as roadside springs and bottled water. Previous research studies have stressed the importance of considering the economic burden of all water related expenditures including financial and non-financial water related costs; however, past examinations of water costs have primarily focused on municipal water supplies. We propose an economic model to consider the full economic burden associated with multiple-source water use by incorporating both direct costs (e.g., utility bills, well maintenance, bottled water purchase, payments for water hauling/delivery) and indirect water-related expenditures (e.g., transportation costs to gather water, productivity lost due to time spent collecting). Using data gathered from household surveys along with the economic model, this study estimates the economic burden from two case studies in rural Central Appalachia with persistent water quality concerns: (1) McDowell County, WV (n=15) and (2) Letcher and Harlan Counties, KY (n=9). All surveyed households (n=24) rely on multiple-source water to meet their needs, frequently citing their perception of unsafe in-home tap water. Bottled water was the most common choice for drinking water in both settings (92%, n=24), though roadside spring use was also prevalent in McDowell County, WV (53%, n=15). The results show that multiple-water source use is associated with a large economic burden. Households reliant primarily on bottled water as their drinking water source spent 12.3% (McDowell County, WV) and 5.6% (Letcher and Harlan Counties, KY) of their respective county’s median household income (MHI) on water related expenditures. Households reliant primarily on roadside springs as their drinking water source spent 11.8% (McDowell County, WV) of MHI on water related expenditures. Hence, the vast majority of participating households (92%, n=24) spend above the US water affordability threshold of 2% MHI. The application of this economic model highlights major water affordability concerns in water insecure Appalachian communities and provides a foundation for future studies and enhancements.
Effectiveness of stormwater control measures in protecting stream channel stability
Towsif Khan, Sami; Wynn-Thompson, Theresa; Sample, David; Al-Smadi, Mohammad; Shahed Behrouz, Mina; Miller, Andrew J. (2024-04-23)
While research on the hydrologic impact of different types of stormwater control measures (SCMs) is extensive, little research exists linking urbanization, widespread implementation of SCMs and channel stability in headwater streams. This study evaluated whether the unified stormwater sizing criteria (USSC) regulations in the state of Maryland, USA, which require the use of both end-of-pipe and distributed, smallscale SCMs, protect channel stability. To achieve this goal, a coupled hierarchical modelling approach utilizing the Storm Water Management Model (SWMM) and the Hydrologic Engineering Center River Analysis System 6.3 (HEC-RAS) was developed to predict changes in streamflow and sediment transport dynamics in a first-order gravel-bed, riffle-pool channel. Storm event discretization revealed that 88% of observed storm events during the 16 years (2004–2020) had durations less than 18 h and that the greatest peak flows resulted from storm events with durations less than 24 h. HEC-RAS simulation results also showed that both channel degradation and aggradation, as high as 1.2 m, will likely occur due to regulations which require the use of 24 h duration design storms with a target stormwater detention time rather than bed material sediment transport limits. Overall, this study provides valuable insights into the complex interactions between SCM practises, flow regimes and sediment transport dynamics in heavily urbanized watersheds. It is recommended that SCMs be designed using a continuous simulation model with at least 10 years of continuous rainfall data. Furthermore, to protect channel stability, the SCM design goal should focus on maintaining pre-development sediment transport regimes across a range of flows.
Attomolar-sensitive milk fever sensor using 3D-printed multiplex sensing structures
Kachouei, Matin Ataei; Parkulo, Jacob; Gerrard, Samuel D.; Fernandes, Tatiane; Osorio, Johan S.; Ali, Md. Azahar (Springer Nature, 2025-01-02)
The diagnosis of milk fever or hypocalcemia in lactating cows has a significant economic impact on the dairy industry. It is challenging to identify asymptomatic subclinical hypocalcemia (SCH) in transition dairy cows. Monitoring subclinical hypocalcemia in milk samples can expedite treatment and improve the health, productivity, and welfare of dairy cows. In this study, an attomolar-sensitive sensor is developed using extrusion-based 3D-printed sensing structures to detect the ratio of ionized calcium to phosphate levels in milk samples. The unique geometries of the lateral structure of 3D-printed sensors, along with the wrinkled surfaces, provide a limit of detection down to the attomole (138 am) concentration of the target analyte. The calcium-to-phosphate ratio in milk samples not only provides early disease indications but also enables on-site testing. This highly selective test is validated using real milk and blood samples, and the results are compared with those of commercial meters. This fast response (~10 s) low-cost sensor opens a promising tool for the farm-side diagnostic of dairy cows that can promote best practice management of dairy cows.
Stream restoration that allows for self-adjustment can increase channel-floodplain connectivity
Christensen, Nicholas D.; Prior, Elizabeth M.; Czuba, Jonathan A.; Hession, W. Cully (2024-02-14)
Streams are often “restored” to reduce sediment loading using one or a combination of practices such as livestock exclusion, riparian plantings, and/or bank reshaping and stabilization. Direct comparisons of how these methods affect stream processes, including channel-floodplain connectivity, over time are essential to informing restoration design. (Channel-floodplain connectivity is the ability of a stream to exchange water, sediment, and nutrients with its floodplain at high flows.) To investigate the impact these stream restoration practices have had on channel-floodplain connectivity, we developed a 2-D HEC-RAS hydraulic model for 3 restoration treatments along an urban and agriculturally impacted stream in southwest Virginia, United States. All 3 treatments excluded cattle in 2009. The farthest upstream treatment, Treatment 1, had no other intervention while the other two, Treatments 2 and 3, were regraded and stabilized, then replanted with native species (completed May 2010). The overhanging banks of Treatment 2 were regraded to a slope of 3:1, while those of Treatment 3 had a flat inset floodplain cut into the bank before sloping the banks at 3:1. During the 11-year monitoring timeline, prior work showed the streambanks in Treatment 1 migrated through both outer bank erosion and inner bank deposition with the autogenic creation of inset floodplains, while Treatments 2 and 3 had minimal bank adjustment. The adjusted geometry of Treatment 1 provided higher floodplain volume, channel-floodplain exchange flows, and flow moving across the floodplain than Treatments 2 and 3. Treatment 3 showed some metrics of higher connectivity than Treatment 2, but there was not uniform agreement between metrics. While the hydraulic analysis indicates a higher channelfloodplain connectivity in Treatment 1, active management of Treatments 2 and 3 has reduced the bank erosion rate and accelerated the riparian forest regrowth, providing other benefits including increased shading, wood supply, and vegetation diversity.
Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models
Prior, Elizabeth M.; Michaelson, Nathan; Czuba, Jonathan A.; Pingel, Thomas J.; Thomas, Valerie A.; Hession, W. Cully (American Geophysical Union, 2024-07-07)
Topography and the computational mesh grid are fundamental inputs to all two-dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter-scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC-RAS model with varying mesh grid resolutions (1 and 2 m) and drone-based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel-floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.
Cancer detection in dogs using rapid Raman molecular urinalysis
Robertson, John L.; Dervisis, Nikolaos G.; Rossmeisl, John H. Jr.; Nightengale, Marlie; Fields, Daniel; Dedrick, Cameron; Ngo, Lacey; Issa, Amr Sayed; Guruli, Georgi; Orlando, Giuseppe; Senger, Ryan S. (Frontiers, 2024-02-07)
Introduction: The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor. Methods: Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software. Results and discussion: The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.
A novel insight on input variable and time lag selection in daily streamflow forecasting using deep learning models
Khatun, Amina; Nisha, M. N.; Chatterjee, Siddharth; Sridhar, Venkataramana (Elsevier, 2024-08)
This study investigates the feasibility of using hybrid models namely Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU), for short-to-medium range streamflow forecasting in the Mahanadi River basin in India. The performance of these hybrid models is compared with that of standalone models. It investigates the impact of selected parameters and associated time lags on the model performance and offers valuable insights into the use of hybrid models for runoff simulation. The hybrid CNN-LSTM model proves to be robust in capturing the overall time series and the typical high peak flows in both the correlation-based and constant lag cases. Also, the upstream discharges play a significant role in improving the streamflow forecasting. Furthermore, the consideration of all input variables with a constant time lag equal to the basin lag time may yield better flood forecasts, even in cases where computational resources are limited.
Real-Time Flood Forecasting using an Integrated Hydrologic and Hydraulic Model for the Vamsadhara and Nagavali basins, Eastern India
Rao, G. Venkata; Nagireddy, Nageswara Reddy; Keesara, Venkata Reddy; Sridhar, Venkataramana; Srinivasan, Raghavan; Umamahesh, N. V.; Pratap, Deva (Springer, 2024-02-23)
Due to recent rainfall extremes and tropical cyclones that form over the Bay of Bengal during the pre- and post-monsoon seasons, the Nagavali and Vamsadhara basins in India experience frequent floods, causing significant loss of human life and damage to agricultural lands and infrastructure. This study provides an integrated hydrologic and hydraulic modeling system that is based on the Soil and Water Assessment Tool model and the 2-Dimensional Hydrological Engineering Centre-River Analysis System, which simulates floods using Global Forecasting System rainfall forecasts with a 48-h lead time. The integrated model was used to simulate the streamflow, flood area extent, and depth for the historical flood events (i.e., 1991–2018) with peak discharges of 1200 m3/s in the Nagavali basin and 1360 m3/s in the Vamsadhara basin. The integrated model predicted flood inundation depths that were in good agreement with observed inundation depths provided by the Central Water Commission. The inundation maps generated by the integrated modeling system with a 48-h lead time for tropical cyclone Titli demonstrated an accuracy of more than 75%. The insights gained from this study will help the public and government agencies make better decisions and deal with floods.

Browse

Recent Submissions