Scholarly Works, Biological Systems Engineering

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A Comprehensive Review of Sensing, Control, and Networking in Agricultural Robots: From Perception to Coordination
Nkwocha, Chijioke Leonard; Adewumi, Adeayo; Folorunsho, Samuel Oluwadare; Eze, Chrisantus; Jjagwe, Pius; Kemeshi, James; Wang, Ning (MDPI, 2025-10-29)
This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture.
Manganese exposure from spring and well waters in the Shenandoah Valley: interplay of aquifer lithology, soil composition, and redox conditions
Hinkle, Margaret A. G.; Ziegler, Brady; Culbertson, Haley; Goldmann, Christopher; Croy, Marina E.; Willis, Noah; Ling, Erin; Reinhart, Benjamin; Lyon, Eva C. (Springer, 2024-06-01)
Manganese (Mn) is of particular concern in groundwater, as low-level chronic exposure to aqueous Mn concentrations in drinking water can result in a variety of health and neurodevelopmental effects. Much of the global population relies on drinking water sourced from karst aquifers. Thus, we seek to assess the relative risk of Mn contamination in karst by investigating the Shenandoah Valley, VA region, as it is underlain by both karst and non-karst aquifers and much of the population relies on water wells and spring water. Water and soil samples were collected throughout the Shenandoah Valley, to supplement pre-existing well water and spring data from the National Water Information System and the Virginia Household Water Quality Program, totaling 1815 wells and 119 springs. Soils were analyzed using X-ray fluorescence and Mn K-Edge X-ray absorption near-edge structure spectroscopy. Factors such as soil type, soil geochemistry, and aquifer lithology were linked with each location to determine if correlations exist with aqueous Mn concentrations. Analyzing the distribution of Mn in drinking water sources suggests that water wells and springs within karst aquifers are preferable with respect to chronic Mn exposure, with < 4.9% of wells and springs in dolostone and limestone aquifers exceeding 100 ppb Mn, while sandstone and shale aquifers have a heightened risk, with > 20% of wells exceeding 100 ppb Mn. The geochemistry of associated soils and spatial relationships to various hydrologic and geologic features indicates that water interactions with aquifer lithology and soils contribute to aqueous Mn concentrations. Relationships between aqueous Mn in spring waters and Mn in soils indicate that increasing aqueous Mn is correlated with decreasing soil Mn(IV). These results point to redox conditions exerting a dominant control on Mn in this region.
Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics
Sun, Yue; Nayani, Nikhita Sai; Xu, Yixiang; Xu, Zhanfeng; Yang, Jun; Feng, Yiming (American Chemical Society, 2024-06-03)
Conventional techniques used to measure oil content in the food are laborious, rely on chemical agents, and have a negative environmental impact. In this study, near-infrared hyperspectral imaging was used as a rapid and nondestructive tool to determine the oil content and its distribution in commercial flat-cooked and batch-cooked potato chips. By evaluating various algorithmic models, such as partial least-squares regression (PLSR), ridge regression, random forest, gradient boosting, and support vector regression, in combination with preprocessing methods like multiplicative scattering correction, standard normal variable (SNV) transform, Savitzky-Golay filtering, normalization, and baseline correction, the most effective preprocessing method and model combination was determined to be SNV-PLSR. Moreover, by employing the optimized PLSR model, a highly accurate oil content prediction model was developed, achieving a coefficient of determination (R2) of 0.95. To identify the wavelengths that contributed most significantly to the model's predictive power, variable importance in projection (VIP) analysis was utilized. A dimensionally reduced PLSR model using only 68 selected wavelengths was developed based on the VIP analysis. This simplified model maintained similar performance to that of the full-spectrum model while using a smaller data set. The model was also used to apply the hyperspectral images of potato chips at the pixel level to visualize the oil distribution in potato chips with the intent to provide a real-time approach to quality control for the potato chip industry.
Quantifying water effluent violations and enforcement impacts using causal AI
Wang, Yingjie; Sobien, Dan; Kulkarni, Ajay; Batarseh, Feras A. (Wiley, 2024-06-01)
In the landscape of environmental governance, controlling water pollution through the regulation of point sources is vital as it preserves ecosystems, protects human health, ensures legal compliance, and fulfills global environmental responsibilities. Under the Clean Water Act, the integrated compliance information system monitors the compliance and enforcement status of facilities regulated by the National Pollutant Discharge Elimination System (NPDES) permit program. This study assesses temporal and geographic trends for effluent violations within the United States and introduces a novel metric for quantifying violation trends at the facility level. Furthermore, we utilize a linear parametric approach for Conditional Average Treatment Effect (CATE) causal analysis to quantify the heterogeneous effects of EPA and state enforcement actions on effluent violation trends at facilities with NPDES permits. Our research reveals insights into national pollutant discharge trends, regional clustering of all pollutant violation types in Ohio (G(i)* Z-score of 2.15), and priority pollutants in West Virginia (G(i)* Z-score of 3.07). The trend metric identifies regulated facilities that struggle with severe and recurring violations. The causal model highlights variations in state compliance and enforcement effectiveness, underscoring the successful moderation of violation trends by states such as Montana and Maryland, among others.
Genetically Encoded, Noise-Tolerant, Auxin Biosensors in Yeast
Chaisupa, Patarasuda; Rahman, Md Mahbubur; Hildreth, Sherry B.; Moseley, Saede; Gatling, Chauncey; Bryant, Matthew R.; Helm, Richard F.; Wright, R. Clay (American Chemical Society, 2024-08-28)
Auxins are crucial signaling molecules that regulate the growth, metabolism, and behavior of various organisms, most notably plants but also bacteria, fungi, and animals. Many microbes synthesize and perceive auxins, primarily indole-3-acetic acid (IAA, referred to as auxin herein), the most prevalent natural auxin, which influences their ability to colonize plants and animals. Understanding auxin biosynthesis and signaling in fungi may allow us to better control interkingdom relationships and microbiomes from agricultural soils to the human gut. Despite this importance, a biological tool for measuring auxin with high spatial and temporal resolution has not been engineered in fungi. In this study, we present a suite of genetically encoded, ratiometric, protein-based auxin biosensors designed for the model yeast Saccharomyces cerevisiae. Inspired by auxin signaling in plants, the ratiometric nature of these biosensors enhances the precision of auxin concentration measurements by minimizing clonal and growth phase variation. We used these biosensors to measure auxin production across diverse growth conditions and phases in yeast cultures and calibrated their responses to physiologically relevant levels of auxin. Future work will aim to improve the fold change and reversibility of these biosensors. These genetically encoded auxin biosensors are valuable tools for investigating auxin biosynthesis and signaling in S. cerevisiae and potentially other yeast and fungi and will also advance quantitative functional studies of the plant auxin perception machinery, from which they are built.
A methodological framework for assessing sea level rise impacts on nitrate loading in coastal agricultural watersheds using SWAT plus : A case study of the Tar-Pamlico River basin, North Carolina, USA
Tapas, Mahesh R.; Etheridge, Randall; Tran, Thanh-Nhan-Duc; Finlay, Colin G.; Peralta, Ariane L.; Bell, Natasha; Xu, Yicheng; Lakshmi, Venkataraman (Elsevier, 2024-11-15)
This study addresses the urgent need to understand the impacts of climate change on coastal ecosystems by demonstrating how to use the SWAT+ model to assess the effects of sea level rise (SLR) on agricultural nitrate export in a coastal watershed. Our framework for incorporating SLR in the SWAT+ model includes: (1) reclassifying current land uses to water for areas with elevations below 0.3 m based on SLR projections for midcentury; (2) creating new SLR-influenced land uses, SLR-influenced crop database, and hydrological response units for areas with elevations below 2.4 m; and (3) adjusting SWAT+ parameters for the SLR-influenced areas to simulate the effects of saltwater intrusion on processes such as plant yield and denitrification. We demonstrate this approach in the Tar-Pamlico River basin, a coastal watershed in eastern North Carolina, USA. We calibrated the model for monthly nitrate load at Washington, NC, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.61. Our findings show that SLR substantially alters nitrate delivery to the estuary, with increased nitrate loads observed in all seasons. Higher load increases were noted in winter and spring due to elevated flows, while higher percentage increases occurred in summer and fall, attributed to reduced plant uptake and disrupted nitrogen cycle transformations. Overall, we observed an increase in mean annual nitrate loads from 155,000 kg NO3-N under baseline conditions to 157,000 kg NO3-N under SLR scenarios, confirmed by a statistically significant paired t-test (p = 2.16 x 10(-10)). This pioneering framework sets the stage for more sophisticated and accurate modeling of SLR impacts in diverse hydrological scenarios, offering a vital tool for hydrological modelers.
Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects
Nkwocha, Chijioke Leonard; Chandel, Abhilash Kumar (MDPI, 2025-10-16)
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.
Comparative Analysis of Aerosol Direct Radiative Forcing During COVID-19 Lockdown Period in Peninsular India
Kotrike, Tharani; Keesara, Venkata Reddy; Sridhar, Venkataramana; Pratap, Deva (Springer, 2025-02-01)
The load of aerosols in the atmosphere has been increasing gradually due to industrialization and urbanization. This increase has contributed to change in the Earth's radiation budget through the absorption or scattering of radiation. The aerosol direct radiative forcing (ADRF) is a measurement utilized to comprehend the impact of cooling or warming up of the atmosphere directly by aerosols. Our study examined the impact of aerosols during the COVID-19 pandemic by comparing them to the average from the preceding 5-year period (2015-2019) in peninsular India. The measure of aerosols deployed in this study is the Aerosol Optical Depth (AOD), and the study was carried out on three distinct time frames: prior to lockdown, during lockdown, and post lockdown. The study revealed that the ADRF increased during all the three time frames of 2020 compared to the average of 2015-2019, and the other time scales experienced an increase in ADRF as well. The most notable rise in ADRF and decrease in temperature occurred in the tropical savanna and warm semi-arid climate regions during the pre-lockdown period. During lockdown, the increase in ADRF was seen throughout the study area, and a decrease in temperature was observed only in the tropical monsoon region. In the post-lockdown period, the decline in ADRF was accompanied by a fall in temperature in the tropical savanna region. This study provides insights into the effect of aerosols on ADRF in peninsular India and highlights the importance of monitoring and regulating aerosol emissions to mitigate the changes in temperature.
CO2 and CH4 Concentrations in Headwater Wetlands Influenced by Morphology and Changing Hydro-Biogeochemical Conditions
Lloreda, Carla Lopez; Maze, James; Wardinski, Katherine; Corline, Nicholas; Mclaughlin, Daniel; Jones, C. Nathan; Scott, Durelle; Palmer, Margaret; Hotchkiss, Erin R. (Springer, 2024-11-01)
Headwater wetlands are important sites for carbon storage and emissions. While local- and landscape-scale factors are known to influence wetland carbon biogeochemistry, the spatial and temporal heterogeneity of these factors limits our predictive understanding of wetland carbon dynamics. To address this issue, we examined relationships between carbon dioxide (CO2) and methane (CH4) concentrations with wetland hydrogeomorphology, water level, and biogeochemical conditions. We sampled water chemistry and dissolved gases (CO2 and CH4) and monitored continuous water level at 20 wetlands and co-located upland wells in the Delmarva Peninsula, Maryland, every 1-3 months for 2 years. We also obtained wetland hydrogeomorphologic metrics at maximum inundation (area, perimeter, and volume). Wetlands in our study were supersaturated with CO2 (mean = 315 mu M) and CH4 (mean = 15 mu M), highlighting their potential role as carbon sources to the atmosphere. Spatial and temporal variability in CO2 and CH4 concentrations was high, particularly for CH4, and both gases were more spatially variable than temporally. We found that groundwater is a potential source of CO2 in wetlands and CO2 decreases with increased water level. In contrast, CH4 concentrations appear to be related to substrate and nutrient availability and to drying patterns over a longer temporal scale. At the landscape scale, wetlands with higher perimeter:area ratios and wetlands with higher height above the nearest drainage had higher CO2 and CH4 concentrations. Understanding the variability of CO2 and CH4 in wetlands, and how these might change with changing environmental conditions and across different wetland types, is critical to understanding the current and future role of wetlands in the global carbon cycle.
An Integrated Framework for Optimal Allocation of Land and Water Resources in an Agricultural Dominant Basin
Buri, Eswar Sai; Keesara, Venkata Reddy; Loukika, K. N.; Sridhar, Venkataramana (Springer, 2025-02-01)
The water deficit is one of the primary challenges faced by developing countries, stemming from several factors such as limited water resources, population growth, and climate change. Optimal allocation of water resources represents a comprehensive strategy for water resource management, acknowledging the intricate connections between water systems and their repercussions on the environment, society, and economy. It serves as a means of integrating diverse elements of development plans into a cohesive approach for land and water planning and management. In the current study, we undertook the optimal allocation of land and water resources across different sectors for the water years 2016-17, 2017-18, and 2018-19. The study area chosen was the Munneru basin, situated in the lower section of the Krishna River Basin in India. This basin is predominantly agricultural, covering 63.17% of the area, and was selected to validate the proposed framework concept. Within the study area, we identified six distinct water-demanding sectors and calculated their sectoral water demands at a basin level. To assess water availability in the basin, we conducted hydrological modeling employing the Soil and Water Assessment Tool (SWAT). Furthermore, we determined the crop water requirements for various crops using CROPWAT. For the optimal allocation of water resources, we applied the Non-dominated Sorting Genetic Algorithm-II (NSGA - II) optimization model, considering two different objectives that account for social and economic aspects. To identify superior solutions from the Pareto front, we employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Compromising Programming (CP) methods. Through this methodology, we achieved maximum utilization of water and land resources and maximized returns from the agricultural sector. Following the optimal allocation of land and water, we observed an average annual increase of 3.61% in agricultural sector returns. These outcomes demonstrated a substantial enhancement in the water use efficiency across all pertinent water use sectors. As a result, decision-makers may contemplate the implementation of this framework in large-scale regions, with potential expansion to encompass a national sustainable development strategy at the country level.
Quantitative Assessment of Environmental Site Design vs. Traditional Storage-Based Stormwater Management: Impacts on Catchment Hydrology of Minebank Run, Baltimore, MD
Maliha, Mushtari; Alsmadi, Mohammad; Sample, David J.; Wynn-Thompson, Theresa; Miller, Andrew J. (Wiley, 2025-10-05)
Environmental site design (ESD) is a stormwater management approach that prioritises the use of infiltration-based non-structural techniques to mimic the natural hydrologic cycle by reducing impervious surfaces, slowing runoff and increasing infiltration. Traditional storage-based stormwater management is designed for flood control by quickly diverting runoff from developed areas. This study compared the effect of ESD and only storage-based stormwater management practices on the hydrology of an urban watershed in Baltimore County, Maryland, USA. Minebank Run is an 8.47 km2 flashy urban stream with a catchment largely developed without stormwater management. A calibrated SWMM model was used to simulate changes in catchment hydrology resulting from ESD and detention basins over a 54-year period, from the onset of urbanisation in 1948 to the state of urbanisation in 2001. This approach offers a novel, retrospective perspective by simulating how the watershed hydrology might have changed if ESD had been implemented from the beginning of urban development. The model results were analysed by quantifying and comparing different hydrologic metrics to evaluate runoff quantity and flow variability. Results indicated that although storage ponds performed similarly to ESD in reducing annual maximum peak flows (43% vs. 45% reduction, respectively), ESD reduced mean annual runoff coefficients significantly more than ponds (28% vs. 2.7%, p < 0.0001). The Richards–Baker Flashiness Index was reduced from 0.46 to 0.32 with the implementation of ESD, as compared to 0.36 with detention ponds. This study also tested the hypothesis that the impact of urbanisation on the hydrology of the Minebank Run watershed would have been reduced if it had been developed with ESD. The results indicated that the implementation of ESD would have reduced annual maximum peak flows by an average of 46%, annual mean runoff coefficients by 51% and the Richards–Baker Flashiness Index by 37%, as compared to the as-is condition. The study provides quantitative insights into the performance of traditional and innovative stormwater management techniques at the catchment scale, illustrating the benefits of a combination of both infiltration practices and detention storage in reducing the hydrologic impacts of urbanisation.
Precision Adjuvant Strategies in Vaccine Development for Substance Use Disorders: Variability and Mechanistic Insights
Bian, Yuanzhi; Ci, Qiaoqiao; Luo, Xin M.; Zhang, Chenming (MDPI, 2025-09-20)
Substance use disorders (SUDs) remain a major global health challenge with limited treatment options and high relapse rates. Vaccines that induce drug-sequestering antibodies have shown promise, but their efficacy is hindered by the poor immunogenicity of small-molecule haptens. Adjuvants, substances that enhance immune responses, are critical for overcoming this limitation and improving vaccine efficacy. This review synthesizes over two decades of preclinical and clinical research to guide rational adjuvant design for SUD vaccines. Five major adjuvant classes are examined: aluminum-salt adjuvants, emulsion adjuvants, toll-like receptor (TLR) agonists, protein immunopotentiators, and cytokine modulators. Their physicochemical properties, innate immune activation profiles, and applications in nicotine, stimulant, and opioid vaccines are discussed. Comparative analyses reveal pronounced drug-specific and carrier-specific variability. Case studies illustrate the superior performance of a complementary TLR-agonist pair in a nicotine nanovaccine versus its limited effect in oxycodone vaccines. They also reveal the differential efficacy of an oil-in-water emulsion adjuvant across antigen types. Four principles emerge: (i) no adjuvant is universally optimal; (ii) drug pharmacology influences immune signaling; (iii) adjuvant-carrier compatibility is important; (iv) complementary adjuvant pairings often outperform single agents. These insights support a precision-vaccinology paradigm that tailors adjuvant strategies to each drug class and the delivery vehicle, advancing the development of next-generation SUD vaccines.
Haloferax mediterranei for bioplastics production from wasted materials: potential, opportunities, and challenges
Zhang, Xueyao; Zhao, Fujunzhu; Wang, Mingxi; Huang, Haibo; Kim, Young-Teck; Lansing, Stephanie; Wang, Zhi-Wu (2025-04-01)
This chapter explored the potential of Haloferax mediterranei , a halophilic archaeon, as a sustainable biocatalyst for polyhydroxyalkanoates (PHA) production from waste ma- terials. PHAs, biodegradable bioplastics, offer an eco-friendly alternative to petroleum- based plastics but face commercialization challenges due to high production costs and feedstock variability. H. mediterranei addresses these issues with its ability to thrive in high-salinity environments, reducing contamination risks and sterilization costs, while metabolizing diverse, low-cost waste-derived substrates. The chapter details H. mediter- ranei s tolerance of inhibitors, high PHA yields, efficient downstream processing, and adaptability to continuous fermentation systems. Challenges, including substrate and product inhibition, can be addressed through innovative pretreatment and fermentation strategies. The chapter also highlighted H. mediterranei s versatility in producing valuable co-products like carotenoids and extracellular polymeric substances, explored its role in high-salinity wastewater treatment, and emphasized its upscaled application potential, thereby paving the way for scalable, eco-friendly bioplastic production.
Enhancement of 3-hydroxyvalerate fraction in poly (3-hydroxybutyrate-co-3-hydroxyvalerate) produced by Haloferax mediterranei fed with food waste pretreated via arrested anaerobic digestion integrated with microbial electrolysis cells
Zhang, Xueyao; Amradi, Naresh Kumar; Moore, Martin; Hassanein, Amro; Mickol, Rebecca L.; McCoy, Emily L.; Eddie, Brian J.; Shepard, Jamia S.; Wang, Jiefu; Lansing, Stephanie; Yates, Matthew D.; Kim, Young-Teck; Wang, Zhi-Wu (Elsevier, 2025-08)
Bioplastics made of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) with a 20 mol% HV fraction are highly desirable in the market for 3-Hydroxyvalerate (HV)-conferred superior thermal, biological, and mechanical properties. Although Haloferax mediterranei (HM) is capable of producing PHBV from food waste, its HV fraction is generally lower than 10 mol%. This study for the first time investigated the engineering approach to increasing HV fraction through elevating the propionic and valeric acid fractions in volatile fatty acids (VFAs) produced from food waste via arrested anaerobic digestion with and without microbial electrolysis cells (MECs) incorporation. Results showed that HV fraction in PHBV produced by HM is proportional to the fractions of propionic and valeric acids in VFAs. A 20 mol% HV fraction can be achieved by MECs incorporation, which might be attributable to pH regulation by the MECs. These findings lay a foundation for developing waste-processing technologies that enable the production of high-value, microbially-derived materials.
Reducing Heat Without Impacting Quality: Optimizing Trypsin Inhibitor Inactivation Process in Low-TI Soybean
Xiao, Ruoshi; Rosso, Luciana; Walker, Troy; Reilly, Patrick; Zhang, Bo; Huang, Haibo (MDPI, 2025-08-29)
A soybean meal is a key protein source in human foods and animal feed, yet its digestibility is constrained by endogenous trypsin inhibitors (TIs). Thermal processing is the mainstream tool for TI inactivation, but high-intensity heat treatments increase energy consumption and can potentially denature proteins, diminishing nutritional quality. Reducing the thermal input while maintaining nutritional quality is, therefore, a critical challenge. One promising strategy is the use of soybean cultivars bred for low-TI expression, which may allow for milder processing. However, the performance of these low-TI cultivars under reduced heat conditions remains unstudied. This study treated soybean samples under four different temperatures (60, 80, 100, and 121 °C) for 10 min and investigated the impact of heat treatment on TI concentration, in vitro protein digestibility, and nutritional properties of meals from a conventional high-TI variety (Glenn) and a novel low-TI variety (VT Barrack). Results showed that heat treatment at 100 °C significantly improved protein digestibility and lower TI concentrations in both varieties. A negative correlation was observed between protein digestibility and TI concentration in both soybean varieties. At 100 °C, the low-TI variety achieved 81.4% protein digestibility with only 0.6 mg/g TIs, whereas the high-TI variety required 121 °C to achieve comparable protein digestibility and a TI reduction. These findings highlight that low-TI soybeans can lower the necessary thermal treatment to 100 °C to minimize TIs while simultaneously preserving protein quality and cutting energy demand, offering a practical, cost-effective approach to producing higher-quality soybean meals.
Testing for heavy metals in drinking water collected from Dog Aging Project participants
Sexton, Courtney L.; O'Brien, Janice S.; Lytle, Justin; Rodgers, Sam; Keyser, Amber; Kauffman, Mandy; Dunbar, Matthew D.; Dog Aging Project Consortium; Edwards, Marc A.; Krometis, Leigh-Anne H.; Ruple, Audrey (PLOS, 2025-08-06)
Heavy metals are commonly found in groundwater and can affect the quality of drinking water. In this pilot study, we analyzed the quality of drinking water for dogs participating in the Dog Aging Project (DAP) who lived in homes not served by a municipal water supply. In order to capture both diverse and localized environmental factors that may affect drinking water, 200 owners of DAP dogs located in one of 10 selected states were invited to participate. We tested for the presence of 28 metals in dogs’ drinking water, including eight (8) heavy metals that have maximum contaminant levels (MCLs) designated by the Environmental Protection Agency (EPA) and five (5) heavy metals that have EPA health guidance levels. The eight metals with MCLs are known to cause chronic health issues in humans after long-term ingestion. Our aim in this pilot was to determine whether such elements could be detected by at-home sampling of dogs’ drinking water, and, using regression models, to examine associations between water source variables, metal values, and developed disease. We found detectable levels of all metals tested. There were 126 instances when an analyte (arsenic, lead, copper, sodium, strontium, nickel, or vanadium) was above the EPA MCL or health guidance level. We further identified potential association between the presence of titanium and chromium, and occurrence of a known health condition in dogs. This prompts further investigation with a larger, stratified sample analyzing dogs’ drinking water composition and long-term health and wellness outcomes in dogs living in diverse geographies. These results may impact veterinary care decisions and husbandry, and underscore the validity and importance of utilizing dogs as sentinels of human health outcomes in the context of drinking water contamination.
PerceptiSync: Trustworthy Object Detection using Crowds-in-the-Loop for Cyber-Physical Systems
Wilchek, Matthew; Nguyen, Minh; Wang, Yingjie; Luther, Kurt; Batarseh, Feras (ACM, 2025-07)
Establishing reliable object detection in distributed environments is challenging, particularly when trust depends on results from multiple computer vision systems. In this manuscript, we introduce PerceptiSync, a novel and trustworthy Embodied-AI (EAI) framework. It is designed for shared perception across distributed Cyber-Physical Systems (CPS) that utilize object detection. This includes applications in Connected Autonomous Vehicles, drone swarms, and CCTV camera networks. PerceptiSync is designed around a Crowds-in-the-Loop (CITL) concept to enhance system reliability by incorporating four individual user configurations and the Dirichlet-Categorical trust model. PerceptiSync undergoes a two-stage evaluation. First, it is assessed using a benchmark Computer Vision (CV) dataset to track performance over time. Second, it is tested with integrated user configurations to evaluate trust accuracy and mitigation capabilities against false positives. The results show that PerceptiSync outperforms existing AI-only trust frameworks, achieving a higher mean Kendall's Tau coefficient of 0.228 compared to 0.051, demonstrating successful performance over time.
Context-driven Deep Learning Forecasting for Wastewater Treatment Plants
Sikder, Md Nazmul Kabir; Batarseh, Feras A. (ACM, 2025-06)
Wastewater-treatment utilities face various operational challenges that could benefit from embodied AI and other advanced cyber-physical technologies. These challenges include optimizing pump schedules, managing energy and chemical consumption during extreme weather events, and interpreting sensor data for water-quality treatment. Addressing these issues requires accurate short-term, multi-step forecasting tools to provide reliable real-time decision support, particularly during heavy rainfall events that can overwhelm operations. Leading water-system operators and vendors in the United States report that tools capable of forecasting 4–6 hours ahead can significantly enhance resource management, including energy, chemicals, and manpower. However, accurate short-term forecasting is particularly difficult because of the non-linearities and seasonal variations inherent in plant data, which limit effective decision-making. To address these challenges, we propose cP2O, a context-driven forecasting solution, a novel hybrid deep-learning architecture integrating dynamic context extraction with hierarchical, dilated long-short-term memory (LSTM) cells. The proposed model utilizes internal water-system data, such as flow rates and tunnel levels, along with exogenous variables including weather, river flow, and demographic information to derive relevant context. It captures both short-term fluctuations and long-term dependencies in water-level data, while an internal attention mechanism dynamically weighs the importance of exogenous information. We validate the model on two full-scale utilities: tunnel-water-level forecasting at DC Water’s Blue Plains facility and nitrate-level prediction at AlexRenew. Relative to strong baselines, cP2O reduces mean absolute percentage error by 22 % and 19 %, respectively, and its 90 % prediction bands cover 90.5 % ± 3.2 % of observations (5.9 % below, 3.6 % above). By dynamically incorporating contextual information, especially under critical conditions, the model delivers reliable real-time forecasts that enhance resource allocation and strengthen the overall resilience of wastewater-treatment operations.
Volatile fatty acids recovery from thermophilic acidogenic fermentation using hydrophobic deep eutectic solvents
Liu, Can; Zhang, Xueyao; Qiao, Qi; Wang, Zhiwu; Shao, Qing; Shi, Jian (2025-07-31)
Background: Volatile fatty acids (VFA) derived from acidogenic fermentation can be recovered as precursors for synthesizing value-added chemicals to replace those from fossil fuels. However, separating VFAs from the fermentation broth with complex constituents and a high-water content is an energy-intensive process. Results: This study developed an innovative membrane extraction technology, utilizing hydrophobic deep eutectic solvents (HDESs) as the acceptor phase along with an omniphobic membrane contactor for efficient extraction of anhydrous VFAs. All tested HDESs, three terpene-based type V HDESs and two tetraalkylammonium halide-based type III HDESs, were found to effectively extract VFAs at pH 3, with extraction recovery percentages (ERPs) up to 80% and 92% for 4 C- and 5 C- VFAs, respectively. However, the ERP of type V HDESs decreased significantly when the aqueous phase was adjusted to pH 6. Molecular simulations suggest that the VFA-HDES interactions vary with VFA dissociation, where the ion-dipole interactions between VFA conjugate bases and hydrogen bond donors at near-neutral pH conditions may destabilize the type V HDES structure and lead to reduced extraction efficiency. The temperature increases from 25 °C to 55 °C did not significantly impact VFA distribution, but a higher temperature could enhance cross-membrane mass transfer. Conclusions: This study demonstrated a novel continuous VFA extraction technology based on HDESs and elucidates the impact of temperature, pH, impurities in real fermentate and the applicability of an integrated membrane system through combined experimental and computational approaches.
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.

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