More Like this

1. Organic solar powered greenhouse performance optimization and global economic opportunity

   https://doi.org/10.1039/d1ee03474j  

   Ravishankar, Eshwar ; Booth, Ronald E. ; Hollingsworth, Joseph A. ; Ade, Harald ; Sederoff, Heike ; DeCarolis, Joseph F. ; O'Connor, Brendan T. ( April 2022 , Energy & Environmental Science)

   Greenhouses conserve land and water while increasing crop production, making them an attractive system for low environmental impact agriculture. Yet, to achieve this goal, there is a need to reduce their large energy demand. Employing semitransparent organic solar cells (OSCs) on greenhouse structures provide an opportunity to offset the greenhouse energy needs while maintaining the lighting needs of the plants. However, the design trade-off involved in optimizing solar power generation and crop productivity to maximize greenhouse economic value is yet to be studied in detail. Here, a functional plant growth model is integrated with a dynamic energy model that includes supplemental lighting to optimize the economics of growing lettuce and tomato. The greenhouse optimization considers 64 different OSC active layers with varying roof coverage for 25 distinct climates providing a global perspective. We find that crop yield is the primary economic driver, and that crop yield can be maintained in OSC-greenhouses across diverse climates. The crop productivity along with the energy produced by the OSCs results in improved net present value of the OSC-greenhouses relative to conventional systems in most climates for both lettuce and tomato. In addition, we find common solar cell active layers that maximize greenhouse economic valuemore »resulting in guidelines for scaling up OSC-greenhouse design. Through this model framework, we highlight the opportunity for OSCs in greenhouses, uncover designs and locations that provide the most value, and provide a basis for further development of OSC-greenhouses to achieve a sustainable means of food production.« less
2. Identifying and characterizing pesticide use on 9,000 fields of organic agriculture

   https://doi.org/10.1038/s41467-021-25502-w  

   Larsen, Ashley E. ; Claire Powers, L. ; McComb, Sofie ( September 2021 , Nature Communications)

   Notwithstanding popular perception, the environmental impacts of organic agriculture, particularly with respect to pesticide use, are not well established. Fueling the impasse is the general lack of data on comparable organic and conventional agricultural fields. We identify the location of ~9,000 organic fields from 2013 to 2019 using field-level crop and pesticide use data, along with state certification data, for Kern County, CA, one of the US’ most valuable crop producing counties. We parse apart how being organic relative to conventional affects decisions to spray pesticides and, if spraying, how much to spray using both raw and yield gap-adjusted pesticide application rates, based on a global meta-analysis. We show the expected probability of spraying any pesticides is reduced by about 30 percentage points for organic relative to conventional fields, across different metrics of pesticide use including overall weight applied and coarse ecotoxicity metrics. We report little difference, on average, in pesticide use for organic and conventional fields that do spray, though observe substantial crop-specific heterogeneity.
3. “We Have Been Part of the Response”: The Effects of COVID-19 on Community and Allotment Gardens in the Global North

   https://doi.org/10.3389/fsufs.2021.732641  

   Schoen, Victoria ; Blythe, Chris ; Caputo, Silvio ; Fox-Kämper, Runrid ; Specht, Kathrin ; Fargue-Lelièvre, Agnès ; Cohen, Nevin ; Poniży, Lidia ; Fedeńczak, Konstancja ( October 2021 , Frontiers in Sustainable Food Systems)

   Throughout history, urban agriculture practitioners have adapted to various challenges by continuing to provide food and social benefits. Urban gardens and farms have also responded to sudden political, economic, ecological, and social crises: wartime food shortages; urban disinvestment and property abandonment; earthquakes and floods; climate-change induced weather events; and global economic disruptions. This paper examines the effects on, and responses by, urban farms and gardens to the COVID-19 pandemic. The paper is based on data collected in the summer of 2020 at the onset of the pandemic when cities were struggling with appropriate responses to curb its spread. It builds on an international research project (FEW-meter) that developed a methodology to measure material and social benefits of urban agriculture (UA) in five countries (France, Germany, Poland, UK and USA) over two growing seasons, from a Food-Energy-Water nexus perspective. We surveyed project partners to ascertain the effects of COVID-19 on those gardens and farms and we interviewed policy stakeholders in each country to investigate the wider impacts of the pandemic on UA. We report the results with respect to five key areas: (1) garden accessibility and service provision during the pandemic; (2) adjustments to operational arrangements; (3) effects on production; (4)more »support for urban farms and gardens through the pandemic; and (5) thoughts about the future of urban agriculture in the recovery period and beyond. The paper shows that the pandemic resulted in multiple challenges to gardens and farms including the loss of ability to provide support services, lost income, and reductions in output because of reduced labor supply. But COVID-19 also created several opportunities: new markets to sell food locally; more time available to gardeners to work in their allotments; and increased community cohesion as neighboring gardeners looked out for one another. By illustrating the range of challenges faced by the pandemic, and strategies to address challenges used by different farms and gardens, the paper illustrates how gardens in this pandemic have adapted to become more resilient and suggests lessons for pandemic recovery and longer-term planning to enable UA to respond to future public health and other crises.« less
4. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEGmore »channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
5. Potential Environmental Impacts of Peanut Using Water Footprint Assessment: A Case Study in Georgia

   https://doi.org/10.3390/agronomy12040930  

   Deepa, Raveendranpillai ; Anandhi, Aavudai ; Bailey, Nathaniel O. ; Grace, Johnny M. ; Betiku, Omolola C. ; Muchovej, James J. ( April 2022 , Agronomy)

   The recent decade has witnessed an increase in irrigated acreage in the southeast United States due to the shift in cropping patterns, climatic conditions, and water availability. Peanut, a major legume crop cultivated in Georgia, Southeast United States, has been a staple food in the American household. Regardless of its significant contribution to the global production of peanuts (fourth largest), studies related to local or regional scale water consumption in peanut production and its significant environmental impacts are scarce. Therefore, the present research contributes to the water footprint of peanut crops in eight counties of Georgia and its potential ecological impacts. The impact categories relative to water consumption (water depletion—green and blue water scarcity) and pesticide use (water degradation—potential freshwater ecotoxicity) using crop-specific characterization factors are estimated for the period 2007 to 2017 at the mid-point level. These impacts are transformed into damages to the area of protection in terms of ecosystem quality at the end-point level. This is the first county-wise quantification of the water footprint and its impact assessment using ISO 14046 framework in the southeast United States. The results suggest inter-county differences in water consumption of crops with higher blue water requirements than green and grey water.more »According to the water footprint analysis of the peanut crop conducted in this study, additional irrigation is recommended in eight Georgia counties. The mid-point level impact assessment owing to water consumption and pesticide application reveals that the potential freshwater ecotoxicity impacts at the planting and growing stages are higher for chemicals with high characterization factors regardless of lower pesticide application rates. Multiple regression analysis indicates blue water, yield, precipitation, maximum surface temperature, and growing degree days are the potential factors influencing freshwater ecotoxicity impacts. Accordingly, a possible impact pathway of freshwater ecotoxicity connecting the inventory flows and the ecosystem quality is defined. This analysis is helpful in the comparative environmental impact assessments for other major crops in Georgia and aids in water resource management decisions. The results from the study could be of great relevance to the southeast United States, as well as other regions with similar climatic zones and land use patterns. The assessment of water use impacts relative to resource availability can assist farmers in determining the timing and layout of crop planting.« less