More Like this

1. Ultrahigh‐Capacity Rocksalt Cathodes Enabled by Cycling‐Activated Structural Changes

   https://doi.org/10.1002/aenm.202300221  

   Ahn, Juhyeon ; Giovine, Raynald ; Wu, Vincent C. ; Koirala, Krishna Prasad ; Wang, Chongmin ; Clément, Raphaële J. ; Chen, Guoying ( May 2023 , Advanced Energy Materials)

   Mn‐redox‐based oxides and oxyfluorides are considered the most promising earth‐abundant high‐energy cathode materials for next‐generation lithium‐ion batteries. While high capacities are obtained in high‐Mn content cathodes such as Li‐ and Mn‐rich layered and spinel‐type materials, local structure changes and structural distortions ( often lead to voltage fade, capacity decay, and impedance rise, resulting in unacceptable electrochemical performance upon cycling. In the present study, structural transformations that exploit the high capacity of Mn‐rich oxyfluorides while enabling stable cycling, in stark contrast to commonly observed structural changes that result in rapid performance degradation, are reported. It is shown that upon cycling of a cation‐disordered rocksalt (DRX) cathode (Li_1.1Mn_0.8Ti_0.1O_1.9F_0.1, an ultrahigh capacity of ≈320 mAh g⁻¹(energy density of ≈900 Wh kg⁻¹) can be obtained through dynamic structural rearrangements upon cycling , along with a unique voltage profile evolution and capacity rise. At high voltage, the presence of Mn⁴⁺and Li⁺vacancies promotes local cation ordering, leading to the formation of domains of a “δphase” within the disordered framework. On deep discharge, Mn⁴⁺reduction, along with Li⁺insertion transform the structure to a partially ordered DRX phase with aβ′‐LiFeO₂‐type arrangement. At the nanoscale, domains of the in situ formed phases are randomly oriented, allowing highly reversible structural changes and stable electrochemical cycling. These new insights not only help explain the superior electrochemical performance of high‐Mn DRXbut also provide guidance for the future development of Mn‐based, high‐energy density oxide, and oxyfluoride cathode materials.

    

   more » « less
2. Mayfly emergence production and body length response to hydrology in a tropical lowland stream

   https://doi.org/10.7717/peerj.9883  

   Gutiérrez-Fonseca, Pablo E. ; Ramírez, Alonso ( January 2020 , PeerJ)

   null (Ed.)

   Background Hydrological impacts on aquatic biota have been assessed in numerous empirical studies. Aquatic insects are severely affected by population declines and consequent diversity loss. However, many uncertainties remain regarding the effects of hydrology on insect production and the consequences of energy transfer to the terrestrial ecosystem. Likewise, sublethal effects on insect morphology remain poorly quantified in highly variable environments. Here, we characterized monthly fluctuation in benthic and emerged biomass of Ephemeroptera in a tropical lowland stream. We quantified the proportion of mayfly production that emerges into the riparian forest. We also examined the potential morphological changes in Farrodes caribbianus (the most abundant mayfly in our samples) due to environmental stress. Methods We collected mayflies (nymphs and adults) in a first-order stream in Costa Rica. We compared benthic and adult biomass from two years’ worth of samples, collected with a core sampler (0.006 m 2 ) and a 2 m 2 -emergence trap. The relationship between emergence and annual secondary production (E/P) was used to estimate the Ephemeroptera production that emerged as adults. A model selection approach was used to determine the relationship between environmental variables that were collected monthly and the emergent biomass. To determine potential departures from perfect bilateral symmetry, we evaluated the symmetry of two morphological traits (forceps and forewing) of F. caribbianus adults. We used Spearman’s rank correlation coefficients (ρ) to examine potential changes in adult body length as a possible response to environmental stress. Results Benthic biomass was variable, with peaks throughout the study period. However, peaks in benthic biomass did not lead to increases in mayfly emergence, which remained stable over time. Relatively constant mayfly emergence suggests that they were aseasonal in tropical lowland streams. Our E/P estimate indicated that approximately 39% and 20% (for 2002 and 2003, respectively) of the nymph production emerged as adults. Our estimated proportion of mayfly production transferred to terrestrial ecosystems was high relative to reports from temperate regions. We observed a strong negative response of F . caribbianus body length to increased hydrology (Spearman: ρ = −0.51, p < 0.001), while slight departures from perfect symmetry were observed in all traits. Conclusion Our two years study demonstrates that there was large temporal variability in mayfly biomass that was unrelated to hydrological fluctuations, but potentially related to trophic interactions (e.g., fish predation). Body length was a good indicator of environmental stress, which could have severe associated costs for mayfly fitness in ecosystems with high temporal variation. Our results highlight the complex ecological and evolutionary dynamics of tropical aquatic insects, and the intricate connection between aquatic and terrestrial ecosystems. 

   more » « less
3. Resistance Is Not Futile: Grain Resistance Controls on Observed Critical Shields Stress Variations

   https://doi.org/10.1029/2018JF004817  

   Yager, Elowyn M. ; Schmeeckle, Mark W. ; Badoux, Alexandre ( December 2018 , Journal of Geophysical Research: Earth Surface)

   Estimates of the onset of sediment motion are integral for flood protection and river management but are often highly inaccurate. The critical shear stress (τ^*_c) for grain entrainment is often assumed constant, but measured values can vary by almost an order of magnitude between rivers. Such variations are typically explained by differences in measurement methodology, grain size distributions, or flow hydraulics, whereas grain resistance to motion is largely assumed to be constant. We demonstrate that grain resistance varies strongly with the bed structure, which is encapsulated by the particle height above surrounding sediment (protrusion,p) and intergranular friction (ϕ_f). We incorporate these parameters into a novel theory that correctly predicts resisting forces estimated in the laboratory, field, and a numerical model. Our theory challenges existing models, which significantly overestimate bed mobility. In our theory, small changes inpandϕ_fcan induce large changes inτ^*_cwithout needing to invoke variations in measurement methods or grain size. A data compilation also reveals that scatter in empirical values ofτ^*_ccan be partly explained by differences inpbetween rivers. Therefore, spatial and temporal variations in bed structure can partly explain the deviation ofτ^*_cfrom an assumed constant value. Given that bed structure is known to vary with applied shear stresses and upstream sediment supply, we conclude that a constantτ^*_cis unlikely. Values ofτ^*_care not interchangeable between streams, or even through time in a given stream, because they are encoded with the channel history.

    

   more » « 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 EEG 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. 

   more » « less
5. Informer: Irregular Traffic Detection for Containerized Microservices RPC in the Real World

   Chen, Jiyu Chen ; Huang, Heqing ; Chen, Hao ( November 2019 , ACM/IEEE Workshop on Security and Privacy in Edge Computing (Edge S&P))

   Containerized microservices have been widely deployed in industry. Meanwhile, security issues also arise. Many security enhancement mechanisms for containerized microservices require predefined rules and policies. However, it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data. Hence, automatic policy generation becomes indispensable. In this paper, we focus on the automatic solution for the security problem: irregular traffic detection for RPCs. We propose Informer, which is a two-phase machine learning framework to track the traffic of each RPC and report anomalous points automatically. Firstly, we identify RPC chain patterns by density-based clustering techniques and build a graph for each critical pattern. Next, we solve the irregular RPC traffic detection problem as a prediction problem for time-series of attributed graphs by leveraging spatial-temporal graph convolution networks. Since the framework builds multiple models and makes individual predictions for each RPC chain pattern, it can be efficiently updated upon legitimate changes in any of the graphs. In evaluations, we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from an large Kubernetes system for two weeks. We provide two case studies of detected real-world threats. As a result, our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario, which demonstrates the effectiveness of Informer. 

   more » « less