Leveraging long short-term memory (LSTM)-based neural networks for modeling structure-property relationships of metamaterials from electromagnetic responses.
Ontology highlight
ABSTRACT:
SUBMITTER: Pillai P
PROVIDER: S-EPMC8452651 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundRecent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.ResultsWe propose a new model to detect and classify relations in text, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using open biomedical ontologies, specifically Chemical Entities of Biological Interest (ChEBI), Human Phenotype, and Gene Ontology. We assessed the performance of BO-LSTM with drug-drug interactions mentioned in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved the F1-score of both the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. We adapted an existing DDI extraction model with our ontology-based method, obtaining a higher F1 score than the original model. Furthermore, we developed and made available a corpus of 228 abstracts annotated with relations between genes and phenotypes, and demonstrated how BO-LSTM can be applied to other types of relations.ConclusionsOur findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.
| S-EPMC6323831 | biostudies-literature
Project description:Polymer networks built out of dynamic covalent bonds offer the potential to translate the control and tunability of chemical reactions to macroscopic physical properties. Under conditions at which these reactions occur, the topology of covalent adaptable networks (CANs) can rearrange, meaning that they can flow, self-heal, be remolded, and respond to stimuli. Materials with these properties are necessary to fields ranging from sustainability to tissue engineering; thus the conditions and time scale of network rearrangement must be compatible with the intended use. The mechanical properties of CANs are based on the thermodynamics and kinetics of their constituent bonds. Therefore, strategies are needed that connect the molecular and macroscopic worlds. In this Perspective, we analyze structure-reactivity-property relationships for several classes of CANs, illustrating both general design principles and the predictive potential of linear free energy relationships (LFERs) applied to CANs. We discuss opportunities in the field to develop quantitative structure-reactivity-property relationships and open challenges.
| S-EPMC9812368 | biostudies-literature
Project description:BackgroundThe accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.MethodsFirst, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.ResultsThe proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.ConclusionsThe presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.
| S-EPMC8532313 | biostudies-literature
Project description:The National Institute of Environmental Research, under the Ministry of Environment of Korea, provides two-day forecasts, through AirKorea, of the concentration of particulate matter with diameters of ≤ 2.5 μm (PM2.5) in terms of four grades (low, moderate, high, and very high) over 19 districts nationwide. Particulate grades are subjectively designated by human forecasters based on forecast results from the Community Multiscale Air Quality (CMAQ) and artificial intelligence (AI) models in conjunction with weather patterns. This study evaluates forecasts from the long short-term memory (LSTM) algorithm relative to those from CMAQ-solely and AirKorea using observations from 2019. The skills of the one-day PM2.5 forecasts over the 19 districts were 39–70% for CMAQ, 72–79% for LSTM, and 73–80% for AirKorea; the AI forecasts showed comparable skills to the human forecasters at AirKorea. The one-day forecast skill levels of high and very high PM2.5 pollution grades are 31–98%, 31–74%, and 39–81% for the CMAQ-solely, the LSTM, and the AirKorea forecasts, respectively. Despite good skills for forecasting the high and very high events, CMAQ-solely forecasts also generate substantially higher false alarm rates (up to 86%) than the LSTM and AirKorea forecasts (up to 58%). Hence, applying only the LSTM model to the CMAQ forecasts can yield reasonable forecast skill levels comparable to the operational AirKorea forecasts that elaborately combine the CMAQ model, AI models, and human forecasters. The present results suggest that applications of appropriate AI models can greatly enhance PM2.5 forecast skills for Korea in a more objective way. Supplementary Information The online version contains supplementary material available at 10.1007/s13143-022-00293-2.
| S-EPMC9483905 | biostudies-literature
Project description:We present the theory of electromagnetic energy propagation through a dispersive and absorbing hyperbolic metamaterial (HMM). In this way, the permittivity tensor components of HMM (especially, nanowire HMM) may appear to be hopeless, but as a non-trivial step, we find that they can be cast into more transparent forms. We find under the influence of an electromagnetic wave, the responses of nanowire HMM (multilayer HMM) in the directions perpendicular to and parallel to the optical axis are similar to those of Lorentz (Drude) and Drude (Lorentz) media, respectively. We obtain simple expressions for the electromagnetic energy density formula of both typical structures of HMMs, i.e., nanowire and multilayer HMMs. Numerical examples reveal the general characteristics of the direction-dependent energy storage capacity of both nanowire and multilayer HMMs. The results of this study may shed more physical insight into the optical characteristics of HMMs.
| S-EPMC9232634 | biostudies-literature
Project description:Modeling metal additive manufacturing processes is of great importance because it allows for the production of objects that are closer to the desired geometry and mechanical properties. Over-deposition often takes place during laser metal deposition, especially when the deposition head changes its direction and results in more material being melted onto the substrate. Modeling over-deposition is one of the necessary steps toward online process control, as a good model can be used in a closed-loop system to adjust the deposition parameters in real-time to reduce this phenomenon. In this study, we present a long-short memory neural network to model over-deposition. The model has been trained on simple geometries such as straight tracks, spiral and V-tracks made of Inconel 718. The model shows good generalization capabilities and can predict the height of more complex and previously unseen random tracks with limited performance loss. After the addition to the training dataset of a small amount of data coming from the random tracks, the performance of the model for such additional shapes improves significantly, making this approach feasible for more general applications as well.
| S-EPMC10320393 | biostudies-literature
Project description:The definition of a precise illumination region is essential in many applications where the electromagnetic field should be confined in some specific volume. By using conventional structures, it is difficult to achieve an adequate confinement distance (or volume) with negligible levels of radiation leakage beyond it. Although metamaterial structures and metasurfaces are well-known to provide high controllability of their electromagnetic properties, this feature has not yet been applied to solve this problem. We present a method of electromagnetic field confinement based on the generation of evanescent waves by means of metamaterial structures. With this method, the confinement volume can be controlled, namely, it is possible to define a large area with an intense field without radiation leakage. A prototype working in the microwave region has been implemented, and very good agreement between the measurements and the theoretical prediction of field distribution has been obtained.
| S-EPMC5123569 | biostudies-literature
Project description:Metamaterials with a Dirac-like cone dispersion at the center of the Brillouin zone behave like an isotropic and impedance-matched zero refractive index material at the Dirac-point frequency. Such metamaterials can be realized in the form of either bulk metamaterials with efficient coupling to free-space light or on-chip metamaterials that are efficiently coupled to integrated photonic circuits. These materials enable the interactions of a spatially uniform electromagnetic mode with matter over a large area in arbitrary shapes. This unique optical property paves the way for many applications, including arbitrarily shaped high-transmission waveguides, nonlinear enhancement, and phase mismatch-free nonlinear signal generation, and collective emission of many emitters. This review summarizes the Dirac-like cone-based zero-index metamaterials' fundamental physics, design, experimental realizations, and potential applications.
| S-EPMC8481486 | biostudies-literature
Project description:Electromagnetic (EM) metamaterial is a composite material with EM stealth properties, which is constructed by artificially reverse engineering metal split resonance rings (SRR). However, the greatest limitation of EM metamaterials is that they can only stealth at a fixed and lower frequency of EM waves, and modern processing techniques still cannot meet the accuracy requirements to fabric nano-size structural unit. Nano-sized and even ultra-small SRR at molecular level are promising arrays to realize the ability of EM stealth function at a higher frequency, although it has proven challenging to synthesize long, straight, connected molecular SRR, and also difficult to arrange those molecular SRR into a strict array. Here, the study overcomes this challenge and demonstrates that the fabric of polypyrrole molecular SRR achieves an ultra-small inner diameter of 2.49 Å and realizes the arrays arrangement at molecular level. Furthermore, the study exploits the EM stealth function and verifies that such arrays of molecular SRR with 2.49 Å have the ability to reach high-performance EM stealth in the range of 106 -1016 Hz. This design concept opens a pathway for developing new metamaterials with broadband EM wave stealth and also serves the wider range of new applications.
| S-EPMC10323625 | biostudies-literature
Project description:This paper presents an algorithm for real-time detection of the heart rate measured on a person's wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.
| S-EPMC8749621 | biostudies-literature