The triplet–triplet annihilation (TTA) ratio and the rate coefficient (kTT) of TTA are key factors in estimating the contribution of triplet excitons to radiative singlet excitons in fluorescent TTA ...organic light‐emitting diodes. In this study, deep learning models are implemented to predict key factors from transient electroluminescence (trEL) data using new numerical equations. A new TTA model is developed that considers both polaron and exciton dynamics, enabling the distinction between prompt and delayed singlet decays with a fundamental understanding of the mechanism. In addition, deep learning models for predicting the kinetic coefficients and TTA ratio are established. After comprehensive optimization inspired by photophysics, determination coefficient values of 0.992 and 0.999 are achieved in the prediction of kTT and TTA ratio, respectively, indicating a nearly perfect prediction. The contribution of each kinetic parameter of polaron and exciton dynamics to the trEL curve is discussed using various deep‐learning models.
An advanced triplet‐triplet annihilation (TTA) decay model is implemented and deep learning (DL) model for the prediction of kinetic coefficients is developed using the new TTA model, which DL model presented superior predictability by obtaining determination coefficient value of 0.992 and 0.999 of the TTA rate coefficient and TTA ratio.
•We present a new EEG data set and evaluation tasks with well-formed annotations.•We propose a novel, fully end-to-end deep model (CEEDNet) for screening EEG signals.•CEEDNet aims to bring all ...functions for EEG analysis in a seamless learnable fashion.•The proposed CEEDNet significantly improves accuracy compared to existing methods.•Extensive experiments and analyses provide the in-depth property of our CEEDNet.
Based on the Chung-Ang University Hospital EEG (CAUEEG) dataset, this paper presents a new fully end-to-end deep learning approach for screening EEG signals, called the CAUEEG End-to-end Deep neural Network (CEEDNet). The core idea of CEEDNet is to combine all the functional elements used to analyze EEG signals in a seamless learnable fashion. CEEDNet pursues to utilize the domain characteristics of EEG signals while minimizing unnecessary human intervention. On the CAUEEG-Dementia and CAUEEG-Abnormal evaluation tasks, CEEDNet produced a significant im provement in accuracy and other metrics compared with existing methods. Display omitted
For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG (CAUEEG), which has event history, patient’s age, and corresponding diagnosis labels. We also designed two reliable evaluation tasks for the low-cost, non-invasive diagnosis to detect brain disorders: i) CAUEEG-Dementia with normal, mci, and dementia diagnostic labels and ii) CAUEEG-Abnormal with normal and abnormal. Based on the CAUEEG dataset, this paper proposes a new fully end-to-end deep learning model, called the CAUEEG End-to-end Deep neural Network (CEEDNet). CEEDNet pursues to bring all the functional elements for the EEG analysis in a seamless learnable fashion while restraining non-essential human intervention. Extensive experiments showed that our CEEDNet significantly improves the accuracy compared with existing methods, such as machine learning methods and Ieracitano-CNN (Ieracitano et al., 2019), due to taking full advantage of end-to-end learning. The high ROC-AUC scores of 0.9 on CAUEEG-Dementia and 0.86 on CAUEEG-Abnormal recorded by our CEEDNet models demonstrate that our method can lead potential patients to early diagnosis through automatic screening.
This study examined the electrical and self-sensing capacities of ultra-high-performance fiber-reinforced concrete (UHPFRC) with and without carbon nanotubes (CNTs). For this, the effects of steel ...fiber content, orientation, and pore water content on the electrical and piezoresistive properties of UHPFRC without CNTs were first evaluated. Then, the effect of CNT content on the self-sensing capacities of UHPFRC under compression and flexure was investigated. Test results indicated that higher steel fiber content, better fiber orientation, and higher amount of pore water led to higher electrical conductivity of UHPFRC. The effects of fiber orientation and drying condition on the electrical conductivity became minor as sufficiently high amount of steel fibers, 3% by volume, was added. Including only steel fibers did not impart UHPFRC with piezoresistive properties. Addition of CNTs substantially improved the electrical conductivity of UHPFRC. Under compression, UHPFRC with a CNT content of 0.3% or greater had a self-sensing ability that was activated by the formation of cracks, and better sensing capacity was achieved by including greater amount of CNTs. Furthermore, the pre-peak flexural behavior of UHPFRC was precisely simulated with a fractional change in resistivity when 0.3% CNTs were incorporated. The pre-cracking self-sensing capacity of UHPFRC with CNTs was more effective under tensile stress state than under compressive stress state.
Terahertz (THz) radiation is a powerful tool with widespread applications ranging from imaging, sensing, and broadband communications to spectroscopy and nonlinear control of materials. Future ...progress in THz technology depends on the development of efficient, structurally simple THz emitters that can be implemented in advanced miniaturized devices. Here, it is shown how the natural electronic anisotropy of layered conducting transition metal oxides enables the generation of intense terahertz radiation via the transverse thermoelectric effect. In thin films grown on off‐cut substrates, femtosecond laser pulses generate ultrafast out‐of‐plane temperature gradients, which in turn launch in‐plane thermoelectric currents, thus allowing efficient emission of the resulting THz field out of the film structure. This scheme is demonstrated in experiments on thin films of the layered metals PdCoO2 and La1.84Sr0.16CuO4, and model calculations that elucidate the influence of the material parameters on the intensity and spectral characteristics of the emitted THz field are presented. Due to its simplicity, the method opens up a promising avenue for the development of highly versatile THz sources and integrable emitter elements.
Intense terahertz radiation generation via the transverse thermoelectric effect in layered conducting transition metal oxides is demonstrated . Ultrafast out‐of‐plane temperature gradients, induced by femtosecond laser pulses on thin films grown on off‐cut substrates, launch in‐plane thermoelectric currents leading to efficient THz emission. This approach offers a simple and promising avenue for versatile THz sources and integrable emitter elements.
Evidence for the associations between mental illness and the likelihood of a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result and the clinical outcomes of COVID-19 is ...scarce. We aimed to investigate these associations with data from a national register in South Korea.
A nationwide cohort study with propensity score matching was done in South Korea using data collected from the Health Insurance Review and Assessment Service of Korea. We defined mental illness as present if one of the relevant ICD-10 codes was recorded at least twice within 1 year for an outpatient or inpatient. Severe mental illness was considered as non-affective or affective disorders with psychotic features. We included all patients aged older than 20 years who were tested for SARS-CoV-2 through services facilitated by the Korea Centers for Disease Control and Prevention, the Health Insurance Review and Assessment Service of Korea, and the Ministry of Health and Welfare, South Korea. We investigated the primary outcome (SARS-CoV-2 test positivity) in the entire cohort and the secondary outcomes (severe clinical outcomes of COVID-19: death, admission to the intensive care unit, or invasive ventilation) among those who tested positive.
Between Jan 1 and May 15, 2020, 216 418 people were tested for SARS-CoV-2, of whom 7160 (3·3%) tested positive. In the entire cohort with propensity score matching, 1391 (3·0%) of 47 058 patients without a mental illness tested positive for SARS-CoV-2, compared with 1383 (2·9%) of 48 058 with a mental illness (adjusted odds ratio OR 1·00, 95% CI 0·93-1·08). Among the patients who tested positive for SARS-CoV-2, after propensity score matching, 109 (8·3%) of 1320 patients without a mental illness had severe clinical outcomes of COVID-19 compared with 128 (9·7%) of 1320 with a mental illness (adjusted OR 1·27, 95% CI 1·01-1·66).
Diagnosis of a mental illness was not associated with increased likelihood of testing positive for SARS-CoV-2. Patients with a severe mental illness had a slightly higher risk for severe clinical outcomes of COVID-19 than patients without a history of mental illness. Clinicians treating patients with COVID-19 should be aware of the risk associated with pre-existing mental illness.
National Research Foundation of Korea.
Direct exploring the electroluminescence (EL) of organic light‐emitting diodes (OLEDs) is a challenge due to the complicated processes of polarons, excitons, and their interactions. This study ...demonstrated the extraction of the polaron dynamics from transient EL by predicting the recombination coefficient via artificial intelligence, overcoming multivariable kinetics problems. The performance of a machine learning (ML) model trained by various EL decay curves is significantly improved using a novel featurization method and input node optimization, achieving an R2 value of 0.947. The optimized ML model successfully predicts the recombination coefficients of actual OLEDs based on an exciplex‐forming cohost, enabling the quantitative understanding of the overall polaron behavior under various electrical excitation conditions.
Extraction of polaron dynamics from EL in organic light‐emitting diodes is realized by AI. The quantitative understanding of polaron dynamics in complicated light‐emitting processes is facilitated by predicting the recombination coefficient.
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