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Cable is crucial to the control and instrumentation of machines and facilities. Therefore, early diagnosis of cable faults is the most effective approach to prevent system downtime and maximize productivity. We focused on a “soft fault state”, which is a transient state that eventually becomes a permanent fault —open-circuit and short-circuit. However, the issue of soft fault diagnosis has not been considered enough in previous research, which could not provide crucial information, such as fault severity, to support maintenance. In this study, we focused on solving soft fault problem by estimating fault severity to diagnose early-stage faults. The proposed diagnosis method comprised a novelty detection and severity estimation network. The novelty detection part is specially designed to deal with varying operating conditions of industrial applications. First, an autoencoder calculates anomaly scores to detect faults using three-phase currents. If a fault is detected, a fault severity estimation network, wherein long short-term memory and attention mechanisms are integrated, estimates the fault severity based on the time-dependent information of the input. Accordingly, no additional equipment, such as voltage sensors and signal generators, is required. The conducted experiments demonstrated that the proposed method successfully distinguishes seven different soft fault degrees.

Although sentiment analysis for COVID-19 tweets is becoming popular, no study has mined a sentiment other than polarity. This study aims to extract ‘disaster’ sentiment by using various machine learning models, statistically validate sentiment markers extracted by deep learning, and discuss the potentials of ‘disaster’ as valid sentiment marker. A total of 7,613 disaster tweets from Kaggle site were utilized to train nine machine learning models. A total of 15,619 tweets in English sent from USA were downloaded using streaming API with keywords of Covid, and Omicron, respectively and were classified into disaster/non-disaster categories using the four best performing models: MNB, deep learning, USE and BERT. Principal component analysis, correlation analysis and regression analysis were performed to determine the psychometric properties. Cronbach Alpha for 13 sentiment markers was 0.71. All 4 machine learning markers were loaded in a factor. A higher level of unfavorable emotions (e.g., fear), a lower level of favorable emotions (e.g., joy), and a higher level of negative polarity were found during surging Omicron variants than early onset of COVID-19. The higher frequency of disaster tweets was found during surging Omicron variants than the early onset of COVID-19. Our study revealed that disaster tweets were characterized to be a higher level of unfavorable emotions and negative polarity, and a lower level of favorable emotions. Since disaster as a sentiment marker was evidently reliable and valid, it should be a part of the sentiment analysis in describing the global health issues.

Context/Objective: To evaluate the accuracy of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) motor examination in individuals with spinal cord injury (SCI) with motor grade 0 or 1 and analyze its degree of concordance with needle electromyography (EMG) findings for each key muscle. Design: Retrospective study. Setting: University hospital in Goyang, Korea. Participants: Individuals with SCI admitted to the Department of Rehabilitation from January 2013 to June 2019. Interventions: In the enrolled persons, needle EMG was performed on muscles with motor grade 0 or 1 on ISNCSCI examination, and muscle contraction was confirmed through the detection of motor unit action potential. Outcome measures: The agreement between motor examination and needle EMG findings was analyzed. Results: In 175 key muscles, needle EMG findings in 115 and 60 muscles evaluated as grades 0 and 1 on ISNCSCI examination showed 80% and 50% agreements, respectively. We found a fair agreement between motor examination and needle EMG findings (κ = 0.309, P

Background Antibiotic resistance threatens public health worldwide, and inappropriate use of antibiotics is one of the main causes. Antibiotic use for asymptomatic bacteriuria (ABU) has been defined as “antibiotics never events”, and urinary tract infection (UTI) is one of the most common infectious diseases for which antibiotics are prescribed in Korea. To establish an effective antimicrobial stewardship strategy, a qualitative assessment of antibiotic use in actual clinical syndrome is necessary. Methods Cases of positive urine cultures (≥105 CFU/ml), performed in inpatient, outpatient, and emergency departments in April 2021 were screened in 26 hospitals located throughout Korea. Cases were classified into ABU, lower UTI, and upper UTI. The appropriateness of antibiotic use was retrospectively evaluated by infectious disease specialists using quality indicators based on the domestic clinical guideline for ABU and UTI. Figure 1.Study flow diagram Results A total of 2697 cases of ABU or UTI were included. The appropriateness of antibiotic use was assessed in 1157 cases with asymptomatic bacteriuria, 677 and 863 cases with lower and upper UTI (Figure 1). Antibiotics were prescribed in 21.7% (251 of 1157) of ABU without appropriate indication. Of 66 ABU cases with appropriate indication in which prophylactic antibiotics were prescribed, the duration of antibiotics was adequate in only 34.8% (Table 1). For lower UTI, the appropriateness of empirical and definite antibiotics was 77.8% (527 of 677) and 68.0% (353 of 519). In terms of upper UTI, 86.3% (745 of 863) and 78.2% (583 of 746) was appropriate, respectively. The duration of antibiotics was adequate in 65.7% (421 of 641) of lower UTI and 77.9% (592 of 760) in upper UTI (Table 2, 3). Conclusion This nationwide qualitative assessment of antibiotic use in ABU and UTI revealed that a significant proportion of antibiotics were prescribed inappropriately and, furthermore the duration of antibiotics was prolonged unnecessarily. Interventions for appropriate antibiotic use in ABU and UTI at the national level are required. Disclosures All Authors: No reported disclosures.

The function of peripheral nociceptors is frequently tuned by the action of G protein-coupled receptors (GPRs) that are expressed in them, which contribute to pain alteration. Expanding new information on such GPRs and predicting their potential outcomes can help to construct new analgesic strategies based on their modulations. In this context, we attempted to present a new GPR not yet acknowledged for its pain association. Gpr83 exhibits relatively high expressions in the peripheral nervous system compared to other tissues when we mined and reconstructed Gene Expression Omnibus (GEO) metadata, which we confirmed using immunohistochemistry on murine dorsal root ganglia (DRG). When Gpr83 expression was silenced in DRG, neuronal and behavioral nociception were all downregulated. Pathologic pain in hind paw inflammation and chemotherapy-induced peripheral neuropathy were also alleviated by this Gpr83 knockdown. Dependent on exposure time, the application of a known endogenous Gpr83 ligand PEN showed differential effects on nociceptor responses in vitro. Localized PEN administration mitigated pain in vivo, probably following Gq/11-involved GPR downregulation caused by the relatively constant exposure. Collectively, this study suggests that Gpr83 action contributes to the tuning of peripheral pain sensitivity and thus indicates that Gpr83 can be among the potential GPR targets for pain modulation.

This article proposes a distributed estimation algorithm that uses local information about the neighbors through sensing or communication to design an estimation-based cooperative control of the stochastic multiagent system (MAS). The proposed distributed estimation algorithm solely relies on local sensing information rather than exchanging estimated state information from other agents, as is commonly required in conventional distributed estimation methods, reducing communication overhead. Furthermore, the proposed method allows interactions between all agents, including non-neighboring agents, by establishing a virtual fully connected network with the MAS state information independently estimated by each agent. The stability of the proposed distributed estimation algorithm is theoretically verified. Numerical simulations demonstrate the enhanced performance of the estimation-based linear and nonlinear control. In particular, using the virtual fully connected network concept in the MAS with the sensing/communication range, the flock configuration can be tightly controlled within the desired boundary, which cannot be achieved through the conventional flocking methods.

Haptics researchers often endeavor to deliver realistic vibrotactile feedback through broad-bandwidth actuators; however, these actuators typically generate only single-axis vibrations, not 3D vibrations like those that occur in natural tool-mediated interactions. Several three-to-one (321) dimensional reduction algorithms have thus been developed to combine 3D vibrations into 1D vibrations. Surprisingly, the perceptual quality of 321-converted vibrations has never been comprehensively compared to rendering of the original 3D signals. In this study, we develop a multi-dimensional vibration rendering system using a magnetic levitation haptic interface. We verify the system's ability to generate realistic 3D vibrations recorded in both tapping and dragging interactions with four surfaces. We then conduct a study with 15 participants to measure the perceived dissimilarities between five 321 algorithms (SAZ, SUM, VM, DFT, PCA) and the original recordings. The resulting perceptual space is investigated with multiple regression and Procrustes analysis to unveil the relationship between the physical and perceptual properties of 321-converted vibrations. Surprisingly, we found that participants perceptually discriminated the original 3D vibrations from all tested 1D versions. Overall, our results indicate that spectral, temporal, and directional attributes may all contribute to the perceived similarities of vibration signals., IEEE Transactions on Haptics, 15 (3), ISSN:1939-1412

With the development of smart factories, deep learning, which automatically extracts features and diagnoses faults, has become an important approach for fault diagnosis. In this article, a novel interturn short-circuit fault (ISCF) diagnosis approach using an attention-based recurrent neural network is proposed. An encoder–decoder architecture using an attention mechanism diagnoses the ISCF by estimating a fault indicator that directly reflects the severity of the fault, using currents and rotational speed signals as inputs. The attention mechanism helps the decoding process in accurate diagnosis and solves the long-term dependence problem of the encoder–decoder structure. The proposed algorithm uses only three-phase current and rotational speed as the inputs to evaluate the severity of the ISCF and enable early stage diagnosis of ISCF. The diagnosis of ISCF is achieved in various operating points and fault conditions, and no additional sensors, such as voltage and vibration sensors, are required. Experimental results for various operating and fault conditions demonstrate that the proposed method effectively diagnoses ISCFs.

Exploiting the long-range polarizability of an electrolyte based on ion migration, electric double-layer transistors (EDLTs) can be constructed in an unconventional configuration; here, the gate electrode is placed coplanarly with the device channel. In this paper, we demonstrate the influence of the distance factors of the electrolyte layer on the operation of EDLTs with a coplanar gate. As the promptness of the electric double-layer formation depends on the distance between the channel and the gate, the dynamic characteristics of a remote-gated transistor degrade with long distances. To suppress this degradation, we suggest using multiple coplanar floating gates bridged through ionic dielectric layers. Unlike remotely gated EDLTs that utilize a single extended electrolyte layer, the devices with multiple segmented electrolyte layers operate effectively even when they are gated from a distance longer than 1 mm.