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Abstract This work proposes smart auto mining (SAM) for resource‐efficient mining in a blockchain network. The SAM algorithm stops the miners when there is zero pending transaction and starts the miner when there is at least one transaction sent into the network. The miner listens to the network to identify when a transaction has been made by a node. The model does not need any instruction to start mining when there is a pending transaction. The results show that a private Ethereum network produced over 300% more blocks in a 12‐h period with 599,950 transactions compared to when SAM is applied. The proposed algorithm is also able to reduce the storage used by the chaindata by 14%. The overhead of mining is decreased by reducing the production of empty blocks in the network which saves energy, storage space, network bandwidth and computational complexity.

Abstract The erythrocyte sedimentation rate (ESR) is a non-specific blood test for determining inflammatory conditions. However, the long measurement time (60 min) to obtain ESR is an obstacle for a prompt evaluation. In this study, to reduce the measurement time of ESR, deep neural networks (DNNs) were applied to the sedimentation tendency of blood samples. DNNs using multilayer perceptron (MLP), long short-term memory (LSTM), and gated recurrent unit (GRU) were assessed and compared to determine a suitable length of time for the input sequence. To avoid overfitting, a stacking ensemble learning was adopted, which combines multiple models by using a meta model. Four meta models were compared: mean, median, least absolute shrinkage and selection operator, and partial least squares regression (PLSR) schemes. From the empirical results, LSTM and GRU models have better prediction than MLP over sequence lengths of 5 to 20 min. The decrease in $$\overline{\mathrm{MAPE} }$$ MAPE ¯ and $$\overline{\mathrm{RMSE} }$$ RMSE ¯ of GRU and LSTM was attenuated after a sequence length of 15 min, so the input sequence length is determined as 15 min. In terms of the meta model, the statistical comparison suggests that GRU combined with PLSR (GRU–PLSR) is the best case. Then, the GRU–PLSR was tested for prediction of ESR data obtained from periodontitis patients to check its applicability to a specific disease. The Bland–Altman plot shows acceptable agreement between measured and predicted ESR values. Based on the results, the GRU–PLSR can predict ESR with improved performance within 15 min and has potential applicability to ESR data with inflammatory and non-inflammatory conditions.

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rule-based diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

Background : : This study aimed to identify chronicity predictors of pediatric primary immune thrombocytopenia (ITP). Methods : : This study retrospectively reviewed the medical records of patients with primary pediatric ITP admitted to a tertiary medical center between 2010 and 2021. Forty-five patients with a platelet count

Triplet–triplet fusion (TTF) has been an efficiency-enhancing mechanism in fluorescent organic light-emitting diodes (OLEDs) caused by the collision of two triplet excitons. However, achieving a high TTF ratio in fluorescent OLEDs has been difficult despite device strategies to maximize the triplet exciton density within a narrow recombination zone near the electron blocking layer (EBL) due to charge imbalance and hole accumulation between the TTF type emitter and EBL. Based on a trap-detrap mechanism, we were able to realize an improved TTF ratio and reduce hole accumulation by adding a TTF-assisting material (TTF-AM) in the TTF emitter. The TTF-AM served as the hole transport channel, triggering hole trap and detrap while improving the hole transport character of the emitting layer. Through the process of hole detrapping, the improved hole transport properties balanced carriers and generated more triplet excitons in order to activate the TTF mechanism from low to high current density ranges. By adjusting the TTF-AM, the TTF ratio of the anthracene-based emitter was increased from 5.5/20.1% to 13.4/25.5% (low/high current density), thereby resulting in more than doubled external quantum efficiency at low current density.

Abstract Chemical asphyxiation at petrochemical factories can provoke the unconsciousness or death of factory workers through suffocation. Some chemicals vaporize and mix with air without showing any warning properties that raise the risk of oxygen deficiency. In light of this, Industry 5.0 focuses more on human‐centricity than technology‐driven implementations to ensure secured and work‐friendly environments in industries. Recently, research on factory safety management dependent on the Internet of things (IoT) sensors have been executed unwaveringly. In this work, the ultra‐wideband (UWB) sensor is adopted to recognize the motion and breathing pattern of workers in smart factory scenarios. After capturing the data from the UWB sensor in real‐time, the proposed dataset is further inspected by the deep learning (DL) and traditional machine learning (ML) approaches. Twofold detection schemes are considered where the movement and vital patterns are distinguished first by the stacked ensemble (SE) and the long short‐term memory (LSTM) frameworks. The Bayesian optimized ensemble learning (EL) and bidirectional (Bi‐LSTM) models are further occupied to analyze abnormalities in the breathing rate of a worker in the smart shop floors. The investigated outcome shows that the DL frameworks (LSTM and Bi‐LSTM) outperformed the others by acquiring 99.90% and 99.94% accuracy in 147 s and 293 s, respectively. The devised perception indicates prominent attainment to the smart factory shop floor, Internet of medical things (IoMT), the smart city paradigm, and e‐health appliances.

Procedural sedation has become increasingly common in endoscopy. Sedatives and analgesics induce anxiolysis and amnesia. In addition, an appropriate level of sedation is necessary for safe procedures including therapeutic endoscopy. Midazolam and propofol are the most commonly used drugs in sedative endoscopy. In recent years, the need to ascertain the safety and effectiveness of sedation has increased in practice. Therefore, new sedatives and analgesic drugs for optimal sedative endoscopy, have recently emerged. This article reviews the characteristics of sedatives and analgesics, and describes their clinical use in gastrointestinal endoscopy.