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Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Digital low-dropout (DLDO) is widely used for power management in the system-on-chip (SoC) because of its low-voltage operation and process scalability. However, conventional DLDOs suffer from the trade-off between transient response and power consumption of the DLDO and the clock generator. This paper proposes an event-driven self-clocked DLDO regulator. The proposed low quiescent current (IQ) event-driven adaptive frequency clock generator (EACG) adapts its frequency in different load conditions without a current sensor or complex compensation circuit for stable operation in the entire load range. The proposed DLDO does not need any external clocking signal and can maintain low output ripple and low power consumption in the steady-state. The clock-less transient detector (CLTD), consisting of two clock-independent transient detection paths, uses power more efficiently and improves the transient response significantly without sacrificing the power consumption. This work was fabricated in a 40 nm CMOS process with an 0.3 nF on-chip capacitor. The measurement results show that with the step load current between 1 mA and 60 mA, the proposed DLDO achieves a transient recovery time of 220 ns. The total IQ of the proposed DLDO is only 26 μA in steady-state, and it achieves stable operation in the entire load range.

Abstract Background Haworthia are desert succulents belonging to the Asphodelaceae family. Haworthia species are cultivated commercially as ornamentals and some rare species are quite valuable at retail market but growth slowly and difficult to propagation. However, an efficient micropropagation protocol was remained insufficient. Results The organogenic cultures obtained from inflorescence explants were cultured on Murashige and Skoog (MS) medium supplemented with various combinations of 6-benzylaminopurine (BA) and α-naphthalene acetic acid (NAA) under a light intensity of 10 μmol m−2 s−1 or 45 μmol m−2 s−1. The highest callus proliferation index (93.15%) with 1.0 mg L−1 BA + 0.1 mg L−1 NAA under a light intensity of 10 μmol m−2 s−1. The best shoot proliferation rates were on media with either 1 mg L−1 BA + 0–0.4 mg L−1 NAA (65.57–81.01%) under a light intensity of 45 μmol m−2 s−1. The highest root length (15.57 mm) and the highest rooting frequency (17 roots per shoot) were obtained when adventitious shoots were inoculated on MS medium with 0.4 mg L−1 NAA + 0.4 mg L−1 IBA. The survival rate of the transplanted plantlets was about 100%. The efficient micropropagation protocol proliferated Haworthia regenerate plants from inflorescence within 11 weeks. Conclusions The present study determined the best combination of light intensity and plant growth regulators (PGRs) for improved organogenesis of Haworthia during propagation by tissue culture. This optimized protocol showed light intensity is an important factor for efficient callus or shoot regeneration. These results indicate that it will be useful to optimize the light conditions for future commercial cultivation, germplasm conservation, genetic engineering and molecular biology research of this ornamental plant.

This paper presents a non-orthogonal multiplexing scheme using the concept of rateless coding for a downlink broadcast scheme with two users. The proposed approach does not require the inclusion of successive interference cancellation (SIC) techniques for signal detection when compared to the power-domain non-orthogonal multiple access (PD-NOMA) schemes. In various user pair scenarios, it is shown that the proposed scheme not only enhances the user transmission rate but also reduces the overhead required for reliable transmissions when compared to PD-NOMA-based schemes. To further enhance the error performance when considering both unconstrained and constrained inputs, a code design algorithm is proposed based on the transmission rate analysis. It is shown that the designed rateless multiplexing schemes achieve error performances that accurately approach the predicted performance limits.

Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical challenges. In this research, we analyzed electricity consumption from the perspective of load profiling, which charts variations in electrical load during a specified period in order to track energy consumption of an annealing furnace in a co-operating steel forging plant. We made a preliminary proposal to use a symbolic time-series data mining framework for electricity consumption analysis. First, we adopted a piecewise aggregate approximation (PAA) approach to perform dimension reduction. Then, we refined the distance measure of the symbolic aggregate approximation (SAX) algorithm. SAX is a symbolic representation of time-series for dimensionality reduction and indexing with a lower-bounding distance measure. Our experimental results showed that the dimension reduction method known as PAA can better detect the state of the annealing furnace compared to the fixed feature point (FFP) method. In addition, the refined lower-bounding distance measure proved to be better than the traditional measure for calculating the similarity between energy load profiles. The results can help the plant conduct further normal and abnormal electricity pattern detection.