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The assembly of the ship block is an extremely important stage of the shipbuilding process. Nevertheless, currently, the manual assembly efficiency is low, the accuracy is poor, and collision is very easy to occur. Therefore, there is an urgent need to conduct technical research on the automatic docking of ship blocks. The core of the automated docking technology is the attitude estimation and the trajectory planning of the posturing equipment. However, current data measurement and point set matching methods lead to large attitude-estimation errors, and it is difficult to meet the accuracy requirements of the assembly. Moreover, the current ship block trajectory planning methods pay more attention to single metrics, for example, time or energy consumption, while omitting the shock degree. In response to the above, this study first proposes a high-precision matching method for measuring point sets, in order to estimate the exact attitude of the ship block. Subsequently, trajectory translation for the block is performed using the seventh-degree polynomial. On this basis, a nonlinear weighted improved particle swarm optimization (IPSO) method is proposed to optimize the time, energy consumption and shock degree in the ship block trajectory planning process. Finally, the accuracy of the matching optimization is validated by simulation analysis and it is concluded that the seventh-degree polynomial leads to less shock than other polynomials. Furthermore, the shock force does not change abruptly even when the ship block is poised in steps. Through IPSO, the energy consumption and shock degree performance indices are optimized by 37.07% and 50.06%, respectively, in the ship block translation process. [ABSTRACT FROM AUTHOR]

Background: Nonpharmaceutical interventions (NPIs) targeted at SARS‐CoV‐2 have remarkably affected the circulation of other respiratory pathogens, including respiratory syncytial virus (RSV). This study aimed to assess the changes in epidemiological and clinical characteristics of RSV infections in hospitalized children before and during the pandemic in Suzhou, China. Methods: We prospectively enrolled children aged < 18 years who were hospitalized in Soochow University Affiliated Children's Hospital with acute lower respiratory infection (ALRIs) from January 2018 to July 2022. Changes in epidemiological and clinical characteristics of RSV infections were analyzed. Results: Compared with the same period in 2018–2019, the difference in the overall positive rate of RSV was not statistically significant in 2020, while it increased significantly in 2021 (11.8% [662/5621] vs. 20.8% [356/1711], p < 0.001) and 2022 (9.0% [308/3406] vs. 18.9% [129/684], p < 0.001). Specifically, the positive rates declined considerably from October to December 2020 but sharply increased during the summer of 2021. Compared to prepandemic period, RSV infections were more frequently observed in older children during the pandemic. RSV‐positive children exhibited milder clinical characteristics during the COVID‐19 pandemic, including decreased proportion of patients with hospital stay ≥ 11 days (10.3% vs. 6.7%, p < 0.05), less requirement for oxygen therapy (13.7% vs. 6.9%, p < 0.001), and fewer cases of polypnea (12.2% vs. 9.7%, p < 0.05) and wheeze (50.1% vs. 42.9%, p < 0.001). Conclusions: The implementation of multilayered NPIs targeted at COVID‐19 has affected the activity of RSV. Ongoing monitoring of RSV is warranted as the changing RSV epidemiology can provide valuable insights for future healthcare system planning. [ABSTRACT FROM AUTHOR]

Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction. [ABSTRACT FROM AUTHOR]

The approval of nirsevimab brings light to reducing the heavy disease burden caused by respiratory syncytial virus (RSV). Considering the seasonality of RSV, the timing of administrating monoclonal antibody (mAb) is critical to maximize health utility. This study aimed to model and seek the optimal seasonal mAb administration strategy for preventing RSV-associated hospitalization. Age-season specific hospitalization rates for RSV-associated acute lower respiratory infection (RSV-ALRI) were estimated from a hospital-based birth cohort. Using these rates, we simulated and evaluated the effect of diverse mAb administration strategies on preventing RSV-ALRI hospitalization. Optimal strategies were selected based on their effectiveness and relative cost-effectiveness. Compared with the year-round strategy of administration mAb at birth for all children, 291 out of the 854 candidate strategies, featuring diverse administration timing and age thresholds, demonstrated a greater number of averted RSV-ALRI hospitalizations and a lower number needed to treat (NNT). The NNT represents the number of mAb doses needed to prevent one case of RSV-ALRI hospitalization. Among the 291 strategies, administration mAb to children born in July-January or August-January at birth and administrating to the remaining <12 months old children in September, exhibited the highest increase in averted RSV-ALRI hospitalizations than the year-round strategy, with a magnitude of 23 %, while also achieve an 18 % reduction in NNT. Administrating monoclonal antibodies to children born in July to January at birth, and administrating to the remaining <1-year-old children in September or October would be the optimal seasonal mAb administration strategy for children in Suzhou, China. [ABSTRACT FROM AUTHOR]

This paper proposes a short-term load forecasting method that takes into account the correlation of integrated energy load. The method use wavelet packet to decompose the electric cooling and heating load in frequency bands, analyze the cross-correlation of the electric cooling and heating load in each frequency band, and choose different forecasting methods according to the strength of the correlation to reflect the cross-correlation of the load itself; the method use recurrent neural network as a forecasting model to reflect the autocorrelation of the load itself. Compared with putting the electric cooling and heating load into the same recurrent neural network or back propagation neural network for forecasting, the method in this paper considers the autocorrelation of the electric cooling and heating load itself and the cross- correlation of the electric cooling and heating load in different frequency bands. This method reduces the average absolute percentage error of the load forecasting.

The accurate forecast of integrated energy loads, which has important practical significance, is the premise of the design, operation, scheduling and management of integrated energy systems. In order to make full use of the coupling characteristics of electricity, cooling and heating loads which is difficult to deal with by traditional methods, this paper proposes a new forecast model of integrated energy system loads based on the combination of convolutional neural network (CNN) and long short term memory (LSTM). Firstly, the Pearson correlation coefficients among the electricity, cooling and heating load series of the integrated energy system are calculated, and the results show that there is a strong coupling relationship between the loads of an integrated energy system. Then, the CNN-LSTM composite model is constructed, and CNN is used to extract the characteristic quantity which reflects the load coupling characteristics of the integrated energy system. Then, the characteristic quantity is converted into the time series input to LSTM, and the excellent time series processing ability of LSTM is used for load forecasting. The results show that the CNN-LSTM composite model proposed in this paper has higher prediction accuracy than the wavelet neural network model, CNN model and LSTM model.

Detailed information on system operation is recorded by system logs, from which fast and accurate detection of anomalies is conducive to service management and system maintenance. Log anomaly detection methods often only handle a single type of anomaly, and the utilization of log messages could be higher, which makes it challenging to improve the performance of log anomaly detection models. This article presents the LTAnomaly model to accomplish log anomaly detection using semantic information, sequence relationships, and component values to make a vector representation of logs, and we add Transformer with long short-term memory (LSTM) as our final classification model. When sequences are processed sequentially, the model is also influenced by the information from the global information, thus increasing the dependence on feature information. This improves the utilization of log messages with a flexible, simple, and robust model. To evaluate the effectiveness of our method, experiments are performed on the HDFS and BGL datasets, with the F1-measures reaching 0.985 and 0.975, respectively, showing that the proposed method enjoys higher accuracy and a more comprehensive application range than existing models. [ABSTRACT FROM AUTHOR]

We proposed and experimentally demonstrated a low-complexity generation and reception scheme for guard-band orthogonal frequency-division multiplexing (GB-OFDM) in intensity-modulation and direct-detection (IMDD) transmission systems. The image spectra that arise in the 2nd Nyquist zone from a digital-to-analog converter (DAC) operating in radio-frequency mode is exploited to efficiently generate GB-OFDM signal. In the receiver, the analog-to-digital converter (ADC) undersampling technique is applied to recover the baseband OFDM signal. The sampling rate of DAC/ADC is reduced and no electrical mixer and oscillators are required for up/down-conversions. Moreover, the same digital signal processing algorithms as the baseband OFDM can be applied to the proposed GB-OFDM. The proposed GB-OFDM scheme is experimentally investigated in a real-time short-reach IMDD system. The results exhibit that the receiver sensitivity in terms of the received optical power of the proposed real-time 16/64QAM-encoded GB-OFDM system over 50 km standard single-mode fiber can be improved by 7.5/3.5 dB at the bit error rate of 3.8e-3, compared to the baseband OFDM. [ABSTRACT FROM AUTHOR]

Background: There is a limited amount of data in China on the disease burden of respiratory syncytial virus‐ (RSV) associated acute lower respiratory infection (ALRI) among young children. This study aimed to estimate the hospitalization rate of RSV‐associated ALRI (RSV‐ALRI) among children aged 0–59 months in Suzhou, China. Methods: All cases from children hospitalized with ALRI who were aged 0–59 months in Suzhou University Affiliated Children's Hospital during January 2010 to December 2014 were retrospectively identified. Detailed diagnosis and treatment data were collected by reviewing each individual's medical chart. In accordance with the World Health Organization (WHO) influenza disease burden estimation, the hospitalization rate of RSV‐ALRI among children aged 0–59 months in Suzhou, China, was then estimated. Results: Out of the 28,209 ALRI cases, 19,317 (68.5%) were tested for RSV, of which the RSV positive proportion was 21.3% (4107/19,317). The average hospitalization rate of RSV‐ALRI for children aged 0–59 months was 14 (95% confidence interval [CI]:14–14)/1000 children years, and that for children aged 0–5, 6–11, 12–23, and 24–59 months were 70 (95% CI: 67–73), 31 (95% CI: 29–33), 11 (95% CI: 10–12), and 3 (95% CI: 3–3)/1000 children years, respectively. Conclusion: A considerable degree of RSV‐ALRI hospitalization exists among children aged 0–59 months, particularly in those under 1 year of age. Therefore, an effective monoclonal antibody or vaccine is urgently needed to address the substantial hospitalization burden of RSV infection. [ABSTRACT FROM AUTHOR]