ImportanceWhether selective decontamination of the digestive tract (SDD) reduces mortality in critically ill patients remains uncertain.ObjectiveTo determine whether SDD reduces in-hospital mortality in critically ill adults.Design, Setting, and ParticipantsA cluster, crossover, randomized clinical trial that recruited 5982 mechanically ventilated adults from 19 intensive care units (ICUs) in Australia between April 2018 and May 2021 (final follow-up, August 2021). A contemporaneous ecological assessment recruited 8599 patients from participating ICUs between May 2017 and August 2021.InterventionsICUs were randomly assigned to adopt or not adopt a SDD strategy for 2 alternating 12-month periods, separated by a 3-month interperiod gap. Patients in the SDD group (n = 2791) received a 6-hourly application of an oral paste and administration of a gastric suspension containing colistin, tobramycin, and nystatin for the duration of mechanical ventilation, plus a 4-day course of an intravenous antibiotic with a suitable antimicrobial spectrum. Patients in the control group (n = 3191) received standard care.Main Outcomes and MeasuresThe primary outcome was in-hospital mortality within 90 days. There were 8 secondary outcomes, including the proportion of patients with new positive blood cultures, antibiotic-resistant organisms (AROs), and Clostridioides difficile infections. For the ecological assessment, a noninferiority margin of 2% was prespecified for 3 outcomes including new cultures of AROs.ResultsOf 5982 patients (mean age, 58.3 years; 36.8% women) enrolled from 19 ICUs, all patients completed the trial. There were 753/2791 (27.0%) and 928/3191 (29.1%) in-hospital deaths in the SDD and standard care groups, respectively (mean difference, −1.7% [95% CI, −4.8% to 1.3%]; odds ratio, 0.91 [95% CI, 0.82-1.02]; P = .12). Of 8 prespecified secondary outcomes, 6 showed no significant differences. In the SDD vs standard care groups, 23.1% vs 34.6% had new ARO cultures (absolute difference, −11.0%; 95% CI, −14.7% to −7.3%), 5.6% vs 8.1% had new positive blood cultures (absolute difference, −1.95%; 95% CI, −3.5% to −0.4%), and 0.5% vs 0.9% had new C difficile infections (absolute difference, −0.24%; 95% CI, −0.6% to 0.1%). In 8599 patients enrolled in the ecological assessment, use of SDD was not shown to be noninferior with regard to the change in the proportion of patients who developed new AROs (−3.3% vs −1.59%; mean difference, −1.71% [1-sided 97.5% CI, −∞ to 4.31%] and 0.88% vs 0.55%; mean difference, −0.32% [1-sided 97.5% CI, −∞ to 5.47%]) in the first and second periods, respectively.Conclusions and RelevanceAmong critically ill patients receiving mechanical ventilation, SDD, compared with standard care without SDD, did not significantly reduce in-hospital mortality. However, the confidence interval around the effect estimate includes a clinically important benefit.Trial RegistrationClinicalTrials.gov Identifier: NCT02389036

Emotion classification is one of the state-of-the-art topics in biomedical signal research, and yet a significant portion remains unknown. This paper offers a novel approach with a combined classifier to recognise human emotion states based on electroencephalogram (EEG) signal. The objective is to achieve high accuracy using the combined classifier designed, which categorises the extracted features calculated from time domain features and Discrete Wavelet Transform (DWT). Two innovative designs are involved in this project: a novel variable is established as a new feature and a combined SVM and HMM classifier is developed. The result shows that the joined features raise the accuracy by 5% on valence axis and 1.5% on arousal axis. The combined classifier can improve the accuracy by 3% comparing with SVM classifier. One of the important applications for high accuracy emotion classification system is offering a powerful tool for psychologists to diagnose emotion related mental diseases and the system developed in this project has the potential to serve such purpose.

Due to the high dimensional, non-stationary and nonlinear properties of electroencephalogram (EEG), a significant portion of research on EEG analysis remains unknown. In this paper, a novel approach to EEG-based human emotion study is presented using Big Data methods with a hybrid classifier. An EEG dataset is firstly compressed using Compressed Sensing (CS), then, wavelet transform features are extracted, and a hybrid Support Vector Machine (SVM) and Fuzzy Cognitive Map (FCM) classifier is designed. The compressed data is only one-fourth of the original size, and the hybrid classifier has the average accuracy by 73.32%. Comparing to a single SVM classifier, the average accuracy is improved by 3.23%. These outcomes show that psychological signal can be compressed without the sparsity identity. The stable and high accuracy classification system demonstrates that EEG signal can detect human emotion, and the findings further prove the existence of the interrelationship between various regions of the brain.

The distribution static synchronous compensator (D-STATCOM) has the characteristics of non-linearity, multivariable and strong coupling. Based on the analysis of the D-STATCOM mathematical model, in order to improve the performance of the linear active disturbance rejection controller (LADRC), solve the coupling problem between the d-axis and q-axis current and improve the dynamic tracking response speed and anti-interference ability. A controller with LADRC that compensates the error of the total disturbance is proposed, and the stability of the improved first-order LADRC is proved by the Lyapunov stability theory. Then the output of the full interference channel is corrected to improve the antiinterference ability of the system and the interference observation ability of the linear extended state observer (LESO) to high-frequency noise. Through the analysis of the Bode diagram in the frequency domain, compared with the traditional LADRC, the improved LADRC proposed in this paper has better anti-interference performance. Finally, the improved first-order LADRC is used to replace the traditional D-STATCOM control strategy for current inner loop control, which effectively reduces the disturbance observation error of LESO. The experimental results show that the improved LADRC control performance is better than the proportional integral (PI) controller, and it has better tracking performance and antiinterference performance.

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