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Abstract Purpose To investigate the value of CT urography (CTU) indicators in the quantitative differential diagnosis of bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB). Material and methods The clinical and preoperative CTU imaging data of continuous 103 patients with histologically confirmed BUC or IPB were retrospectively analyzed. The imaging data included 6 qualitative indicators and 7 quantitative measures. The recorded clinical information and imaging features were subjected to univariate and multivariate logistic regression analysis to find independent risk factors for BUC, and a combined multi-indicator prediction model was constructed, and the prediction model was visualized using nomogram. ROC curve analysis was used to calculate and compare the predictive efficacy of independent risk factors and nomogram. Results Junction smoothness, maximum longitudinal diameter, tumor-wall interface and arterial reinforcement rate were independent risk factors for distinguishing BUC from IPB. The AUC of the combined model was 0.934 (sensitivity = 0.808, specificity = 0.920, accuracy = 0.835), and its diagnostic efficiency was higher than that of junction smoothness (AUC=0.667, sensitivity = 0.654, specificity = 0.680, accuracy = 0.660), maximum longitudinal diameter (AUC=0.757, sensitivity = 0.833, specificity = 0.604, accuracy = 0.786), tumor-wall interface (AUC=0.888, sensitivity = 0.755, specificity = 0.808, accuracy = 0.816) and Arterial reinforcement rate (AUC=0.786, sensitivity = 0.936, specificity = 0.640, accuracy = 0.864). Conclusion Above qualitative and quantitative indicators based on CTU and the combination of them may be helpful to the differential diagnosis of BUC and IPB, thus better assisting in clinical decision-making. Key points 1. Bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB) exhibit similar clinical symptoms and imaging presentations. 2. The diagnostic value of CT urography (CTU) in distinguishing between BUC and IPB has not been documented. 3. BUC and IPB differ in lesion size, growth pattern and blood supply. 4. The diagnostic efficiency is optimized by integrating multiple independent risk factors into the prediction model.

This paper presents a neural network-based feedback control method for enhancing the control precision and tracking speed of a permanent magnet brushless motor under command control. The proposed method involves real-time adjustment of the PID controller parameters using electromechanical output signals, enabling adaptive feedback control based on motor output. Experimental results demonstrate that this approach enhances real-time performance and dynamic load response capability, resulting in a current waveform with excellent tracking and low distortion. Overall, this method effectively improves and enhances control effectiveness. Furthermore, the developed control method is successfully applied to the development of tangible products.

Abstract Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models.

A scheme for improving entanglement is proposed by repeatedly performing number-conserving generalized superposition of products (GSP) operation, i.e., saa†+ta†amwith s2+t2=1,on each modes of even entangled coherent state (EECS). Both Einstein–Podolsky–Rosen (EPR) correlation and concurrence are used as the measures for describing entanglement. It is found that both single-mode and two-mode-symmetric GSP operations can be used for the entanglement improvement. In particular, for the optimal EPR correlation, the GSP-EECS with two-mode-symmetric first-order operations presents a better performance for the entanglement improvement than the cases of high-order GSP operations. Compared to several special non-Gaussian operations, including photon subtraction-then-addition (PSTA) (s=0)and photon-addition-then-subtraction (PATS) (s=1), it is shown that the entanglement of GSP-EECS is always superior to those of both PSTA-EECS and PATS-EECS at a small amplitude level. When the GSP-EECS is used as the entangled resource for teleportation, our optimal teleportation fidelity presents the best performance.

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model.

Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness.

We propose and simulate a new kind of compact polarizing beam splitter (PBS) based on a photonic crystal ring resonator (PCRR) with complete photonic bandgaps. The two polarized states are separated far enough by resonant and nonresonant coupling between the waveguide modes and the microring modes. Some defect holes are utilized to control the beam propagation. The simulated results obtained by the finite-difference time-domain method show that high transmission (over 95%) is obtained and the polarization separation is realized with a length as short as 3.1 [micro]m. The design of the proposed PBS can be flexible, thanks to the advantages of PCRRs. [c] 2010 Optical Society of America OCIS codes: 230.4555, 130.3120, 230.5440, 230.1360.

Background: Since December 2019, there has been a global outbreak of COVID-19. As of the end of July 2020, more than 600,000 deaths had been reported globally. The purpose of this paper is to further explore the application of non-invasive ventilation in severe COVID-19 patients. Methods: A retrospective study was conducted to included 57 confirmed COVID-19 patients, among which 36 cases were severe. According to different oxygen inhalation methods, they were divided into non-invasive ventilator assisted ventilation group with 21 cases (group A) and 15 cases of nasal catheter oxygen inhalation group (group B). The data of respiration (RR), heart rate (HR), partial arterial pressure of oxygen (PaO2), partial arterial pressure of carbon dioxide (PaCO2), and oxygenation index (OI) before the treatment of noninvasive ventilator assisted ventilation or nasal catheter oxygen treatment at 24, 48, and 72 hours of treatment of the 2 groups were collected and analyzed to determine whether the above indicators were statistically different in each time period. Results: After 24 hours of treatment with noninvasive ventilator assisted ventilation in group A, RR gradually decreased, PaO2 and OI were significantly higher than before treatment, while after 24 hours of treatment, PaO2, RR, HR and other indexes in group B showed no significant improvement, and OI increased gradually after 48 hours of treatment, with statistically significant difference compared with that before treatment. Conclusions: Early adoption of non-invasive ventilation can effectively improve the hypoxic state of patients with severe COVID-19. The combination of underlying diseases will not prolong the use of non-invasive ventilation. [ABSTRACT FROM AUTHOR]

Objective • The objective of this research study was to compare the safety and efficacy of bronchial artery embolization (BAE) using Embospheres alone versus Embospheres combined with gelfoam particles in patients with massive hemoptysis. Methods • A total of 127 patients with tuberculous massive hemoptysis who were scheduled to undergo BAE were recruited and divided into two groups: Embosphere group (E group, n = 57) and Embosphere combined with gelfoam particles group (E + G group, n = 70). Technical and clinical success were assessed after BAE surgery, and mortality, untoward reactions, and risk factors for clinical failure were recorded during follow-up. Results • The technical success rate was 92.99% in the E group and 97.14% in the E + G group (P = .272), with similar 1-year mortality rates of 1.76% and 2.86%, respectively (P = .684). However, the E group exhibited a lower clinical success rate compared to the E + G group (85.96% vs. 97.14%), and this difference was statistically significant (P = .020). The untoward reactions showed no statistically significant difference (all P > .05). Univariate analysis revealed that four factors were statistically significant: age (P = .028), presence of pulmonary cavity (P=.001), diabetes (P=.005), and a single use of Embosphere embolization (P = .020). Multivariate regression analysis demonstrated that embolization with Embosphere alone was a risk factor for clinical treatment failure (P=.025). Conclusion • The combination of Embosphere with gelfoam particles can significantly improve the hemostatic effect of BAE without increasing the incidence of adverse reactions. [ABSTRACT FROM AUTHOR]