A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extraction and grouping, (3) false positives reduction for the non-vessel tree group, and (4) classification for the vessel tree group. Lung segmentation is performed first. Then, 3D labeling technology is used to divide nodule candidates into two groups. For the non-vessel tree group, nodule candidates are classified as true nodules at the false positive reduction stage if the candidates survive the rule-based classifier and are not screened out by the dot filter. For the vessel tree group, nodule candidates are extracted using dot filter. Next, RSFS feature selection is used to select the most discriminating features for classification. Finally, WSVM with an undersampling approach is adopted to discriminate true nodules from vessel bifurcations in vessel tree group. The proposed method was evaluated on 154 thin-slice scans with 204 nodules in the LIDC database. The performance of the proposed CAD scheme yielded a high sensitivity (87.81%) while maintaining a low false rate (1.057 FPs/scan). The experimental results indicate the performance of our method may be better than the existing methods.

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost.Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety.Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (IC_PNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the IC_PNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden’s index.Results: Of the statistical approaches implemented, the IC_PNM showed the best performance (AUC, 0.8291; Youden’s index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively.Conclusion: The proposed IC_PNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.

The blast furnace permeability index is one of the crucial technical indicators in the ironmaking process of a blast furnace. Given that the conventional models are not entirely suitable for accommodating the intricate characteristics of blast furnace production, this paper explores a comprehensive approach that involves data mining, the sparrow search algorithm (SSA), convolutional neural networks (CNNs), and gated recurrent unit networks (GRUs) for predicting the blast furnace permeability index. Initially, to address the multi-noise nature of blast furnaces, outliers are eliminated, and a Kalman filter is devised for denoising purposes. Subsequently, in consideration of the nonlinear and substantial time-delay features of blast furnaces, the maximal information coefficient (MIC) method is employed for time-delay alignment, followed by the selection of model input variables based on process analysis and relevance. Subsequent to this, the SSA-CNN-GRU model is established. Within the modeling process, a one-dimensional convolutional neural network is utilized to extract distinct process variable features, thus further resolving the interdependence among blast furnace data. Ultimately, the effectiveness, accuracy, and advancement of the proposed method are validated using real production data.

Sulphur dioxide, as one of the most common air pollutant gases, brings considerable numbers of hazards on human health and environment. For the purpose of reducing the detrimental effect it brings, it is of urgent necessity to control emissions of flue gas in power plants, since a substantial proportion of sulphur dioxide in the atmosphere stems from flue gas generated in the whole process of electricity generation. However, the complexity and nondeterminism of the environment increase the occurrences of anomalies in practical flue gas desulphurization system. Anomalies in industrial desulphurization system would induce severe consequences and pose challenges for high-performance control with classical control strategies. In this article, based on process data sampled from 1000 MW unit flue gas desulphurization system in a coal-fired power plant, a multimodel control strategy with multilayer parallel dynamic neural network (MPDNN) is utilized to address the control problem in the context of different anomalies. In addition, simulation results indicate the applicability and effectiveness of the proposed control method by comparing with different cases.

To address the difficulty of controlling the dynamic stability of the system after time-varying delay of the plate thickness detection at the final stand of the hot strip finishing mill. Firstly, a T-S fuzzy model based on rolling force and thickness state is established for the final stand of the hot strip finishing mill, and the maximum time delay for plate thickness detection is divided into several time delay intervals to establish a linear constant state space model of the time delay subintervals. Secondly, a distributed state feedback controller satisfying sufficient conditions for stability is solved by using the parallel distribution compensation (PDC) principle combined with Lyapunov stability theory and linear matrix inequality (LMI) methods. Simulation comparisons with current M-AGC and Smith prediction M-AGC system (Smith-AGC) show that our method can effectively reduce overshoot and oscillations in the dynamic response of the exit plate thickness when the operating conditions change and the system time delay parameters are mismatched, so that the system has better stability and dynamic performance.

Increasing coal and reducing coke is an important technical means to achieve energy conservation and emission reduction. The blast furnace ironmaking process is characterized by large time lag and nonlinearity, and also a lack of information in the manual collaborative operation mode for increasing coal and reducing coke, which leads to the problem that the furnace temperature is difficult to stabilize. The synergistic optimization with a Takagi-Sugeno fuzzy multi-model for a state feedback is proposed. First, the process of increasing coal and reducing coke from the perspective of control is analytically described, converting the stable control problem of molten iron temperature into the tuyere raceway temperature, the state feedback of tuyere raceway temperature and hearth temperature is introduced, based on the establishment of the Takagi-Sugeno fuzzy model of the pulverized coal combustion system in the tuyere raceway, using the principle of parallel distributed compensation algorithm with the help of solving linear matrix inequality, the state feedback controller that satisfies the Lyapunov stability is designed to achieve the temperature stability, thereby ensuring the stability of blast furnace operation under all conditions. Then, the upper and lower synergistic control of increasing coal and reducing coke is realized by the principle of heat balance in high temperature zone and the rule of replacement ratio, to ensure the dynamic balance of temperature in the raceway. Finally, by comparing the simulation results, we found that the proposed method could achieve relatively high stability, meanwhile verify the effectiveness of the coordinated control strategy of reducing coke.