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Aiming at the problem of data island in the process of coal mine information construction, this paper proposed the architecture of coal mine collaborative scheduling platform based on multi-source heterogeneous data. Through the research of data standard system and key technologies of multi-source heterogeneous data acquisition, we developed the coal mine collaborative scheduling platform of Ningxia Coal Industry Company, and established multi-system data fusion and sharing pattern. The results show that the application of the platform can effectively achieve the goal of eliminating data islands, improve the information management ability of coal mine operation coordination and command business, and improve the intensive management level of coal mine integrated management and control.

Intelligent rice disease recognition methods based on deep neural networks can predict the degree of disease on the basis of, for example, the number of disease spots on an image, so that preventive measures can be taken. Currently, intelligent recognition methods for rice diseases suffer from the disadvantages of poor versatility and low accuracy. This paper uses eight common image classification networks to classify and identify four rice diseases. ResNet50 was selected as the feature extraction network and an enhanced feature fusion and target adaptive network (EFFTAN), referred to as EFFTAN, is proposed. The EFFTAN was used to detect four rice spot diseases in the rice leaf disease image samples dataset; the mean average precision of the final detection was 95.3%, and effective detection was also achieved for the dense spot features. [ABSTRACT FROM AUTHOR]

Wheat rust is one of the important factors leading to wheat yield decline. The traditional method of artificial identification of wheat rust remains inefficient. With the development of unmanned technology, unmanned aerial vehicles (UAVs) with advanced deep vision models can be used to monitor wheat growth, hence enabling timely disease detection and deployment of corresponding treatment measures. However, due to the limitation of embedded system hardware, it is a challenge to deploy a high‐accuracy and lightweight wheat rust detection model in an embedded system. In this paper, a training method based on transfer learning and sharpness‐aware minimization (SAM) is proposed to improve the accuracy of lightweight models. Specifically, the initial model is pretrained on an ImageNet dataset, and then the classifier of the model is fine‐tuned on the wheat rust training set; to prevent the risk of overfitting, the SAM method is applied to adjust the global parameters of the model. The experimental results on the Yellow‐Rust‐19 dataset show that the training method can effectively improve the accuracy of the wheat rust detection model, which is 3.56% higher than the existing methods. In addition, the parameter scale and computing resource requirements of four lightweight models are compared. The results indicate that MobileNetV3‐Small can achieve satisfactory accuracy with low requirements for storage and computing resource, and a detection frame rate of 101.36 frames per second in the Raspberry Pi, which is suitable for the unmanned detection of wheat rust. [ABSTRACT FROM AUTHOR]

The healthy operation conditions of wind turbines (WTs) with insufficient data need attention, but they face the problems of data imbalance and lack of labels. Aiming at the condition monitoring (CM) of these WTs, a CM method based on transfer learning and one-class classification (OCC) is proposed. This method uses the source WT data to help learn information about monitoring data from the target WT. First, the data of the target and source WTs are preprocessed to construct a training set. Second, to improve the TrAdaBoost algorithm for the OCC task, a novel weighting method based on an autoencoder (AE)—an unsupervised one-class classifier—is designed to assign weights for samples in the source and target domain dynamically and then the CM model of the target WT is established. Finally, its effectiveness is verified by using the monitoring data to detect the generator fault of the target WT. The comparison with the nontransfer method shows that the proposed method can significantly reduce the number of false alarms and can issue early warnings. [ABSTRACT FROM AUTHOR]

Dust deposition on the surface of photovoltaic (PV) modules is a nonnegligible factor that reduces a PV system’s efficiency and reliability. Cleaning can remove dust, and the effect of cleaning on PV performance resembles that of maintenance. In this article, we propose a hybrid cleaning scheduling policy with periodic planning and dynamic adjustment for refining the operations and maintenance of PV systems. Specifically, the periodic planning stage aims for medium-term scheduling while the dynamic adjustment stage is tailed for short-term fine-tuning. In the former stage, we show that when the number of cleaning actions is fixed, a periodic cleaning strategy is optimal. Moreover, we derive the optimality condition under which the optimal cleaning interval can be determined. In the latter stage, based on the determined cleaning interval, we dynamically adjust the cleaning schedule with the forecast of meteorological parameters, PV power generation, and dust deposition in order to further minimize economic losses. In addition, we take the forecasting uncertainty into account and propose a new custom parameter called risk-taking tendency (RTT), which is able to quantify the risk preference of decision makers and analyze its influence on the scheduling policy. A case study is provided to illustrate the proposed strategy. [ABSTRACT FROM AUTHOR]

Bearing is a key component in rotary machines. Their failures may cause the abrupt shutdown of these machines, which would result in substantial economic losses. Therefore, the prediction of the remaining useful life (RUL) of bearings is regarded as one of the critical approaches to avoid failure of bearings and their systems. In this article, an ensemble data-driven approach is proposed to predict the RUL of bearings. It uses feature extraction, an attention mechanism, and uncertainty analysis. First, the features embedded in the bearings’ vibration signals are extracted. Second, a stacked gated recurrent unit (GRU) is constructed to predict the bearing RUL. A novel attention mechanism based on dynamic time warping (DTW) is developed to improve the performance of information extraction, and a Bayesian approach is employed to analyze the prediction uncertainty. Finally, the proposed approach is validated using two benchmark-bearing data sets. The results show that the proposed approach can predict the bearing RUL effectively, and the prediction uncertainty can also be evaluated. [ABSTRACT FROM AUTHOR]

This paper focuses on the safety of railway vehicles. A new semi-active control strategy is proposed based on the skyhook control theory. In view of the main railway vehicle safety performance indicators, the new control strategy aims at reducing the derailment coefficient of railway vehicles by restraining the lateral vibrations of the bogie and the wheelset. Furthermore, to evaluate the improvement of the safety performance brought about by the new control strategy, a complete railway vehicle model is established using the ADAMS/Rail software package. In further co-simulations, five conventional control methods are compared with the proposed approach under the same conditions. Co-simulation results indicate that the new control strategy is effective in improving the safety performance of railway vehicles. [ABSTRACT FROM AUTHOR]

Nonlinear degradation trajectories are encountered frequently, and not all of them evolve homogeneously in practical systems. To take nonlinearity, heterogeneity, and the entire historical degradation data into account, we propose a nonlinear heterogeneous Wiener process model with an adaptive drift to characterize degradation trajectories. A state-space based method is employed to delineate our model. Due to the introduction of the adaptive drift, it is difficult to directly apply Kalman filter methods to update the distribution of the estimated degradation drift. To address this issue, we develop an online filtering algorithm based on Bayes’ theorem. The expectation-maximization (EM) algorithm, as well as a novel Bayes’-theorem-based smoother, are adopted to estimate the unknown parameters in our model. Moreover, the distribution of the predicted remaining useful life (RUL) incorporating the complete distribution of the estimated degradation drift is achieved analytically. Finally, a simulation, and a case study are provided to validate the proposed approach. [ABSTRACT FROM AUTHOR]

To solve the problem of NATted (Network Address Translator, NAT) hosts counting in network security, a new method based on Hough transform is proposed. By exploiting the linear relation between TCP timestamp and received time in the packets, the problem is transformed to counting lines in the coordinate graph. Hough transform is introduced to detect lines in the graph. To get more precise result, zooming in the graph iteratively is used in the algorithm. Compared with existing algorithms, the proposed method can solve the problem of choosing initial clusters, and improve the detection accuracy with intersected or short distance timestamp sequence. Experiment results show that the algorithm based on Hough transform can detect the number of NATted hosts precisely which outperforms existing methods. [ABSTRACT FROM AUTHOR]

Inter- and intra-batch variability of the quality attributes contribute to the uncertainty for demonstrating equivalent microstructure of post-approval changes and generic/hybrids of semisolid topical products. Selecting a representative sample size to describe accurately the in vitro properties of semisolids and to reach enough statistical power to demonstrate similarity between two semisolid topical products is currently challenging. The objective of this work is to establish the number of batches and units per batch to be compared based on different inter-batch and intra-batch variability to demonstrate equivalence in the physical characteristics of the products that ensure a similar microstructure of the semisolid. This investigation shows that the minimum number of batches to be compared of each product is 3 and the minimum number of units per batch could be 6 in the case of low intra- and inter-batch variability. If the products are not identical, i.e., 2.5–5% differences that are expected due to differences in the manufacturing process or the suppliers of excipients, 12 units and 6 batches are needed. If intra- or inter-batch variability is larger than 10%, the number of batches and/or the number of units needs to be increased. As the interplay between inter- and intra-batch variability is complex, the sample size required for each combination of inter- and intra-batch variability and expected difference between products can be obtained in the attached tables. [ABSTRACT FROM AUTHOR]

The coal pulverizing system is an important auxiliary system in thermal power generation systems. The working condition of a coal pulverizing system may directly affect the safety and economy of power generation. Prognostics and health management is an effective approach to ensure the reliability of coal pulverizing systems. As the coal pulverizing system is a typical dynamic and nonlinear high-dimensional system, it is difficult to construct accurate mathematical models used for anomaly detection. In this paper, a novel data-driven integrated framework for anomaly detection of the coal pulverizing system is proposed. A neural network model based on gated recurrent unit (GRU) networks, a type of recurrent neural network (RNN), is constructed to describe the temporal characteristics of high-dimensional data and predict the system condition value. Then, aiming at the prediction error, a novel unsupervised clustering algorithm for anomaly detection is proposed. The proposed framework is validated by a real case study from an industrial coal pulverizing system. The results show that the proposed framework can detect the anomaly successfully. [ABSTRACT FROM AUTHOR]

Due to its decade-long progression, colorectal cancer (CRC) is most suitable for population screening to achieve a significant reduction in its incidence and mortality. DNA methylation has emerged as a potential marker for the early detection of CRC. However, the current mainstream methylation detection method represented by bisulfite conversion has issues such as tedious operation, DNA damage, and unsatisfactory sensitivity. Herein, a new high-performance CRC screening tool based on the promising specific terminal-mediated polymerase chain reaction (STEM-PCR) strategy is developed. CRC-related methylation-specific candidate CpG sites are first prescreened through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using self-developed bioinformatics. Next, 9 homebrew colorectal cancer DNA methylated STEM‒PCR assays (ColoC-mSTEM) with high sensitivity (0.1%) and high specificity are established to identify candidate sites. The clinical diagnostic performance of these selected methylation sites is confirmed and validated by a case-control study. The optimized diagnostic model has an overall sensitivity of 94.8% and a specificity of 95.0% for detecting early-stage CRC. Taken together, ColoC-mSTEM, based on a single methylation-specific site, is a promising diagnostic approach for the early detection of CRC which is perfectly suitable for the screening needs of CRC in primary healthcare institutions., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

Background: Depression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population., Materials and Methods: We used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set ( n = 880) and an external validation set ( n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively., Results: The predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722-0.803) and 0.679 (95% CI = 0.572-0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp_depression/)., Conclusion: Reductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tan, Xu, He, Zhang, Xiang, Xu, Xu, Gong, Tan and Tan.)