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Land subsidence induced by coal mining (MLS) has posed a huge threat to the ecological environment, buildings, roads, and other infrastructure safety in mining areas. However, the prediction and evaluation of MLS is relatively complex, and the reliability of the prediction results is closely related to factors such as the professional knowledge and engineering experience of researchers. This paper aims to combine intelligent optimization algorithms: ant lion optimizer (ALO), bald eagle search (BES), bird swarm algorithm (BSA), harris hawks optimization (HHO), and sparrow search algorithm (SSA), with machine learning model of gradient boosting with categorical features support algorithm (CatBoost) to predict MLS. To achieve this goal, five hybrid models based CatBoost were developed and the prediction accuracy and reliability of the models were compared and analyzed. The prediction performance of the hybrid models has been significantly improved on the basis of a single model, of which the SSA-CatBoost model has the most obvious improvement (from R 2  = 0.927 to 0.965, RMSE = 0.541 to 0.377, MAE = 0.386 to 0.297, VAF = 92.720 to 95.837). The importance and predictive contribution of all input features to predictive labels were studied with the Shapley method. The research results indicate that hybrid model technology is a reliable MLS prediction method. This study can help mining technicians use machine learning methods to study the degree of MLS damage to the surface environment and provide scientific advanced prediction and evaluation for the protection and management of the ecological environment in mining areas and the formulation of safety production measures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information., Materials and Methods: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning., Results: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations., Conclusions: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Purpose: Ultrasound images show a granular pattern of noise known as speckle that diminishes their quality and results in difficulties in diagnosis. To preserve edges and features, this paper proposes a fractional differentiation-based image operator to reduce speckle in ultrasound., Methods: An image de-noising model based on fractional partial differential equations with balance relation between k (gradient modulus threshold that controls the conduction) and v (the order of fractional differentiation) was constructed by the effective combination of fractional calculus theory and a partial differential equation, and the numerical algorithm of it was achieved using a fractional differential mask operator., Results: The proposed algorithm has better speckle reduction and structure preservation than the three existing methods [P-M model, the speckle reducing anisotropic diffusion (SRAD) technique, and the detail preserving anisotropic diffusion (DPAD) technique]. And it is significantly faster than bilateral filtering (BF) in producing virtually the same experimental results., Conclusions: Ultrasound phantom testing and in vivo imaging show that the proposed method can improve the quality of an ultrasound image in terms of tissue SNR, CNR, and FOM values.

As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study. The number of streamlines (NoS) is fused with normalized fractional anisotropy (FA) for more comprehensive brain bioinformation. The brain region importance contribution matrix and information transfer efficiency value are constructed, respectively, and then by combining these two factors together we can calculate the importance value of each node and locate the hubs. Profiting from both local and global features of the nodes and the multi-information fusion of human brain biosignals, the experiment results show that this method can detect the brain hubs more accurately and reasonably compared with other methods. Furthermore, the proposed location method is used in impaired brain hubs connectivity analysis of schizophrenia patients and the results are in agreement with previous studies.

Colon cancer is one of the leading causes of cancer-related deaths, while the virtual colonoscopy plays an important role in the early detection of colon cancer. Effective colon segmentation is the basis of virtual colonoscopy. Oral contrast agent can reduce the use of laxatives, but it can to bring some difficulties in the colon segmentation. Because of the partial volume effect, the CT value of air-tagging boundary span a wide range, which are not easily eliminated. We present an effective method of colon segmentation in this paper. Firstly, we used adaptive threshold method to extract air area. Then we segmented tagging region and removed air-tagging boundary by segment region growing method based on statistical information and morphological operation. The experiments showed that the agreement between the results by manual and by orithm segmentation reached 93.4%.

Background: In the classification of bruxism patients based on electroencephalogram (EEG), feature extraction is essential. The method of using multi-channel EEG fusing electrocardiogram (ECG) and Electromyography (EMG) signal features has been proved to have good performance in bruxism classification, but the classification performance based on single channel EEG signal is still understudied. We investigate the efficacy of single EEG channel in bruxism classification. Methods: We have extracted time-domain, frequency-domain, and nonlinear features from single EEG channel to classify bruxism. Five common bipolar EEG recordings from 2 bruxism patients and 4 healthy controls during REM sleep were analyzed. The time domain (mean, standard deviation, root mean squared value), frequency domain (absolute, relative and ratios power spectral density (PSD)), and non-linear features (sample entropy) of different EEG frequency bands were analyzed from five EEG channels of each participant. Fine tree algorithm was trained and tested for classifying sleep bruxism with healthy controls using five-fold cross-validation. Results: Our results demonstrate that the C4P4 EEG channel was most effective for classification of sleep bruxism that yielded 95.59% sensitivity, 98.44% specificity, 97.84% accuracy, and 94.20% positive predictive value (PPV). Conclusions: Our results illustrate the feasibility of sleep bruxism classification using single EEG channel and provides an experimental foundation for the development of a future portable automatic sleep bruxism detection system. [ABSTRACT FROM AUTHOR]

The utilization of scale invariant feature transform algorithm in synthetic-aperture radar images (SAR–SIFT) to match image features may lead to principal orientation assignments of descriptors being affected by speckle noise, thereby diminishing accuracy. In this study, we propose using the Fourier histogram of oriented ratio gradient (Fourier HORG) descriptor for robust matching of SAR images. This method is based on the SAR–SIFT algorithm framework. During feature description, the rotation-invariant Fourier HORG descriptor is established by performing Fourier analysis on the ratio gradient in the polar coordinate system, whereby the principal orientation assignment process is avoided and the robustness of SAR image registration improved. A matching experiment was conducted involving four sets of SAR image pairs, and the results demonstrated that our method exhibited higher accuracy and robustness compared to image matching based on the Fourier histogram of oriented gradient (Fourier HOG) descriptor and the SAR–SIFT algorithm, thus confirming the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Summary Accurate estimation of the state of charge (SOC) of Li‐ion battery can ensure the reliability of the storage system. A combined estimator of online full‐parameter identification and adaptive unscented particle filter for Li‐ion battery SOC based on an improved fractional‐order model is proposed, which overcomes the shortcomings of the traditional SOC cumulative error and the difficulty of OCV acquisition. The proposed adaptive fractional unscented particle filter algorithm introduces fractional parameters as hidden parameters and reduces the complexity of the algorithm iteration by reducing the number of particles. At the same time, the noise adaptive algorithm based on the residual sequence can solve the divergence problem of the filter and improve the adaptability of the algorithm. To verify the feasibility of the algorithm under complex operating conditions, the urban dynamometer driving schedule dynamic working conditions of Li‐ion batteries are verified. The experimental results show that the evaluation index of the algorithm is the best, the RMSE is 0.67%, and the SOC estimation is more accurate. It shows that the algorithm has strong robustness and fast convergence. [ABSTRACT FROM AUTHOR]

Summary: The research of the real‐time state of charge (SOC) estimation method for lithium‐ion battery is developing towards the trend of model diversification and algorithm complexity. However, due to the limitation of computing ability in the actual battery management system, the traditional ampere‐hour (Ah) method is still widely used. First, temperature, charge‐discharge current, and battery aging are considered as the main factors, which affect the estimation accuracy of the Ah method under the condition that detection accuracy of the current sensor is determined. Second, the relationship between the SOC and battery open‐circuit voltage at different temperatures is analyzed, which is used to modify the initial SOC. Third, the influence mechanism of main factors on the effect of the Ah method is analyzed, and proposes a capacity composite correction factor to reflect the influence of charge‐discharge efficiency, coulomb efficiency, and battery aging comprehensively, and then update its value in real‐time. Lastly, the adaptive improved Ah formula and the complete SOC estimation model is designed, and the estimation effect of this model is verified by comparing with other SOC estimation methods in the experiment of dynamic cycle test. The results show that the estimation error of the adaptive improved method is less than 2% under two comprehensive working conditions, while the error of the traditional method is 5% to 10%, and compared with an extended kalman filter algorithm, it also gets a better SOC estimation performance, which proves that this method is scientific and effective. Highlights: The factors affecting the accuracy of traditional ampere‐hour method are analyzed in detail.The necessary shelf time of battery at extreme temperature was explored.Based on the semi‐empirical model, the capacity attenuation function k is proposed.Compound correction factor F is proposed to synthetically characterize the influence of internal and external factors of battery.A novel ampere‐hour (Ah) integral formula is proposed.A novel practical state of charge (SOC) estimation method is proposed. [ABSTRACT FROM AUTHOR]

Most existing deep-learning-based fall detection methods use either 2D neural network without considering movement representation sequences, or whole sequences instead of only those in the fall period. These characteristics result in inaccurate extraction of human action features and failure to detect falls due to background interferences or activity representation beyond the fall period. To alleviate these problems, a skeleton-based 3D consecutive-low-pooling neural network (S3D-CNN) for fall detection is proposed in this paper. In the S3D-CNN, an activity feature clustering selector is designed to extract the skeleton representation in depth videos using pose estimation algorithm and form optimized skeleton sequence of fall period. A 3D consecutive-low-pooling (3D-CLP) neural network is proposed to process these representation sequences by improving network in terms of layer number, pooling kernel size, and single input frame number. The proposed method is evaluated on public and self-collected datasets respectively, outperforming the existing methods. [ABSTRACT FROM AUTHOR]

Currently, the popularity of various social media platforms has made convenient communications and prompt information spreading in Online Social Networks (OSNs). However, it brings a serious threat to social stability and public safety when information is identified as false, misleading or inappropriate. Therefore, it is valuable that the timely and real-time rumor refutations are respectively studied to promptly and continually block the rumor spreading. Existing works on the rumor refutations are lacking in sufficient decomposition on the trust mechanism and ignore the analysis of continual blocking the rumor spreading. Motivated by these issues, this paper proposes a comprehensive Hybrid Clustered Shuffled Frog-Leaping Algorithm-Particle Swarm Optimization (HCSFLA-PSO) algorithm for promptly and continually blocking the rumor spreading in OSNs. Firstly, by making decomposition of social relations and analyzing degree of intimacy, self-reliability and credibility, a new trust mechanism of rumor refutations and a novel expression of trust degree are proposed. Secondly, a comprehensive HCSFLA-PSO algorithm is proposed by taking advantage of the clustered local search capability of the SFLA and the fast-convergence of the PSO algorithm collaborative for the rumor refutations, which includes CNP-HCSFLA-PSO sub-algorithm for the timely rumor refutation and CP-HCSFLA-PSO sub-algorithm for the real-time rumor refutation during the truth evolution according to different social relations with different trust degrees. Thirdly, a new concept of timeliness of information and a novel energy consumption model are proposed to solve the influence problem of continual updated truths. Finally, numerical simulations and ablation experiments are conducted to verify the effectiveness of our proposed algorithm. • A novel expression of the trust degree on the rumor refutation is proposed. • A HCSFLA-PSO algorithm is proposed for the timely and real-time rumor refutations. • Two sub-algorithms are proposed during the truth evolution within each cluster. • An accumulation model of the rumor influence is designed for the continual update. • The conduits of the truth propagation are classified by different trust degrees. [ABSTRACT FROM AUTHOR]

Recently, a new approach for the stabilization of the incompressible Navier-Stokes equations for high Reynolds numbers was introduced based on the nonlinear differential filtering of solutions on every time step of a discrete scheme. In this article, the stabilization is shown to be equivalent to a certain eddy-viscosity model in Large Eddy Simulation. This allows a refined analysis and further understanding of desired filter properties. We also consider the application of the filtering in a projection (pressure correction) method, the standard splitting algorithm for time integration of the incompressible fluid equations. The article proves an estimate on the convergence of the filtered numerical solution to the corresponding Navier-Stokes solution. © 2013 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2013 [ABSTRACT FROM AUTHOR]

A new image edge detection algorithm which is based on the theory of fuzzy gradient is proposed in the paper. The steps of the algorithm is as follows: firstly, the original gray image is changed into fuzzy matrix by the normalized tangent function; secondly, gradient matrix of the fuzzy matrix mentioned above is computed; thirdly, the fuzzy gradient matrix which is also called fuzzy set of the gradient matrix is obtained using sine function; and in the end, appropriate threshold which is identified by genetic algorithm based on normalized real-coded is used to segment the fuzzy gradient set into two subsets: the edge point set and non-edge point set. At this point, the image edge points are obtained. To test the speed and accuracy of the algorithm for detecting edge of micro-part image, the experiments have been done taking different kinds of micro-gear as the subjects. The experimental results show that the detecting accuracy is better than 10µs and the processing time is generally about 1 s. Compared with Canny or Pal-King algorithms, it could be concluded that the performance of proposed new algorithm is the best. [ABSTRACT FROM AUTHOR]

This paper presents a particle swarm optimization (PSO) based approach to achieve optimal capacitor placement in radial distribution systems. Harmonic distortion effects, discrete nature of capacitors, and different load levels are all taken into consideration in the problem formulation. Mathematically, the capacitor placement problem is a non-linear and non-differentiable mixed integer optimization problem with a set of equality and inequality operating constraints. Most conventional optimization techniques are incapable to solve this hard combinatorial problem, whereas PSO algorithm is very suitable. The proposed solution method employs PSO to search for optimal locations, types, and sizes of capacitors to be placed and optimal numbers of switched capacitor banks at different load levels. Computation procedures of applying the method to the capacitor placement problem are given in detail. The proposed approach has been implemented and tested on a distorted IEEE 9-bus test system with promising results. [Copyright &y& Elsevier]