BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.

Data-Driven Self-Triggered Control for Networked Motor Control Systems Using RNNs and Pre-Training: A Hierarchical Reinforcement Learning Framework.

This paper introduces a novel data-driven self-triggered control approach based on a hierarchical reinforcement learning framework in networked motor control systems. This approach divides the self-triggered control policy into higher and lower layers, with the higher-level policy guiding the lower-level policy in decision-making, thereby reducing the exploration space of the lower-level policy and improving the efficiency of the learning process. The data-driven framework integrates with the dual-actor critic algorithm, using two interconnected neural networks to approximate the hierarchical policies. In this framework, we use recurrent neural networks as the network architecture for the critic, utilizing the temporal dynamics of recurrent neural networks to better capture the dependencies between costs, thus enhancing the critic network's efficiency and accuracy in approximating the multi-time cumulative cost function. Additionally, we have developed a pre-training method for the control policy networks to further improve learning efficiency. The effectiveness of our proposed method is validated through a series of numerical simulations.

Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs.

While system identification methods have developed rapidly, modeling the process of batch polymerization reactors still poses challenges. Therefore, designing an intelligent modeling approach for these reactors is important. This paper focuses on identifying actual models for batch polymerization reactors, proposing a novel recursive approach based on the expectation-maximization algorithm. The proposed method pays special attention to unknown inputs (UIs), which may represent modeling errors or process faults. To estimate the UIs of the model, the recursive expectation-maximization (EM) technique is used. The proposed algorithm consists of two steps: the E-step and the M-step. In the E-step, a Q-function is recursively computed based on the maximum likelihood framework, using the UI estimates from the previous time step. The Kalman filter is utilized to calculate the estimates of the states using the measurements from sensor data. In the M-step, analytical solutions for the UIs are found through local optimization of the recursive Q-function. To demonstrate the effectiveness of the proposed algorithm, a practical application of modeling batch polymerization reactors is presented. The performance of the proposed recursive EM algorithm is compared to that of the augmented state Kalman filter (ASKF) using root mean squared errors (RMSEs). The RMSEs obtained from the proposed method are at least 6.52% lower than those from the ASKF method, indicating superior performance.

Psychological workplace violence and its influence on professional commitment among nursing interns in China: A multicenter cross-sectional study.

Background: Psychological workplace violence (WPV) is the primary form of workplace violence suffered by nursing interns. Psychological WPV not only damages the physical and mental health of nursing interns, but also has a negative impact on their work quality and career choice. Aim: To investigate the characteristics and types of psychological WPV suffered by nursing interns in China, analyze the influencing factors of psychological WPV among nursing interns, and explore the influence of psychological WPV on the professional commitment of nursing interns. Methods: The subjects were 1,095 nursing interns from 14 medical colleges in Shandong Province. The data were collected electronically using the psychological WPV against nursing interns questionnaire and the professional commitment scale of nursing. The frequency and component ratio were used to describe the incidence and characteristics of psychological WPV. Binary logistic regression was used to analyze the influencing factors of psychological WPV, and linear regression investigated the influence of psychological WPV on the professional commitment of nursing interns. Results: In the study, 45.0% (n = 493) of nursing interns suffered at least one incidence of psychological WPV during clinical practice, mainly discrimination and verbal abuse. Patients and their relatives were the main perpetrators of psychological WPV. Discrimination and lack of trust were the two main reasons behind psychological WPV. Furthermore, 75.9% of psychological WPV incidents were not effectively reported. Logistic regression showed that clinical internship duration, place of family residence, and hospital level were the influencing factors of psychological WPV among nursing interns. Linear regression results showed that psychological WPV had a negative effect on nursing interns' professional commitment. Conclusion: Psychological WPV against nursing interns is highly prevalent in China, negatively impacting their professional commitment. It is suggested that colleges should introduce courses for nursing interns to understand and cope with psychological WPV before entering clinical practice, and hospitals should establish a mechanism to prevent, cope with, report, and deal with psychological WPV to effectively reduce the incidence of psychological WPV against nursing interns, improve their ability to cope with psychological WPV, and enhance their professional commitment.

Distributed Filtering for Semi-Markov-Type Sensor Networks With Hybrid Sojourn-Time Distributions-A Nonmonotonic Approach.

This article examines the distributed filtering problem for a general class of filtering systems consisting of distributed time-delayed plant and filtering networks with semi-Markov-type topology switching (SMTTS). The SMTTS implies the topology sojourn time can be a hybrid function of different types of probabilistic distributions, typically, binomial distribution used to model unreliable communication links between the filtering nodes and Weibull distribution employed to depict the cumulative abrasion failure. First, by properly constructing a sojourn-time-dependent Lyapunov-Krasovski function (STDLKF), both time-varying topology-dependent filter and topology-dependent filter are designed. Second, a novel nonmonotonic approach with less design conservatism is developed by relaxing the monotonic requirement of STDLKF within each topology sojourn time. Moreover, an algorithm with less computational effort is proposed to generate a semi-Markov chain from a given Markov renewal chain. Simulation examples, including a microgrid islanded system, are presented to testify the generality and elucidate the practical potential of the nonmonotonic approach.

Bayesian Inference for State-Space Models With Student-t Mixture Distributions.

This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- t distribution, while the likelihood function is modified to the Student- t mixture distribution. By learning the corresponding parameters online, informative components of the Student- t mixture distribution are adapted to approximate the statistics of potential uncertainties. Then, the obstacle caused by the coupling of the updated parameters is eliminated by the variational Bayesian (VB) technique and fixed-point iterations. Discussions are provided to show the reasons for the achieved advantages analytically. Using the Newtonian tracking example and a three degree-of-freedom (DOF) hover system, we show that the proposed inference approach exhibits better performance compared with the existing method in the presence of modeling uncertainties and measurement outliers.

Solving the Zero-Sum Control Problem for Tidal Turbine System: An Online Reinforcement Learning Approach.

A novel completely mode-free integral reinforcement learning (CMFIRL)-based iteration algorithm is proposed in this article to compute the two-player zero-sum games and the Nash equilibrium problems, that is, the optimal control policy pairs, for tidal turbine system based on continuous-time Markov jump linear model with exact transition probability and completely unknown dynamics. First, the tidal turbine system is modeled into Markov jump linear systems, followed by a designed subsystem transformation technique to decouple the jumping modes. Then, a completely mode-free reinforcement learning algorithm is employed to address the game-coupled algebraic Riccati equations without using the information of the system dynamics, in order to reach the Nash equilibrium. The learning algorithm includes one iteration loop by updating the control policy and the disturbance policy simultaneously. Also, the exploration signal is added for motivating the system, and the convergence of the CMFIRL iteration algorithm is rigorously proved. Finally, a simulation example is given to illustrate the effectiveness and applicability of the control design approach.

Sorting Center Value Identification of "Internet + Recycling" Based on Transfer Clustering.

As the core link of the "Internet + Recycling" process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain. Furthermore, an improved RFM (Recency, Frequency, and Monetary) model, named GFMR (Gap, Frequency, Monetary, and Repeat), has been presented to attain a more reasonable attribute description for sorting centers and consumers. The application in the field of electronic waste recycling shows the effectiveness and advantages of the proposed method.

Isolation method of Saccharomyces cerevisiae from red blood cells based on the optically induced dielectrophoresis technique for the rapid detection of fungal infections.

Saccharomyces cerevisiae (S. cerevisiae) has been classically used to treat diarrhea and diarrhea-related diseases. However, in the past two decades, fungal infections caused by S. cerevisiae have been increasing among immunocompromised patients, and it takes too long to isolate S. cerevisiae from blood to diagnose it in time. In this paper, a new method for the isolation and selection of S. cerevisiae from red blood cells (RBC) is proposed by designing a microfluidic chip with an optically-induced dielectrophoresis (ODEP) system. It was verified by theory and experiments that the magnitude and direction of the dielectrophoresis force applied on RBCs and S. cerevisiae are different, which determine that the S. cerevisiae can be isolated from RBCs by the ODEP system. By designing the specific light images and the dynamic separation mode, the optimal operating conditions were experimentally achieved for acquiring higher purity of S. cerevisiae. The purity ranges were up to 95.9%-97.3%. This work demonstrates a promising tool for efficient and effective purification of S. cerevisiae from RBCs and provides a novel method of S. cerevisiae isolation for the timely diagnosis of fungal infections.

A resource-aware sliding mode control approach for Markov jump systems.

The problem of sliding mode control (SMC) for a class of Markov jump systems (MJSs) is addressed in this paper based on a resource-aware triggering mechanism which realizes computational resources saving and disturbance attenuation simultaneously. By introducing the self-triggered policy, the next execution time is pre-computed for sampling, updating and executing by relying on the latest sampled information. Then, the switching surface and the related dynamics of the original MJSs are obtained by means of a self-triggered sampling scheme. To guarantee both the system stability and the desired disturbance attenuation performance, sufficient conditions are presented in terms of linear matrix inequalities. Moreover, to ensure the time finiteness of the predefined switching surface reachability and satisfy the desirable sliding motion performance, an SMC law is proposed. The validity and superiority of the developed scheme are demonstrated via a simulation example.

Fuzzy Fault Detection for Markov Jump Systems With Partly Accessible Hidden Information: An Event-Triggered Approach.

This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.

Confidence set-membership state estimation for LPV systems with inexact scheduling variables.

In this paper, a confidence set-membership state estimator is proposed for a class of polytopic linear parameter varying (LPV) systems with inexact scheduling variables. The set-bounded and Gaussian uncertainties are considered simultaneously in the process disturbances and measurement noises. The purpose of the proposed estimator is to achieve a confidence set of the state with given confidence level. Based on the polytopic LPV uncertain enclosure model, the set-bounded/Gaussian uncertainties of the state are given by using the worst case strategy. The size of the confidence set is minimized to get the optimal gain for the estimator. Meanwhile, the constrained zonotope is adopted to represent set-bounded uncertainties for more accurate results. Finally, a vehicle example is given to illustrate the effectiveness of proposed methods.

Asynchronous Fault Detection Observer for 2-D Markov Jump Systems.

In this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed. Then, combining the H∞ attenuation index and H_ increscent index, a multiobjective solution to the FD problem is formed. In terms of linear matrix inequality technology, sufficient conditions are gained to guarantee the existence of the asynchronous FD. Simultaneously, an asynchronous FD algorithm is generated to acquire the optimal performance indices. Finally, a numerical example concerned with the Darboux equation is demonstrated to exhibit the soundness of the developed approach.

Clinical efficacy analysis of different therapeutic methods in patients with cesarean scar pregnancy.

OBJECTIVE: The purpose of this study was to analyze the clinical efficacy of five therapeutic strategies in patients with CSP. MATERIALS AND METHODS: A total of 135 CSP patients were included and divided into five groups based on the treatment they received, including transvaginal resection (Group A), laparoscopic resection (Group B), uterine arterial embolization (UAE) combined with hysteroscopic curettage (Group C), UAE combined with uterine curettage (Group D), and hysteroscopic curettage (Group E). To investigate the clinical efficacy of these strategies, intraoperative bleeding, serum ß-hCG levels and recovery time, menstruation recovery time, hormone levels at 1 month after treatment. RESULTS: Patients in group A had the lowest postoperative serum ß-hCG levels, and the shortest recovery times of both serum ß-hCG and menstruation, followed by patients in group B. Group C and D had small amount of blood loss. The hospital stays and costs were low in group E. In addition, the sex hormone levels showed no significant difference among the five groups. CONCLUSION: Our results indicated that resection surgery and UAE have good curative effects, but high hospital costs in CSP treatment. The selection of an optimal treatment regimen for CSP should be carried out based on specific conditions of the patients.

Finite-Time L2-Gain Asynchronous Control for Continuous-Time Positive Hidden Markov Jump Systems via T-S Fuzzy Model Approach.

This article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi-Sugeno fuzzy model method. Different from the existing methods, the Markov jump systems under consideration are considered with the hidden Markov model in the continuous-time case, that is, the Markov model consists of the hidden state and the observed state. We aim to derive a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite-time bounded and positive, and fulfill the given L2 performance index. Applying the stochastic Lyapunov-Krasovskii functional (SLKF) methods, we establish sufficient conditions to obtain the finite-time state-feedback controller. Finally, a Lotka-Volterra population model is used to show the feasibility and validity of the main results.

Multilevel LASSO-based NIR temperature-correction modeling for viscosity measurement of bisphenol-A.

Temperature variation will affect the prediction accuracy when using the near-infrared (NIR) analytical model to measure the viscosity of bisphenol-A. In order to correct the effect of temperature on the prediction performance of NIR model, a multilevel least-absolute shrinkage and selection operator (LASSO) is proposed in this paper. The multilevel LASSO algorithm combines LASSO with the multilevel simultaneous component analysis (MLSCA) to enhance the robustness of the model under temperature changes and external disturbances. MLSCA is applied to decompose the molecular spectral data into two parts. One part denotes the property caused by temperature, the other means the changes of concentration. LASSO, a sparse regression model, is used to select the variables and perform the regularization to further enhance the robustness and interpretability of the model. Experimental results demonstrate the effectiveness of the proposed model in measuring bisphenol-A viscosity, which provides a more stable prediction result compared with the existing ones without temperature corrections.

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression.

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the prediction of the components in industrial processes. The ability to record spectra for solid and liquid samples without any pretreatment is advantageous and the method is widely used. However, the disadvantages of analyzing high-dimensional near-infrared spectral data include information redundancy and multicollinearity of the spectral data. Thus, we propose to use partial least squares regression method, which has traditionally been used to reduce the data dimensionality and eliminate the collinearity between the original features. We implement the method for predicting the o-cresol concentration during the production of polyphenylene ether. The proposed approach offers the following advantages over component regression prediction methods: 1) partial least squares regression solves the multicollinearity problem of the independent variables and effectively avoids overfitting, which occurs in a regression analysis due to the high correlation between the independent variables; 2) the use of the near-infrared spectra results in high accuracy because it is a non-destructive and non-polluting method to obtain information at microscopic and molecular scales.

Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function.

This paper proposes a near-infrared (NIR) fault detection technology based on a process pattern via a potential function. Near-infrared spectroscopy is used to acquire process information at the molecular level. In this study, the process pattern concept is first introduced in the field of process control and a process pattern construction method based on elastic net-PCA is put forth. Next, the potential function discriminant method is applied to distinguish and classify the constructed process pattern and identify the running state of the industrial system. Finally, the proposed method is verified and analyzed using spectra data of the crude oil desalination and dehydration process. Compared with existing fault detection methods, the proposed approach offers the following advantages: (1) potential function discrimination achieves nonlinear process classification with better fault detection accuracy and good visualization performance; (2) fault detection based on NIR spectra is faster with and possesses greater accuracy because it acquires process information from a microscopic molecular perspective; and (3) the process pattern contains more effective process information and can more comprehensively characterize the essential features of processes.

Probabilistic PCR based near-infrared modeling with temperature compensation.

Considering that temperature makes a difference to near-infrared spectrum, a probabilistic principle component regression (PPCR) based temperature compensation modeling strategy is investigated under the framework of maximum likelihood estimation. First, a PPCR model is established to extract the dynamic information of the spectra at designated experimental temperature. Then, by decomposing the temperature-induced spectral variation into the shift in horizontal direction and the drift in vertical direction, the quantitative expression between spectral variation and temperature change is derived. Based on the decomposition, the estimation of new latent variables that vary with temperature is derived according to the spectral data set collected at certain temperatures. Finally, for performance evaluation, applications of the theoretical results to bisphenol-A and gasoline-ethanol mixture illustrate the effectiveness and advantages of the developed techniques.

Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process.

The fault detection problem of the oil desalting process is investigated in this paper. Different from the traditional fault detection approaches based on measurable process variables, near-infrared (NIR) spectroscopy is applied to acquire the process fault information from the molecular vibrational signal. With the molecular spectra data, principal component analysis was explored to calculate the Hotelling T2 and squared prediction error, which act as fault indicators. Compared with the traditional fault detection approach based on measurable process variables, NIR spectra-based fault detection illustrates more sensitivity to early failure because of the fact that the changes in the molecular level can be identified earlier than the physical appearances on the process. The application results show that the detection time of the proposed method is earlier than the traditional method by about 200 min.